Plant phenotyping methods.

Wheat biomass estimation across crop development using UAV LiDAR structure–intensity fusion alongside multispectral and thermal data →

This study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework. Among the evaluated single sensor approaches, LiDAR features consistently provided the strongest performance, demonstrating the complementary value of crop height, vertically distributed canopy density, and normalized LiDAR intensity for characterizing canopy structure and within-canopy variability. Multi-layer GF improved AGB estimation relative to conventional ground-based GF approaches, highlighting the importance of incorporating the vertical distribution of canopy density. Multi-sensor fusion produced only modest additional improvements, indicating limited benefits relative to the increased acquisition and processing requirements. Temporal analysis showed that structural LiDAR features were most informative during early crop development, whereas normalized intensity, spectral reflectance, and thermal observations became increasingly valuable during canopy maturation and senescence. Comparisons with destructively measured plant area index (PAI), leaf area index (LAI), green leaf area index (GLAI), and green fraction of LAI further demonstrated that normalized LiDAR intensity (903 nm) was more closely associated with green canopy components than purely structural LiDAR metrics. Overall, the results demonstrate that fully exploiting both the structural and spectral information contained within LiDAR observations can substantially improve UAV-based biomass estimation, while multispectral and thermal observations provide complementary information whose contribution varies with crop development and monitoring objectives.

Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトル・熱画像とANNを用いた小麦バイオマス推定手法を系統的に比較・評価しており、植物形質推定の取得・解析方法が研究の中心である。

abstractThis study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework.

A Cost-Effective Approach to Estimate Quinoa Aboveground Biomass Volume Combining UAV RGB Data with Sentinel-1 and Sentinel-2 Satellite Imagery →

This study assessed the integration of Unmanned Aerial Vehicles (UAVs) and satellite images (Sentinel-1 and Sentinel-2) in advanced machine-learning techniques to monitor the ABV of quinoa crops (Jacha Grano variety) across the Bolivian Altiplano. The proposed method follows a two-step procedure. First, UAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs at a 10 m spatial resolution from the crop canopy 3D model and classification, respectively. Secondly, several spectral and polarization/texture indices derived from Sentinel-2 and -1 images were integrated into three decision-tree-based machine-learning models (Random Forest-RF, Gradient Boosting-GB, eXtreme Gradient Boosting-XGB), and one CNN-based machine-learning model to estimate ABV. Additionally, a Stacking Model (STM) build on top of the three decision-tree-based models was considered for comparison. Model evaluation was also performed in a two-step approach. First, a 10-fold cross-validation strategy was used to highlight ABV sensitivity to Sentinel-2 and Sentinel-1 alone and in combination. Secondly, a Leave-One-Plot-Out Cross-Validation (LOPOCV) strategy was used to avoid autocorrelation between the training and evaluation dataset and therefore provided more insight into ABV mapping potential. The results showed that the combination of Sentinel-1 and Sentinel-2 features in the CNN model achieved the best predictive performance with R2 and RMSE values of 0.64 and 0.39 m3 ∙ 100 m−2, respectively. These findings highlight the potential of integrating multi-source information in advanced artificial intelligence algorithms for quinoa ABV monitoring, offering new insights toward the identification of sustainable practices across remote regions with complex socio-economic contexts.

Why it matches plant phenotyping methodsUAV画像・衛星センサー・機械学習を統合し、キノアの地上部バイオマス体積という植物形質を推定する手法を開発・比較検証しており、フェノタイピング手法が中心である。

abstractUAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs

Deep Learning-Based Monitoring of Tea Plant Growth and Nitrogen Status Using UAV Multisource Remote Sensing Features →

Accurate and efficient monitoring of tea plant growth parameters via remote sensing is essential for precision plantation management. However, spectral indices relying solely on reflectance often exhibit limited sensitivity in capturing complex tea canopy characteristics. This study developed a data-driven framework integrating spectral reflectance, frequency-domain harmonic components, and spatial texture features to construct tri-feature fusion indices (TFIs) and establish machine learning and deep learning models for tea growth monitoring. Ten-band multispectral imagery was acquired using a UAV alongside synchronous field measurements of leaf and plant biomass and nitrogen accumulation. TFIs were constructed through exhaustive feature combinations and optimized via a data-driven search strategy. Subsequently, random forest (RF), multilayer perceptron (MLP), convolutional neural network (CNN), and transformer models were evaluated using a leave-one-site-out cross-validation (LOSO-CV) strategy. The selected TFIs showed strong associations with tea growth parameters within the investigated dataset, with R2 values up to 0.63 and 0.62 for leaf dry matter and leaf nitrogen accumulation, respectively. Models incorporating selected TFIs achieved cross-validated R2 values of 0.56 for leaf dry matter (MLP), 0.59 for plant dry matter (MLP), 0.73 for leaf nitrogen accumulation (MLP), and 0.68 for plant nitrogen accumulation (CNN). These models exhibited competitive predictive performance comparable to RF, although no statistically significant differences in mean absolute error were observed under site-held-out evaluation. Furthermore, model-derived spatial maps provided insights into fine-scale spatial heterogeneity and potential interannual variations in tea growth parameters across representative plantations from 2024 to 2025. Overall, this study provides a UAV-based framework for tea growth parameter estimation by integrating multi-domain information without requiring additional environmental observations.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から茶植物の乾物量・窒素蓄積を推定する特徴量融合および機械学習・深層学習フレームワークを開発し、サイト外交差検証で評価しており、植物形質の取得・推定手法が中心である。

abstractThis study developed a data-driven framework integrating spectral reflectance, frequency-domain harmonic components, and spatial texture features to construct tri-feature fusion indices (TFIs) and establish machine learning and deep learning models for tea growth monitoring.

Improving cotton biomass estimation by assimilating SAR data into a modified crop growth model with simple calibration →

Aboveground biomass density (AGBD) is a key indicator in agricultural systems, directly reflecting crop carbon storage potential and yield levels. The World Food Studies (WOFOST) model is widely used for crop growth simulation due to its process-based interpretability. However, its application is limited by complex calibration demands and struggles with spatial heterogeneity. To address these limitations, this paper proposes an assimilation system that integrates Synthetic Aperture Radar (SAR) data into a modified WOFOST model, which requires simple calibration. WOFOST is run in potential production mode, which assumes ideal conditions to reduce input data requirements. Before assimilation, phenology-related temperature sums are aligned with phenology and meteorological data to match local growth stages. Two quantities are then estimated and updated in the model through data assimilation by minimizing the difference between SAR-derived and simulated AGBD. These quantities are the proposed yield reduction factor, which represents the proportional decrease in potential CO 2 assimilation under stresses, and the initial total dry weight at sowing. Validation experiments were conducted using multi-year cotton datasets from two farms in Georgia, USA, differing in whether irrigation was applied. Compared to WOFOST simulations and evaluated against in situ AGBD measurements, the assimilation results improve agreement and reduce error (approximately 43% RMSE reduction at the rainfed site and 15% at the irrigated site). It also delivers spatial maps of biomass, together with model-derived yield and harvest-index diagnostics, and remains operational under frequent cloud cover where optical observations are sparse. This SAR-based assimilation strategy reduces calibration demands, providing a novel and practical pathway to extend WOFOST applications to diverse agricultural scenarios.

Why it matches plant phenotyping methodsSARデータを作物成長モデルに同化して綿の地上部バイオマスを推定する手法を開発し、複数年・複数圃場データで検証しているため、植物形質取得が中心である。

abstractthis paper proposes an assimilation system that integrates Synthetic Aperture Radar (SAR) data into a modified WOFOST model

Destructive harvest validation of high-throughput measurements show that water use efficiency is unaffected by moderate drought in tobacco →

ABSTRACT Water-use efficiency (WUE), the ratio of accumulated plant biomass to water lost through transpiration has conventionally been determined using a destructive single-point measurement. Recent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies. Therefore, we compared digital biomass determined point clouds produced from multispectral camera scanners with destructive harvests across eight harvests using Samsun tobacco grown under both drought and high-water conditions. WUE efficiency, calculated using the digital biomass estimated from a point cloud and gravimetric water use determinations, were compared to destructive harvest determinations. The coefficient of variation (CV) showed there were no significant differences in digital and destructive measurements for either biomass or WUE. Indicating that digital measurements can be used in place of destructive measurements. Drought plants used significantly less water and were significantly smaller than high-water plants from Harvests 4 through 8. However, there were no significant differences in the ratio of evapotranspiration to leaf area or WUE, indicating that drought plants were simply smaller and used less water than the high-water plants. This work validates that estimating plant biomass from a digital point coupled with continuous gravimetric determination of water use provides a reliable nondestructive measure of WUE in high-throughput measurements across the full plant life cycle. PLAIN LANGUAGE SUMMARY We grew tobacco plants under either a drought or high-water treatment and harvested a portion of the plants every few days for a total of eight harvests. Throughout the experiment, we collected 3D images of the plants and continuously measured pot weight to track plant growth and water use across different developmental stages. Destructive biomass served as the gold-standard measurement. We then compared biomass and water-use estimates generated from the digital measurements with the destructive measurements. The digital approach provided accurate estimates of plant biomass and water use while requiring little hands-on labor and no plant destruction. These nondestructive methods could help plant breeders identify water-efficient plants earlier in the breeding process, accelerating the development of crops that use water more efficiently.

Why it matches plant phenotyping methods3D画像による非破壊バイオマス推定と連続的な重量測定からWUEを推定する手法を、破壊収穫と比較して検証しており、植物表現型取得法が中心である。

abstractRecent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies.

Combining 3D-multispectral and hyperspectral imaging to identify environmental stress treatments imposed during plant growth →

Abstract Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments – including different growth irradiances, heat treatment and drought stress – with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.

Why it matches plant phenotyping methods3Dマルチスペクトル画像・ハイパースペクトル画像と機械学習を統合し、植物形態・スペクトル形質を非破壊かつ高スループットに取得・検証する方法が研究の中心である。

abstractNon-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function

Explainable TabPFN-Based Machine Learning for Single-Plant Yield Estimation and Trait Prioritization in Faba Bean (Vicia faba L.) →

Faba bean yield reflects complex relationships among genotype, environment, and agronomic traits. This study evaluated an explainable Tabular Prior-data Fitted Network (TabPFN) framework for estimating plot-mean single-plant yield and prioritizing traits using 398 plot-level observations, 13 measured agronomic predictors, and six derived features. On the reference 80/20 split, TabPFN achieved the best values for all four test metrics (R2 = 0.8746, RMSE = 1.9132 g plant−1, MAE = 1.0819 g plant−1, and MAPE = 8.16%). The Friedman test detected differences among the six models (χ2(5) = 16.75, p = 0.005); Nemenyi comparisons distinguished TabPFN from HistGradientBoosting and SVR, whereas the Holm-corrected Wilcoxon analysis confirmed only the TabPFN–SVR difference. Across 10 repeated 80/20 splits, TabPFN obtained the highest mean test R2 (0.8614 ± 0.0691), ranked first in eight splits, and produced a higher R2 than every tuned baseline in at least eight splits. SHAP, permutation importance, and LOCO analyses emphasized pod-, seed-, and biomass-related predictors. Repeated-split ablation showed that derived features improved TabPFN consistently, whereas removing selected target-proximal yield variables reduced performance for every model. The framework is therefore a harvest-time trait-estimation and trait-prioritization tool rather than an early-season forecasting system. Notably, TabPFN achieved this performance without the 100-trial Optuna search used for each baseline; only n_estimators was screened over four prespecified values.

Why it matches plant phenotyping methods単一個体収量を推定し、形質優先順位付けを行う機械学習フレームワークを評価・比較しており、植物形質抽出手法が研究の中心である。

abstractThis study evaluated an explainable Tabular Prior-data Fitted Network (TabPFN) framework for estimating plot-mean single-plant yield and prioritizing traits

Rice aboveground biomass estimation based on three-dimensional dry matter distribution integration model →

Accurate quantification of rice aboveground biomass (AGB) is critical for crop monitoring but remains challenging due to the complex nonlinearity arising from the coupling of plant density, spatial structure, and internal dry matter distribution. To address the limitations of single-source remote sensing, this study proposed a Three-Dimensional Dry Matter Distribution Integration (3D-DMI) model, which establishes a physically interpretable framework decomposing AGB into dry matter density ( ρ ), horizontal projection distribution ( S ), and vertical cumulative distribution ( h d ) components. Guided by this framework, a core subset of six features (Red_650, MTCI, G_correlation, R_correlation, LPI, and HPA0_99) was extracted from UAV-based multispectral, RGB, and LiDAR data using a dual-step feature selection approach combining Maximum Information Coefficient (MIC) and Distance Correlation (dCor). A Random Forest (RF) regression model was then developed to estimate AGB across the entire growth season. The results demonstrated that the 3D-DMI model achieved excellent performance with an R 2 of 0.920, an RMSE of 0.184 kg/m², and an RPD of 3.544, significantly outperforming any single-sensor approach. Single-feature analysis revealed that while LiDAR-derived structural features provided the fundamental basis for biomass estimation, they encountered inherent saturation bottlenecks during late growth stages. Feature contribution analysis based on SHAP further quantified that LiDAR-derived features dominated the estimation process (68.5% contribution), providing the volumetric basis, whereas RGB textures (18.3%) and multispectral features (13.3%) provided indispensable supplements. Ultimately, this study established a robust, physically grounded computational paradigm for high-precision UAV-based rice biomass monitoring across the entire growth cycle.

Why it matches plant phenotyping methodsUAVマルチスペクトル・RGB・LiDARデータからイネの地上部バイオマスを推定する3D-DMI計算手法を開発・評価しており、植物形質の取得・抽出が研究の中心である。

abstractthis study proposed a Three-Dimensional Dry Matter Distribution Integration (3D-DMI) model

UAV multisensor data and GAMLSS improve forage biomass estimation in Cerrado integrated crop–livestock pastures →

Introduction Accurate estimation of aboveground biomass (AGB) is essential for monitoring pasture productivity and supporting sustainable management of integrated crop–livestock (ICL) systems. We hypothesized that integrating multispectral, thermal, and canopy-structural information derived from unmanned aerial vehicles (UAVs) would improve AGB prediction relative to spectral information alone, and that Generalized Additive Models for Location, Scale and Shape (GAMLSS) would accommodate seasonal heteroscedasticity while maintaining predictive performance comparable to Random Forest (RF) and Support Vector Machine (SVM) models. Methods We collected 280 destructive biomass samples from two ICL paddocks and one continuously grazed pasture in the Brazilian Cerrado between 2022 and 2024. Twenty-four UAV-derived predictors, including spectral bands, vegetation indices, canopy surface temperature, and canopy height, were evaluated using repeated five-fold cross-validation. Model transferability was assessed by withholding one management paddock at a time. Results and discussion Under repeated five-fold cross-validation, GAMLSS achieved the lowest prediction error (R² = 0.69 ± 0.01; RMSE = 2.15 ± 0.04 Mg ha⁻¹), followed closely by SVM (R² = 0.68 ± 0.01; RMSE = 2.19 ± 0.03 Mg ha -1 ); RF showed lower accuracy (R 2 = 0.53 ± 0.01; RMSE = 2.63 ± 0.02 Mg ha -1 ). In the paddock-transferability assessment, GAMLSS also showed the lowest error (R 2 = 0.63 ± 0.04; RMSE = 2.34 ± 0.26 Mg ha -1 ). For GAMLSS, the complete multisensor configuration reduced RMSE by 6.2% compared with the spectral-only configuration. The selected model was used to generate spatially explicit maps of AGB and standing aboveground biomass carbon, estimated from the mean measured carbon concentration of forage biomass. Integrating multispectral, thermal, and structural UAV data with distributional regression improves AGB estimation and enables spatial monitoring of tropical pastures under contrasting management conditions.

Why it matches plant phenotyping methodsUAVのマルチセンサーデータと統計モデルを用いて牧草の地上部バイオマスを推定し、交差検証と圃場間移 transferability 評価を行っており、植物形質の取得・推定手法が研究の中心である。

titleUAV multisensor data and GAMLSS improve forage biomass estimation in Cerrado integrated crop–livestock pastures

Evaluation of saline-alkali tolerance in 71 soybean germplasms based on multi-method integrated analysis at the germination stage. →

Background Global soybean production is constrained by scarce arable land, and standardized evaluation tools remain lacking for natural mixed saline-alkali stress, the predominant abiotic stress under field conditions. Objective This study aimed to establish a comprehensive saline-alkali tolerance evaluation system for soybean germplasms via integrated multivariate statistical methods, and screen core and auxiliary indicators for efficient germplasm identification. Methods Seventy-one soybean germplasms were tested under 90 mmol/L mixed saline-alkali stress (NaCl:Na 2 SO 4 :NaHCO 3 :Na 2 CO 3 = 1:9:9:1, pH 8.2) simulating natural saline-alkali soil. We quantified the saline-alkali tolerance coefficients (SATC) of 13 morphological and physiological indicators, followed by coefficient of variation (CV), principal component analysis (PCA), subordinate function, cluster analysis and regression modeling. Results Significant inter-germplasm variations in saline-alkali tolerance were detected, and indicators with CV > 0.35 ( e.g ., root length (RL), root fresh weight (RFW)) were screened as primary indices. PCA extracted five principal components with 87.18% cumulative variance contribution, and the integrated analytical pipeline categorized germplasms into five tolerance grades: eight highly tolerant, 24 moderately tolerant, 13 generally tolerant, 16 sensitive and 10 highly sensitive accessions. A high-precision prediction model was constructed ( D = 0.290 X 1 - 0.026 X 2 + 0.438 X 3 + 0.402 X 4 + 0.180 X 5 + 0.153 X 6 + 0.813 X 7 - 1.123; R 2 = 0.998, where X 1 - X 7 represent the SATC of germination rate (GR), RL, RFW, total fresh weight (TFW), shoot dry weight (SDW), root dry weight (RDW), and total dry weight (TDW), respectively). A Chi-squared Automatic Interaction Detection (CHAID) decision tree model was further developed and validated using 10-fold cross-validation, yielding a cross-validation risk value of 0.003, which was comparable to the resubstitution risk value (0.002), indicating good generalization ability and low risk of overfitting. A novel five-dimensional overlapping analysis identified RFW as the core evaluation indicator, with RDW, TFW and R/S as key auxiliary indicators. Conclusion This study delivers a standardized, reproducible technical framework for large-scale screening of saline-alkali-tolerant soybean germplasms. It facilitates global saline-alkali land utilization, accelerates worldwide soybean stress-tolerance breeding, and provides a transferable paradigm for stress tolerance evaluation in other major crops.

Why it matches plant phenotyping methodsダイズの耐塩・耐アルカリ性を評価するための形態・生理形質の統合評価体系、予測モデル、指標選定、交差検証を中心的に開発・検証しており、再利用可能な植物表現型評価手法に該当する。

abstractThis study aimed to establish a comprehensive saline-alkali tolerance evaluation system for soybean germplasms via integrated multivariate statistical methods, and screen core and auxiliary indicators for efficient germplasm identification.

Genomic prediction vs. gene-based crop models: a case study on rice trait prediction. →

Conventional breeding for ideotypes in target environments remains challenging due to genotype-by-environment interactions and the genetic complexity of key agronomic traits. Traditional multi-environment field trials are costly and time-consuming, limiting rapid genetic gain. These challenges highlight the need for digital tools to support rice breeding. However, two major approaches, genomic prediction (GP) and gene-based crop models (GBCMs), have distinct advantages. In this study, a dataset derived from a natural rice population comprising 210 genotypes, genotyped with about 650,000 markers, rice dry matter, and yield across three environments, was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework. The effectiveness of these models in predicting rice traits and assisting in breeding selection was subsequently evaluated. Prediction results indicated that biomass and yield could be effectively predicted by all models, with Normalized Root Mean Square Error (NRMSE) values ranging from 10.60% to 18.59% and 9.93% to 18.19%, respectively. In terms of predictive accuracy, parameter-based crop models achieved the highest predictive accuracy, although it was confined to theoretical simulations. This was followed by the GBCM and CNN, whereas the GBLUP exhibited the lowest performance. Furthermore, GGE biplot analysis revealed the predictions of the GBCM aligned more closely with field observations than those of the CNN, emphasizing the potential of GBCM as a practical surrogate for digital breeding. These results provide valuable insights into modeling genotype-by-environment interactions and support the development of data-informed breeding strategies for future rice improvement.

Why it matches plant phenotyping methodsイネの乾物量・収量という植物形質を予測する複数の計算モデルを開発・比較評価しており、形質推定手法が研究の中心である。

abstractIn this study, a dataset derived from a natural rice population comprising 210 genotypes, genotyped with about 650,000 markers, rice dry matter, and yield across three environments, was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework.

Multi-Platform LiDAR Comparative Assessment for Aboveground Biomass and Carbon Estimation in Mediterranean Woody Crops →

Reliable aboveground biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy-cover proxies) were extracted from normalized point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). TreeQSM closely matched the field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops.

Why it matches plant phenotyping methodsLiDAR複数プラットフォームと3D再構成を比較・検証し、樹木・区画レベルの地上部バイオマスという植物形質を推定する手法が研究の中心である。

abstractwe benchmarked four LiDAR modalities

Allometric relationships and growth dynamics of quinoa (Chenopodium quinoa Willd.) as influenced by sowing date: implications for non-destructive leaf area estimation →

Abstract Understanding the allometric relationships between leaf area and other plant traits is essential for non-destructive growth monitoring and efficient crop management. However, no comprehensive study has yet modeled leaf area in quinoa ( Chenopodium quinoa Willd.) using simple morphological traits across different sowing dates. This study aimed to quantify allometric relationships between leaf area and plant height, leaf dry weight, stem dry weight, panicle dry weight, and total dry matter, and to evaluate whether these relationships are modified by sowing date. A two-year field experiment was conducted with 12 sowing dates under a randomized complete block design with three replications. Leaf area index (LAI) dynamics were described using a logistic model, and allometric relationships were fitted using power-law equations. The results showed that LAI followed a logistic trend across all sowing dates, with maximum values ranging from 2.7 to 7.9. Plant height provided the most reliable prediction of leaf area (R² = 0.83, b = 1.2), followed by leaf dry weight (R² = 0.72, b = 0.97). The allometric coefficients for stem dry weight (b = 1.54, R² = 0.74) and panicle dry weight (b = 1.95, R² = 0.71) showed greater variability. A striking finding was the exceptionally high allometric coefficient (b = 4.95) recorded on May 6 of the second year, indicating a pronounced shift in resource allocation toward leaf area expansion. Total dry matter was a weak predictor (R² = 0.54), likely due to leaf fall during the growing season. The hypothesis that sowing date modifies allometric relationships was confirmed, as evidenced by considerable variation in allometric coefficients across sowing dates. This study provides, for the first time, a comprehensive set of allometric models for quinoa across multiple sowing dates. Plant height and leaf dry weight are recommended as simple, rapid, and non-destructive indicators for leaf area estimation, facilitating improved crop monitoring and management under diverse environmental conditions.

Why it matches plant phenotyping methods草丈や乾物重から葉面積を非破壊推定するアロメトリックモデルを中心に開発・評価しており、植物形質の取得手法が実質的な主題である。

abstractThis study aimed to quantify allometric relationships between leaf area and plant height, leaf dry weight, stem dry weight, panicle dry weight, and total dry matter, and to evaluate whether these relationships are modified by sowing date.

Spatial Domain Mismatch Between Field Plots and GEDI Inflates Aboveground Biomass Model Accuracy in a Sudanian Savanna Woodland →

Accurate estimation of aboveground biomass (AGB) in dryland savanna woodlands is constrained by sparse field data, which has motivated widespread fusion of field plots with spaceborne LiDAR reference data from the Global Ecosystem Dynamics Investigation (GEDI). Here, we show that such fusion can substantially inflate apparent model accuracy when the two reference sources sample different spatial domains. Using 44 field plots from the Abu-Gadaf Natural Reserved Forest (AGNRF), Sudan, and 56 GEDI L4A footprints drawn from a 50 km buffer surrounding the reserve, we trained Random Forest (RF), Gradient Boosting (GB) and Classification and Regression Tree (CART) models on Sentinel-1, Sentinel-2, SRTM and Dynamic World predictors and evaluated them under 10-fold, 2 km block spatial cross-validation. The merged dataset yielded apparently moderate performance (RF: RMSE = 9.40 Mg ha−1, R2 = 0.33). However, GEDI-derived AGB was 2.1 times higher than field-measured AGB (18.71 vs. 8.89 Mg ha−1; Kolmogorov–Smirnov D = 0.53, p

Why it matches plant phenotyping methods圃場・GEDI・衛星データを統合して植物群落の地上部バイオマス（AGB）を推定し、空間交差検証でモデル精度と空間ドメイン不一致によるバイアスを評価している。植物形質の推定手法の検証が中心で、単なる生物学的実験の routine measurement ではない。

abstractAccurate estimation of aboveground biomass (AGB) in dryland savanna woodlands is constrained by sparse field data, which has motivated widespread fusion of field plots with spaceborne LiDAR reference data from the Global Ecosystem Dynamics Investigation (GEDI).

Comparative evaluation of five biomass quantification methods in bermudagrass →

Accurate estimation of pasture biomass is essential for determining cattle stocking rates and grazing durations. The objective of this study was to comparatively evaluate five sensor-based systems for estimating aboveground Bermudagrass (Cynodon dactylon) biomass and identify the leading sensing approach for continued development and broader validation. The five systems included Structure-from-Motion (SfM), Ultrasound Sensor and Ski (US-Ski), Inertial Measurement Unit and Ski (IMU-Ski), Inertial Measurement Unit and Roller (IMU-Roller), and Depth Camera (DC). These systems were deployed on unmanned aerial and ground vehicles to measure crop height under identical field conditions. Regression models relating measured crop height to wet biomass yield (WBY) were developed as a common calibration framework for statistically comparing sensor performance. These empirical allometric equations were intended to support comparative benchmarking of the sensing systems and were not developed as final operational biomass prediction models for immediate field deployment. The influence of vegetation coverage on yield predictions generated by the crop height-based equations was also examined. The results indicated that the IMU-Ski system demonstrated the strongest overall comparative performance (R2 = 0.97; SeY = 1112 kg-wet/ha), followed by the DC system (R2 = 0.97; SeY = 1132 kg-wet/ha). Based on its overall benchmarking performance, including calibration accuracy, residual error and simplicity, the IMU-Ski system was identified as the leading sensing approach for continued development and broader validation among the five evaluated methods. The results also indicated that addition of vegetation coverage into the crop height-based regression models did not significantly improve prediction accuracy under the experimental conditions evaluated.

Why it matches plant phenotyping methods複数のセンサーシステムによる牧草バイオマス推定を比較・校正・ベンチマークしており、植物形質の取得法と技術性能の評価が研究の中心です。

abstractcomparatively evaluate five sensor-based systems for estimating aboveground Bermudagrass (Cynodon dactylon) biomass

High‐throughput assessment of plant stand establishment, seedling vigor, and light interception in peanut using UAV‐based RGB and multispectral imagery →

Abstract In peanut ( Arachis hypogaea L.), plant stand establishment, seedling vigor, and canopy growth are key determinants of crop performance; yet traditional ground‐based assessment methods can be destructive, labor‐intensive, and limited in throughput. This study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut. Six runner‐type peanut cultivars were evaluated in 2024 and seven in 2025, with each cultivar represented by two seed size classes (small and large), to generate variation in these traits. Within‐row vegetation discontinuity‐based plant stand ratings for estimating plant stand count ( R 2 = 0.81–0.90), together with canopy coverage for assessing seedling biomass ( R 2 = 0.77–0.82) and light interception ( R 2 = 0.96–0.98), were the best‐performing vegetation metrics. These vegetation metrics provided similar or greater cultivar separation compared with ground‐based measurements. In contrast, several vegetation indices exhibited strong correlations with ground‐based measurements but provided inconsistent cultivar rankings and statistical groupings. MS imagery outperformed RGB imagery for plant stand and seedling biomass assessment. Overall, these results demonstrate that UAV‐derived canopy metrics provide reliable, high‐throughput tools for early‐ to mid‐season crop assessment and offer scalable alternatives to traditional ground‐based approaches for agronomic, crop physiological, and plant breeding research.

Why it matches plant phenotyping methodsUAV画像から植物体の出芽・苗勢・バイオマス・光 interception を推定する植 phenotyping 手法を開発・評価しており、取得指標の性能検証が研究の中心です。

abstractThis study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut.

Analytically derived sphere correction enables transferable RGB-D fruit sizing across fruit shapes and depth-sensing principles. →

Abstract Depth cameras measure the distance to a fruit's surface, whereas converting its silhouette into physical dimensions requires the depth of its center; corrections for this offset have so far been empirical, and therefore bound to the crop, sensor, and dataset they were fitted on. This paper derives the correction analytically. For a spherical fruit, integrating the surface-depth distribution over the visible hemisphere yields a closed-form sphere correction whose coefficient follows from sampling geometry, together with a theoretical justification of the median mask depth as the representative statistic. Combined with deep instance segmentation on RGB-D imagery of hydroponic melons, the empirically optimal coefficient coincided with the derived value, and the pipeline reached R 2 of 0.966 for fruit length (MAE 1.43 mm), 0.959 for width (1.84 mm), and 0.861 for end-to-end fresh weight (MAPE 4.6%). The analytical form made the measurement transferable. Applied unchanged to cylindrical mini-cucumbers, the pipeline held mm-level accuracy (width MAE 0.52 mm; fresh weight R 2 0.955 after coe cient refitting), with the correction's negligibility predicted in advance by an R / Z corollary; across active-stereo and time-of-flight cameras, the optimal coefficients proved non-interchangeable, identifying the coefficient as a physical parameter that absorbs geometry, sensor physics, and fruit shape. A field system that fuses and cross-verifies the two sensors, with an error-propagation confidence gate and parameterized grading logic, reproduced 2-3% fresh-weight error and 90.9% confirmed-judgment grading accuracy over four validation sessions in a commercial greenhouse unseen during development. Throughout, geometric components transferred unchanged while learned and regression components required recalibration - a boundary the model predicts and the system itself monitors.

Why it matches plant phenotyping methodsRGB-D画像と深度補正を用いて果実の寸法・重量を推定する手法を開発し、異なる果形・センサー・圃場で精度検証しており、植物表現型取得が中心である。

abstractThis paper derives the correction analytically.

Individual-Tree Stem Volume Modeling Using Handheld LiDAR and UAV-SfM Photogrammetry in a Mediterranean Mixed Forest →

Integrating ground-based and aerial remote sensing for individual tree-level stem volume modeling remains underexplored in Mediterranean mixed forests, despite the growing need for cost-effective, automated forest inventory approaches. This study evaluated the combined use of Handheld Laser Scanning (HLS) and Unmanned Aerial Vehicle (UAV)-based Structure from Motion (SfM) photogrammetry for individual-tree stem volume modeling in a mixed stand in Castilla y León, Spain, dominated by Pinus halepensis, Pinus pinea, Quercus faginea, and Cupressus sempervirens. Two open-source HLS processing tools; the Forest Structural Complexity Tool (FSCT) and 3D Forest Inventory (3DFin), were compared for individual tree attribute extraction, with FSCT outperforming 3DFin across all species. Reference stem volumes were derived by applying species-specific Spanish National Forest Inventory (SNFI) allometric equations to FSCT-extracted diameter and height values. Random Forest models were then built using UAV-SfM crown metrics as predictors, testing two image overlap configurations: 80 × 80 F (80% front and side overlap) and 80 × 60 CF (80% front, 60% side, cross-flight). The 80 × 80 F configuration produced the best-performing model (R2 = 0.730), with 80 × 60 CF achieving comparable accuracy (R2 = 0.688), results confirmed by spatially independent leave-one-plot-out cross-validation (LOPO-CV R2 = 0.627 and 0.613, respectively). These results show that combining HLS and UAV-SfM through a predominantly open-source workflow offers a viable, reproducible approach to stem volume modeling in structurally complex Mediterranean mixed forests.

Why it matches plant phenotyping methodsHLSとUAV-SfMを用いて個体樹の直径・樹高から幹材積を推定する再現可能な計測・解析ワークフローを構築し、複数ツールと飛行条件を比較検証しているため、植物表現型取得が中心である。

abstractIntegrating ground-based and aerial remote sensing for individual tree-level stem volume modeling remains underexplored

Utilizing Vegetation Indices Derived from VNIR-SWIR Hyperspectral Data to Characterize Growth, Maturation, and Senescence in Wheat and Barley →

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions.

Why it matches plant phenotyping methodsRGB画像、赤外線サーモグラフィー、VNIR–SWIRハイパースペクトルを統合した高スループット表現型解析ワークフローが中心的に記述され、複数の植物形質・状態を定量化している。

abstractA high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence.

Cucumber3DGaussians: Plant architecture analysis using semantic-aware Gaussian splatting →

Monitoring continuous agricultural canopies is fundamentally limited by the geometric constraints and computational bottlenecks of traditional 3D reconstruction. This study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture. To drive component-specific optimization, we evaluated custom-trained convolutional networks (YOLO11) against a zero-shot foundation model (SAM3), determining that SAM3 provided the necessary boundary precision for accurate spatial isolation. The optimized 3DGS model outperformed implicit NeRF baselines, preserving fine-scale morphological details at real-time rendering speeds ( ≈ 48 FPS). To enable actionable measurement, a uniform voxelization protocol was applied to the point cloud, successfully neutralizing algorithmic densification bias. This technical framework yielded highly accurate physical geometry, achieving a Root Mean Square Error (RMSE) of ≤ 0.59 cm against in situ leaf measurements. Transitioning to agronomic interpretation, the pipeline was deployed to quantify complex canopy architecture. It mathematically mapped structural congestion zones and provided a temporal validation of a standard pruning intervention, explicitly capturing the geometric increase in lower-canopy porosity and the upward translation of biomass. This framework provides a robust, scale-accurate tool for monitoring plant architecture and guiding dynamic canopy management.

Why it matches plant phenotyping methods植物キャノピーの3D形状を取得・定量化する画像ベースの表現型解析パイプラインを開発し、実測葉寸法で精度検証しているため、方法が研究の中心である。

abstractThis study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture.

Canopy height from drone photogrammetry better predicts aboveground biomass than vegetation greenness indices in a semi-arid savanna →

Semi-arid rangelands support livelihoods and key ecosystem services, yet sustainable management depends on accurate and scalable monitoring of herbaceous aboveground biomass (AGB). Field-based measurements are spatially limited, while satellite-derived vegetation indices often perform poorly in complex savanna systems such as the Kalahari. Using unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient. Canopy height consistently predicted AGB across all grazing levels, whereas normalised difference vegetation index (NDVI) effects were weak and grazing-dependent. The UAV-derived canopy height showed strong relationships with total herbaceous AGB, explaining up to 72% of observed variation, whereas vegetation greenness measured using NDVI showed limited predictive power. In contrast, predicting biomass of foraging importance proved challenging, with UAV-derived structural and spectral metrics explaining only a small proportion of variation. Together, these findings highlight the value of UAV-derived structural measurements over traditional spectral indices for fine-scale rangeland monitoring in semi-arid systems, while underscoring the limitations of current UAV-based spectral and structural metrics for assessing forage value across species and sites.

Why it matches plant phenotyping methodsUAV SfMフォトグラメトリから植物群落の canopy height を抽出し、地上部バイオマス予測性能を評価しており、植物形質取得法の技術的適用・検証が中心である。

abstractUsing unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient.

Canopy height from drone photogrammetry better predicts aboveground biomass than vegetation greenness indices in a semi-arid savanna →

Semi-arid rangelands support livelihoods and key ecosystem services, yet sustainable management depends on accurate and scalable monitoring of herbaceous aboveground biomass (AGB). Field-based measurements are spatially limited, while satellite-derived vegetation indices often perform poorly in complex savanna systems such as the Kalahari. Using unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient. Canopy height consistently predicted AGB across all grazing levels, whereas normalised difference vegetation index (NDVI) effects were weak and grazing-dependent. The UAV-derived canopy height showed strong relationships with total herbaceous AGB, explaining up to 72% of observed variation, whereas vegetation greenness measured using NDVI showed limited predictive power. In contrast, predicting biomass of foraging importance proved challenging, with UAV-derived structural and spectral metrics explaining only a small proportion of variation. Together, these findings highlight the value of UAV-derived structural measurements over traditional spectral indices for fine-scale rangeland monitoring in semi-arid systems, while underscoring the limitations of current UAV-based spectral and structural metrics for assessing forage value across species and sites.

Why it matches plant phenotyping methodsUAV-SfMによるキャノピー高とスペクトル指標から植物群落のバイオマスを推定し、手法の予測性能を比較評価しており、植物形質取得が研究の中心です。

abstractUsing unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient.

Canopy height from drone photogrammetry better predicts aboveground biomass than vegetation greenness indices in a semi-arid savanna →

Semi-arid rangelands support livelihoods and key ecosystem services, yet sustainable management depends on accurate and scalable monitoring of herbaceous aboveground biomass (AGB). Field-based measurements are spatially limited, while satellite-derived vegetation indices often perform poorly in complex savanna systems such as the Kalahari. Using unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient. Canopy height consistently predicted AGB across all grazing levels, whereas normalised difference vegetation index (NDVI) effects were weak and grazing-dependent. The UAV-derived canopy height showed strong relationships with total herbaceous AGB, explaining up to 72% of observed variation, whereas vegetation greenness measured using NDVI showed limited predictive power. In contrast, predicting biomass of foraging importance proved challenging, with UAV-derived structural and spectral metrics explaining only a small proportion of variation. Together, these findings highlight the value of UAV-derived structural measurements over traditional spectral indices for fine-scale rangeland monitoring in semi-arid systems, while underscoring the limitations of current UAV-based spectral and structural metrics for assessing forage value across species and sites.

Why it matches plant phenotyping methodsUAV-SfMによるキャノピー高とスペクトル指標を用いた植物バイオマス推定を比較・評価しており、植物形質の取得・推定手法が研究の中心である。

abstractUsing unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient.

Bridging Magnetic Field Agriculture and UAV-Based Precision Monitoring: An Integrated Dual-Stream Evidence Synthesis and Conceptual Framework for Field-Scale Validation →

Magnetic field (MF) technologies have been explored in agriculture since the 1930s, with research activity increasing markedly since 2016. However, they have not achieved mainstream adoption, partly because no MF-specific validated methodology exists for evaluating their effects under realistic field conditions. Unmanned Aerial Vehicle (UAV)-based multispectral sensing represents a potential pathway to address this limitation by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, thereby enabling, for the first time, the systematic evaluation and validation of MF treatment responses under open-field conditions. To realise this potential, however, a common evidential basis must first be established by identifying crop physiological variables that are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream evidence synthesis of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency (NUE)/nitrogen assimilation, above-ground biomass (AGB), leaf area index (LAI), and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R2 = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The review revealed a complete absence of integration between the two research domains within the reviewed corpus, despite their strong biological and methodological compatibility. The proposed framework is conceptual and remains to be experimentally validated; it provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems.

Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングによる作物生理形質の推定をレビューし、地上真値・植生指数・機械学習を統合した検証フレームワークを提案しており、植物表現型の取得・推定方法が中心です。

abstractUnmanned Aerial Vehicle (UAV)-based multispectral sensing represents a potential pathway to address this limitation by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale

Percent Tolerance to Phosphorus Deficiency (PTPD) as a Potential Metric for Genotypic Screening in Soybean ( Glycine max L.). →

Phosphorus (P) deficiency severely limits soybean ( Glycine max L.) productivity. This study proposed a three-stage screening framework to identify reliable traits and P-efficient genotypes. In Experiment I, percent tolerance to phosphorus deficiency (PTPD) was calculated for ten growth parameters across 98 genotypes under P-deficient and control conditions. Principal component analysis and comprehensive evaluation identified six key indicators in Experiment I, which were subsequently refined to five indicators through further analysis: SPAD at V3 and R1, photosynthetic rate at R1, shoot dry weight at R8, and seed number per plant at R8. Experiment II re-evaluated these traits using 12 contrasting genotypes under three P levels, identifying CN 15 as the most P-efficient and SN 22 as the most P-inefficient. Experiment III further revealed that CN 15 maintained superior PSII performance and exhibited a 26.2% increase in grain P-utilization efficiency under 0 µM KH 2 PO 4 treatment. This integrated framework offers a preliminary reference for screening P-efficient soybean genotypes under controlled conditions, pending field evaluation.

Why it matches plant phenotyping methodsリン欠乏耐性を評価するPTPDと三段階の形質選抜フレームワーク自体を提案・検証しており、単なる生物学的処理試験ではなく、植物形質に基づく遺伝子型スクリーニング手法が中心である。

abstractThis study proposed a three-stage screening framework to identify reliable traits and P-efficient genotypes.

Non-destructive detection of dry matter content based on Vis-NIR spectroscopy and wavelength selection. →

A rapid and non-destructive method for predicting dry matter (DM) content in leeks was developed using visible and near-infrared (Vis-NIR) spectroscopy, coupled with what we believe to be a novel wavelength selection algorithm. Reflectance spectra (397.7-1716.7 nm) were acquired from 288 leek samples collected from three production areas in Nantong, China, and DM content was determined by oven-drying. The full-spectrum partial least squares (PLS) model yielded moderate prediction accuracy, with R P2 of 0.7963 and RMSE P of 1.14%. To improve performance, the iterative ranking-based variable elimination PLS (IRIVE-PLS) algorithm was proposed, which integrates multiple importance metrics to iteratively eliminate uninformative wavelengths. The algorithm autonomously identified the red-edge region (680-780 nm) as the most informative spectral feature, enriching its proportion from 9.5% in the full spectrum to 10.6% in the selected set. The IRIVE-PLS model achieved excellent prediction performance, yielding R P2 of 0.9683 and RMSE P of 0.45%, significantly outperforming conventional wavelength selection methods. The proposed approach provides an accurate, interpretable, and non-destructive alternative for leek quality assessment, with strong potential for online sorting applications in the vegetable industry.

Why it matches plant phenotyping methodsVis-NIR分光と新規波長選択アルゴリズムを用いて、リーキの乾物含量という植物形質を非破壊推定する手法の開発・性能評価が中心である。

abstractA rapid and non-destructive method for predicting dry matter (DM) content in leeks was developed using visible and near-infrared (Vis-NIR) spectroscopy, coupled with what we believe to be a novel wavelength selection algorithm.

Genomic selection and genome-wide association studies using UAV-derived plant height and aboveground biomass of maize →

Plant height (PH) and aboveground biomass (AGB) are critical agronomic traits that determine the yield potential of maize ( Zea mays L . ). However, the application of genomic selection (GS) and genome-wide association studies (GWAS) in maize breeding is often hindered by the limitations of phenotypic data collection, which is typically characterized by low throughput and inadequate accuracy. To address this challenge, we employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB in a panel of 817 maize hybrids derived from 364 inbred lines over two growing seasons. Our results demonstrated that the integration of UAV-derived LiDAR point clouds with crop surface models (CSMs) enabled robust estimation of pH across multiple years ( R 2 > 0.90). Furthermore, a three-dimensional AGB estimation model was developed using UAV-derived PH and canopy coverage (CC), achieving high estimation accuracy ( R 2 > 0.83). Subsequently, the UAV-derived PH and AGB were utilized for GS and GWAS analyses. Replicated 10-fold cross-validation showed that the mean predictability was 0.504 for PH and 0.402 for AGB across eight commonly used GS models. Moreover, of the 66,066 potential crosses derived from the 364 inbred lines, the top 200 crosses selected for AGB showed up to twice the AGB of the bottom 200 crosses. Field validation demonstrated that the mean ear weight (EW) in the AGB top group was 39.0% higher than that in the bottom group. A total of 16 and 11 significant SNPs were identified by at least two GWAS methods for PH and AGB, respectively. Based on these SNPs, 81 candidate genes were functionally annotated, six of which were simultaneously associated with both traits. The candidate gene association analysis suggested that variations in the promoter region of ZmFLA9 may affect both traits. Overall, our study highlights the potential of UAV-based high-throughput phenotyping to accelerate maize genomic breeding by enabling rapid, precise, and large-scale trait assessment.

Why it matches plant phenotyping methodsUAVのLiDAR・RGBデータから草丈と地上部バイオマスを推定する高スループット表現型測定モデルを開発・検証しており、フェノタイピング手法が研究の中心です。

abstractwe employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB

Cover crop biomass estimation using UAV-based multispectral feature fusion and machine learning →

Cover crops offer essential agroecosystem benefits, including reduced soil erosion, weed suppression, and improved soil health. Aboveground biomass (AGB) is a key indicator of these benefits; however, field-based quantification is often limited, which hinders effective cover crop management decisions. This study integrated unmanned aerial vehicle (UAV)-based multispectral imagery with machine learning (ML) models to estimate AGB in cover crops across two water-limited regions of Texas. Ground-truth and imagery data were collected over three years (2023–2025) for winter rye ( Secale cereale L.) in Lamesa and two years (2023–2024) for winter wheat ( Triticum aestivum L.) in Chillicothe under varying irrigation regimes. Five ML algorithms, random forest, support vector regression, extreme gradient boosting, partial least squares regression (PLSR), and artificial neural network (ANN), were evaluated across four individual and eleven feature fusion datasets. The ANN model consistently achieved the highest predictive accuracy, particularly when vegetation indices were combined with structural features (R² = 0.87, RMSE = 9.08 g m - ²), while PLSR showed the weakest performance. Grouped validation (leave-one-year-out, leave-one-species-out, and leave-one-treatment-out) revealed reduced model performance compared to random (70/30) splitting of pooled data, yet the ANN maintained moderate predictive ability, indicating reasonable generalizability across years, species, and management conditions. Shapley additive explanations (SHAP) revealed key predictors in the ANN model, including plant height, chlorophyll vegetation index, chlorophyll sensitive index, blue band reflectance, modified chlorophyll absorption in reflectance index, dissimilarity, correlation, and enhanced green vegetation index. These findings demonstrate the effectiveness of UAV-ML integration for accurate AGB estimation and highlight the potential for scalable, data-driven cover crop monitoring in water-limited environments and beyond.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を統合し、植物の地上部バイオマスを推定する手法を開発・比較検証しており、表現型取得が研究の中心である。

abstractThis study integrated unmanned aerial vehicle (UAV)-based multispectral imagery with machine learning (ML) models to estimate AGB in cover crops across two water-limited regions of Texas.

Rapid forage yield and growth rate measurement using a remotely controlled LiDAR sensor in perennial ryegrass field plots →

Biomass is a key trait in pasture plant breeding and agronomy, but measuring Dry Matter Yield or Fresh Weight across large numbers of samples is labour intensive and costly. Efficient biomass assessment systems must balance accuracy, speed, and cost, while ideally enabling non-destructive measurements. We developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution. The system operates under ambient light conditions at a ground speed of 2.7 km per hour. It was evaluated in small-plot perennial ryegrass trials at two field sites in New Zealand, across two seasons at site A and one season at site B. At site A, correlations between LiDAR-derived height and fresh weight ranged from 0.33 to 0.74 across individual measurement cycles, with an overall multilevel R² of 0.72. At site B, the multilevel correlation increased to R² = 0.88. Weekly LiDAR scans at site B were used to estimate plot-level growth rates for 60 plots, demonstrating improved temporal resolution. Statistically significant differences in growth rate within regrowth cycles were detected among plots. The platform reliably differentiates perennial ryegrass plots based on biomass and offers higher temporal resolution than traditional methods.

Why it matches plant phenotyping methodsLiDARプラットフォームを開発し、草高から牧草バイオマスと成長率を非破壊推定・検証しており、植物形質取得手法が研究の中心です。

abstractWe developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution.

An integrative high-throughput phenotyping framework for assessing canopy growth dynamics and light interception in potato →

Quantifying the canopy growth dynamics and light interception capacity under different management practices laid the physiological foundation for potato yield formation. However, the traditional manual measurement methods are labour-intensive, time-consuming, and incapable of capturing time-series dynamics. To address this, we proposed a novel high-throughput strategy that integrates UAV-based RGB imaging with a piecewise physiological model. Furthermore, how Nitrogen(N)-Potassium(K) interaction affects the temporal canopy growth dynamics, light interception, and tuber yield was determined. The results indicated that: (1) Among the 11 secondary indices extracted from the canopy growth dynamic curves, the interaction of N and K had the greatest effect on the maximum canopy duration and the total canopy growth curve integral. The direct path coefficients of N and K inputs on these two parameters were 0.847 and 0.805, and 0.234 and 0.148, respectively. (2) There was a strong linear relationship between the integral area under the curve (S∫) and the total plant dry weight, with R² at 0.90 in 2023-2024. A simplified net photosynthetically active radiation utilisation assessment framework that achieved high accuracy with minimal parameter was built. (3) Prolonging the maximum canopy continuous coverage time is the main way to improve potato yield. The overall effect of N input on yield was significantly higher than that of K fertiliser, with a total effect value of 1.428. Optimising the N-K interaction improves nutrient precision and light interception. The integration of UAV remote sensing and the crop physiological-ecological model enables the tracking of potato canopy dynamics, which is helpful for optimising management practices to improve potato yield.

Why it matches plant phenotyping methodsUAV RGB画像と生理モデルを統合した高スループット手法を開発し、ジャガイモのキャノピー成長動態と光 interception を時系列で推定することが中心である。

abstractTo address this, we proposed a novel high-throughput strategy that integrates UAV-based RGB imaging with a piecewise physiological model.

Gaussian mixture integration of CNN and transformer models for forest biomass estimation: A multi-sensor fusion approach with uncertainty quantification →

Accurate estimation of forest aboveground biomass (AGB) is critical for carbon accounting, ecosystem monitoring, and climate change mitigation. While remote sensing offers broad spatial coverage, standard deterministic models often struggle to capture the complex relationship between spectral information and structural forest attributes, and they frequently lack reliable quantification of uncertainty. This study presents a novel probabilistic framework that integrates convolutional neural networks (CNNs) and transformer architectures for AGB estimation using a multi-sensor fusion of Sentinel-1/2 imagery. The framework employs a customized, adaptive version of the Swin Transformer-reconfigured specifically for regression tasks and streamlined to a single-stage architecture-and implements a Gaussian Mixture Modeling (GMM) approach to ensemble model outputs. This framework enables the decomposition of predictive uncertainty into epistemic (model-related) and aleatoric (data-inherent) components. Evaluated across heterogeneous forest regions in Montana and Idaho, the CNN-transformer integration achieved superior performance, with R 2 =0.83, RMSE=19.16 Mg/ha, and MAE=13.37 Mg/ha, outperforming standalone CNNs (R 2 =0.81) and vision transformers (R 2 =0.80). Transfer learning and fine-tuning experiments demonstrated high model robustness, improving prediction accuracy in independent test regions by 39% in RMSE. Spatially explicit analysis revealed that while CNNs provide stable local feature extraction, the Swin Transformer’s self-attention mechanism significantly mitigates errors on topographically complex slopes by leveraging non-local spectral cues to compensate for shadowing and geometric distortions. The results highlight that an ensemble approach leveraging the complementary strengths of CNNs and customized transformers provides the transparency and precision required for operational carbon monitoring and high-resolution, trustworthy biomass mapping.

Why it matches plant phenotyping methods森林の地上部バイオマスという植物群落形質を、マルチセンサー画像とCNN・Transformer・GMMで推定する手法を開発し、比較評価と独立地域での検証を行っており、形質推定法が研究の中心である。

abstractThis study presents a novel probabilistic framework that integrates convolutional neural networks (CNNs) and transformer architectures for AGB estimation using a multi-sensor fusion of Sentinel-1/2 imagery.

Assimilation of UAS remote sensing and deep learning-derived crop parameters into DSSAT model for grain yield prediction →

In-season fine-scale (i.e., within-field experiment plot scale) crop grain yield (GY) prediction is critical for optimizing inputs, minimizing environmental impacts, and supporting sustainable food production. Traditional approaches, such as field surveys, are often costly and inefficient over large areas. As an alternative, remote sensing combined with crop simulation models (CSMs) has been increasingly applied for in-season GY prediction. This study investigates the potential of integrating Uncrewed Aircraft Systems (UAS)-based remote sensing data, deep learning, and CSMs to predict maize and soybean GY using a data assimilation approach. UAS multispectral imagery was collected, along with field-measured maize above-ground biomass (AGB) and soybean leaf area index (LAI) during the 2022 and 2023 growing seasons at experimental fields in Brookings, South Dakota. Maize AGB was measured at two growth stages, while soybean LAI was collected across four stages. One-dimensional convolutional neural networks (1D-CNNs) were used to estimate maize AGB and soybean LAI from canopy spectral, textural, and structural features derived from UAS imagery. These UAS and deep learning–derived crop traits were assimilated into DSSAT-Maize and DSSAT-Soybean models to optimize parameters, and the optimized models were subsequently used to predict GY. For maize, the DSSAT-Maize model achieved an R² of 0.62, an RMSE of 717.8 kg ha⁻¹, and an rRMSE of 6.7% for GY prediction. For soybean, the DSSAT-Soybean model achieved an R² of 0.81, an RMSE of 207.3 kg ha⁻¹, and an rRMSE of 4.9%. Overall, these results highlight the potential of combining high-resolution UAS data and deep learning–derived crop traits within a CSM framework through data assimilation, enabling fine-scale, in-season yield predictions and supporting precise agricultural management.

Why it matches plant phenotyping methodsUAS画像と深層学習により、作物のAGBおよびLAIという植物形質を推定する取得・解析手法が研究の中心であり、作物モデルへの同化と性能評価も行っている。

abstractOne-dimensional convolutional neural networks (1D-CNNs) were used to estimate maize AGB and soybean LAI from canopy spectral, textural, and structural features derived from UAS imagery.

Crop residue biomass estimation using UAV multispectral imagery: A comparison of feature selection methods and machine learning models →

Crop residues support soil health by reducing erosion, improving water retention, and contributing to carbon sequestration. Accurate estimation of crop residue biomass is essential for understanding residue distribution patterns and improving sustainable land management practices. Remote sensing, especially high-resolution UAV-based imaging, is a powerful tool for monitoring residue over agricultural fields, and many studies use remote sensing datasets for mapping residue cover (a 2D metric). However, few studies have evaluated residue biomass using remote sensing, despite biomass being more ecologically informative. This study uses high-resolution UAV multispectral imagery to predict crop residue biomass using feature selection and machine learning. Candidate predictors included raw bands, spectral indices, texture metrics, and digital-surface-model-derived topographic variables. Three feature selection methods—recursive feature elimination with cross-validation, Pearson correlation screening, and least absolute shrinkage and selection operator regression, were applied on the training set to identify informative predictors. Four machine learning models (Random Forest Regression, Support Vector Regression, CatBoost, and k-Nearest Neighbors [kNN]) were evaluated individually and in combination using simple averaging, weighted averaging, and stacked ensemble strategies. Results show that Pearson-selected features paired with kNN achieved the best performance (R 2 = 0.61, RMSE = 188.71 g m -2 ). Ensemble approaches did not outperform the best individual model, suggesting limited benefit from meta-learning under small-sample conditions. Across selection methods, red- and blue-band-related predictors were consistently retained, while textural and topographic variables were selected more selectively, indicating context-dependent contributions. Overall, simpler models with targeted feature selection can outperform more complex ensembles for UAV-based crop residue biomass estimation.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物残渣バイオマスを推定し、特徴選択法と複数の機械学習モデルを比較・評価することが中心で、植物由来バイオマスという明示的な状態量を技術的に推定している。

abstractThis study uses high-resolution UAV multispectral imagery to predict crop residue biomass using feature selection and machine learning.

Assessing the suitability of a developed photogrammetric and multispectral method for detecting biostimulant effects on plants →

The present study validates a custom multi-sensor system for high-resolution, non-phenotyping. The photogrammetric workflow was optimised by evaluating image density, algorithms, and camera calibration. Beyond validation, a case study demonstrated the system’s capacity to monitor early plant development following biostimulant treatment. Technical assessment revealed that prior internal camera calibration was unnecessary for the optics used. A reduced dataset of 120 images yielded reconstruction accuracies statistically comparable to full 360-image sets ( p > 0.05), confirming potential for maximised throughput efficiency by reducing processing time by approximately 66% without compromising data integrity. Analysis demonstrated that algorithmic settings determined reconstruction accuracy. Active parameter tweaks were essential for maximising surface area precision across all plant species ( R 2 ≥ 0.98). Conversely, volumetric accuracy required deactivation of these tweaks to maintain mesh consistency. While surface area estimation remained robust, volumetric precision showed species-specific variability driven by morphological complexity. Baseline parameters for accurate plant health scaling were established by defining species-specific vegetation index ranges (e.g., 0.38–0.82 for C. sativus ). The platform’s robustness was validated through a longitudinal study evaluating four treatments: yeast autolysate (A), a fungal biostimulant (F), their combination (AF), and a control (C). The system captured distinct morpho-physiological responses, demonstrating that autolysate-based treatments (A and AF) significantly enhanced biomass growth. The developed multi-sensor system recorded surface area expansions of 122% and 102% relative to the control ( p < 0.001), alongside a 110% increase in biological height. Fidelity of these 3D reconstructions was substantiated by a strong correlation ( R 2 = 0.96) between the 3D-derived leaf area index and ground-truth measurements. A key innovation of the pipeline is the integration of vertical distribution metrics as descriptive statistical tools, enabling high-resolution characterisation of canopy architecture. The plant health status metric evidenced enhanced physiological resilience in variants A and AF. Gravimetric analysis corroborated the non-destructive findings, confirming significant increases ( p < 0.001) in dried shoot weight of 77% (A) and 79% (AF). The validated system decoupled structural biomass from physiological health, offering broad utility across diverse phenotyping tasks. Such functionality streamlines the valorisation of industrial by-products into biopreparations, driving progress in sustainable agriculture.

Why it matches plant phenotyping methodsフォトグラメトリとマルチスペクトル計測による植物表現型取得システムの最適化・技術検証が研究の中心であり、植物形態・生理状態の測定性能を評価している。

abstractThe present study validates a custom multi-sensor system for high-resolution, non-phenotyping.

A dual-branch perception and hybrid attention integrated framework for temporal remote estimation of wheat leaf biomass. →

Introduction Wheat leaf biomass is a key indicator of crop growth, nitrogen status, and yield potential, and its accurate estimation is essential for precision agriculture. Unmanned aerial vehicle (UAV) remote sensing provides multi-stage phenological observations for non-destructive biomass monitoring. However, existing approaches often fail to capture the superimposed temporal patterns inherent to crop phenology, including short-term physiological fluctuations driven by management events and long-term seasonal growth trends, as well as the cumulative causal effects of early-stage conditions on final biomass accumulation. Methods This study proposed a dual-branch perception and hybrid attention integrated framework (DBAFN) for temporal estimation of wheat leaf biomass from UAV multi-temporal observations across key growth stages. Results and discussion Experimental results demonstrated that the DBAFN achieved the best performance, with the coefficient of determination (R²) of 0.87, root mean square error (RMSE) of 38.41 g/m², mean absolute error (MAE) of 27.77 g/m², and relative RMSE (RRMSE) of 17.29%. Overall, the proposed framework provided an effective solution for temporal biomass estimation and demonstrated strong generalization capability, as further validated by independent experiments across different ecological regions and wheat genotypes (R² = 0.816-0.820). Compared with conventional machine learning models, the DBAFN showed consistently higher accuracy and lower prediction error. Multi-source feature analysis indicated that the combination of reflectance, vegetation indices, and canopy height provides the most accurate estimation. Ablation experiments further confirmed the effectiveness of each module in improving model performance. The SHapley Additive exPlanations (SHAP) analysis revealed that the canopy height and key spectral features contribute most to biomass prediction, highlighting the importance of integrating structural and physiological information. This study demonstrates that integrating multi-scale temporal dynamics, hybrid attention mechanisms, and transformer-based dependency modeling significantly improves the reliability of UAV-based biomass estimation. It offers a practical, data-driven pathway for intelligent crop monitoring and precision nitrogen management.

Why it matches plant phenotyping methodsUAVマルチ時期リモートセンシングから小麦葉バイオマスという植物形質を推定する手法を提案し、独立地域・遺伝子型で検証しているため、フェノタイピング手法が中心である。

abstractThis study proposed a dual-branch perception and hybrid attention integrated framework (DBAFN) for temporal estimation of wheat leaf biomass from UAV multi-temporal observations across key growth stages.

Spatio-Temporal Monitoring of the Invasive Plant Alternanthera philoxeroides in a Narrow River Using Sentinel-2 Time-Series Data →

Alternanthera philoxeroides, an invasive alien species, spreads rapidly in river systems via vegetative propagation from stem fragments, requiring river-system-scale monitoring to understand its expansion dynamics and habitat preferences. This study used multi-temporal Sentinel-2 data to analyze spatio-temporal variations in fractional vegetation cover (FVC) within a 3.5 km river reach. FVC estimates derived from vegetation indices were validated against high-resolution aerial images, with an EVI-based model achieving the highest accuracy (RMSE = 9.2%), enabling reliable monitoring even in narrow (~24 m) channels. Time-series analysis from 2019 to 2024 revealed downstream expansion beginning in 2022. Annual maximum FVC (Cmax) was used to assess relationships with removal records and bank structures, showing that removal effects were temporary and more pronounced in the first year, while steel sheet-pile banks limited vegetation growth compared to concrete revetments. These results demonstrate that Sentinel-2 data can provide an effective and accessible tool for evaluating invasive plant dynamics and management effectiveness in low-flow river systems where A. philoxeroides dominates the floating vegetation community.

Why it matches plant phenotyping methodsSentinel-2時系列から侵入植物の植生被覆率を推定し、航空画像で精度検証しており、植物状態の取得・評価手法が中心です。

abstractFVC estimates derived from vegetation indices were validated against high-resolution aerial images, with an EVI-based model achieving the highest accuracy (RMSE = 9.2%)

Image-based trait extraction of Chenopodium quinoa grown under salinity and drought stress. →

Above ground crop traits provide an early indication of a plant's capacity to tolerate stress, and are important for breeding programs aimed at improving stress tolerance. In this work, we present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time under control, drought, and saline conditions. We used daily sideview imaging of individual plants, followed by segmentation of the panicle, leaf and stem using the deep learning U-Net++ segmentation model. The resulting segmentations were used in regression models to estimate leaf area, fresh and dry biomass, and leaf dry weight. The regression models showed high predictive accuracy. Using these estimates, we could calculate specific leaf area and leaf weight ratio. In addition, radiation use efficiency for above-ground biomass production was calculated, providing an independent physiological check on the consistency of these predictions. Finally, using automated measurements of plant transpiration we were able to determine daily averages of whole plant stomatal conductance. The results show that image-derived morphological traits can be used to accurately estimate biomass-related traits and to derive physiologically meaningful indicators of plant performance over time. This method provides a framework for non-destructive monitoring of quinoa responses to drought and salinity.

Why it matches plant phenotyping methods画像取得、深層学習セグメンテーション、回帰による植物形質推定を中核とする高スループット表現型解析手法であり、ストレス実験での単なるルーチン測定ではない。

abstractwe present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time

DINO-Pheno-Cluster: Integrating few-shot foundation models and UAV time-series for spatiotemporal growth and functional characterization of alfalfa →

Precise characterization of alfalfa growth dynamics is essential for breeding accessions with superior regrowth capacity, persistence, and yield stability. However, traditional plot level and coarse scale observations suffer from low signal to noise ratios particularly before canopy closure when phenotypic data are strongly affected by weeds and soil background. Moreover, existing studies rarely capture the dynamic mechanisms of crop development across the entire growth cycle. To address this, the DINO-Pheno-Cluster framework is introduced as a foundation model driven and mechanism decomposed phenotyping approach. This decoupled framework first utilizes DINO-XMem, a few-shot individual plant segmentation network based on DINOv3 and a dual memory mechanism. It subsequently applies parameterized dynamic modeling guided by growth process knowledge. Validation utilized high frequency Unmanned Aerial Vehicle (UAV) imagery from 12 time points across three growing seasons covering 127 alfalfa accessions. DINO-XMem achieved an 89.54% mean Intersection over Union (mIoU) under a 10-shot setting and maintained 86.77% under extreme 1-shot conditions. It successfully resolved dense canopy oversegmentation outperforming fully supervised baselines by 4.08% to 11.97% in mIoU. Crucially, the extracted high purity time series trajectories were parameterized into specific biological indicators including maximum growth rate, comprehensive regeneration index, and seasonal stability index. Gaussian Mixture Model (GMM) clustering based on these mechanistic traits identified four distinct functional ideotypes comprising High yield/High regrowth, Upright/Sparse, High stability/Persistent, and Short/Dense, all validated by ground measured biomass. This workflow establishes a precision screening tool for multi harvest crops advancing crop phenomics toward process level analysis.

Why it matches plant phenotyping methods植物の時系列UAV画像から個体を分割し、成長・再生・安定性などの形質を抽出するフェノタイピング手法の開発と検証が中心である。

abstractthe DINO-Pheno-Cluster framework is introduced as a foundation model driven and mechanism decomposed phenotyping approach

Using UAV-LiDAR for stem volume phenotyping in the genetic selection of radiata pine →

Abstract Context : Radiata pine breeding programmes rely on stem volume as a key objective, but phenotyping constraints limit selection intensity. UAV-LiDAR offers a scalable alternative to labour-intensive field measurements. Aims : We evaluated UAV-LiDAR-derived metrics as genetic selection proxies for stem volume in radiata pine genetic trials and quantified their utility relative to field-measured diameter at breast height (DBH). Methods: LiDAR metrics describing tree height and size were assessed against allometric stem volume (ASV) across 11 genetic trials (~27,000 trees, two trial series) using single-step genomic best linear unbiased prediction (ssGBLUP) with ~9,500 SNPs. Results : The 3D surface area of the individual tree convex hull (convexhull3D_area) had the highest correlation with ASV (up to r = 0.87) and similar heritability to DBH (mean h 2 = 0.26). LiDAR tree height had the highest heritability (mean h 2 = 0.37) and moderate to high genetic correlation with DBH. Selecting the top 100 genotypes by convexhull3D_area recovered 67-86% of potential ASV genetic gain, versus 88-96% for DBH. Including malformed trees in the genetic analyses of LiDAR traits marginally reduced their performance as stem volume proxies. Conclusion UAV-LiDAR-derived tree height and 3D convex hull surface present desirable properties to complement field phenotyping for stem volume selection in radiata pine. Their scalability, repeatability and high heritability support lower phenotyping costs, better early selection and accelerated genetic gain in radiata pine breeding.

Why it matches plant phenotyping methodsUAV-LiDARを用いて樹高・樹体サイズなどの形質を抽出し、茎体積の遺伝選抜プロキシとして相関・遺伝率・選抜効果を検証しており、植物フェノタイピング手法が中心である。

abstractWe evaluated UAV-LiDAR-derived metrics as genetic selection proxies for stem volume in radiata pine genetic trials

Multi dimensional variable influence mechanism analysis for wheat biomass estimation using fused UAV spectral and canopy height data and machine learning. →

Introduction Accurate and non-destructive estimation of wheat biomass is essential for crop growth monitoring, yield prediction, and precision agriculture. Unmanned aerial vehicle (UAV)-based remote sensing, integrating both spectral and structural information, has shown great potential for biomass estimation. However, the mechanisms by which different types of variables contribute to biomass prediction remain poorly understood, especially when using machine learning models. Methods In this study, we fused spectral reflectance, vegetation indices, and canopy height data derived from a UAV multispectral camera to estimate wheat biomass across four growth stages (jointing, booting, heading, and filling). Four machine learning algorithms-XGBoost, Random Forest Regressor (RFR), Support Vector Regressor (SVR), and LASSO-were employed and compared. Results and discussion The results showed that XGBoost achieved the highest accuracy (R 2 = 0.919, RMSE = 102.43 g/m², MAE = 77.43 g/m², RRMSE = 19.71%). Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that canopy height (CH) was the most important variable, followed by spectral indices such as R842 and GNDVI. The univariate and global contribution analyses demonstrated that structural and spectral variables played complementary roles in biomass estimation. This study provides a mechanistic understanding of variable contributions and offers a robust framework for UAV-based wheat biomass estimation.

Why it matches plant phenotyping methodsUAVスペクトル・キャノピー高データから小麦バイオマスを推定し、複数機械学習法を比較・評価する方法論的研究であり、植物形質取得が中心である。

abstractwe fused spectral reflectance, vegetation indices, and canopy height data derived from a UAV multispectral camera to estimate wheat biomass across four growth stages

Remote Sensing Applications in Sugar Beet Production: From Crop Monitoring to Precision Management →

Remote sensing has become an important tool for crop monitoring and precision agriculture, yet its applications in sugar beet production remain fragmented across sensing platforms, target traits and modelling strategies. This review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management. A structured search was conducted in Scopus and the Web of Science Core Collection for publications from 2003 to 2025, and 181 relevant peer-reviewed articles were retained for thematic analysis. The literature shows a clear increase in sugar beet remote sensing studies, particularly after 2015, coinciding with the availability of Sentinel-2 imagery and, from 2016 onwards, the growing use of unmanned aerial vehicle-based sensing. It also indicates a gradual shift from crop mapping and canopy monitoring towards disease detection, weed mapping, yield prediction and management-oriented applications. Current studies demonstrate the value of satellite, unmanned aerial vehicle and proximal sensing for retrieving canopy traits, assessing biotic stresses, estimating root yield and supporting field-scale management. However, sugar beet presents specific challenges because its economic value depends not only on canopy development or root biomass, but also on sucrose concentration, recoverable sugar yield, and processing quality. These quality-related traits remain less studied and are difficult to infer directly from canopy observations. Modelling approaches have evolved from vegetation-index-based empirical models towards machine learning, deep learning, multi-temporal analysis, data fusion and crop model assimilation, but issues of model transferability, ground-truth availability and operational decision support remain unresolved. Future research should strengthen multi-source observations, external validation, quality-oriented prediction and decision-support workflows to promote robust, scalable and economically meaningful remote sensing applications in sugar beet production.

Why it matches plant phenotyping methodsサトウダイコンのリモートセンシングによるキャノピー形質、ストレス、根収量などの推定手法を体系的にレビューしており、センシング基盤とモデル化・検証課題が中心的に扱われている。

abstractThis review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management.

Vegetation Biomass Estimation Using 3D Ground-Based Point Clouds: A Systematic Review →

Abstract Purpose of Review Ground-based 3D point cloud technologies, including static terrestrial laser scanning (TLS), mobile laser scanning (MLS), and close-range photogrammetry, are increasingly used for estimation of aboveground vegetation biomass as they provide detailed structural representations across vegetation types; however, a comprehensive synthesis of how point-cloud data are translated into biomass estimates remains lacking. This review evaluates current approaches, performance patterns, and methodological gaps in biomass estimation using 3D ground-based point clouds. Recent Findings We systematically reviewed and analyzed 160 research articles (comprising 171 device-specific studies) published until the end of 2025 (first appearing in 2010). Research was dominated by tree-based applications (74%), with limited attention to shrubs, grasslands or crops. TLS was the prevailing acquisition technology (78%), although MLS adoption is growing. Biomass estimation primarily relied on allometric equations, volume-based reconstructions (e.g., quantitative structure models, voxelizations, convex hull), and parametric regression models. Reported model performance was generally high in tree- and shrub-based studies (median R 2 > 0.8), but more variable in non-woody vegetation types. Despite rapid advances in 3D sensing, point-cloud-native deep-learning approaches remain rarely implemented in biomass estimation workflows. Summary Ground-based 3D sensing is maturing technically, yet methodological heterogeneity persists. Many workflows still depend on destructive calibration data, semi-manual preprocessing, and non-standardized modelling strategies, limiting reproducibility and cross-study comparability. Multi-sensor integration is emerging but lacks consistent upscaling frameworks. Future research should expand coverage of underrepresented vegetation types, promote standardized and automated processing pipelines, and systematically evaluate point-cloud-native deep learning architectures, both for extracting structural proxies and for assessing their capacity to estimate biomass directly.

Why it matches plant phenotyping methods3Dセンシングによる植物バイオマス推定手法を体系的にレビューし、取得技術、推定ワークフロー、性能、再現性、標準化を評価しており、表現型測定法が中心である。

abstractThis review evaluates current approaches, performance patterns, and methodological gaps in biomass estimation using 3D ground-based point clouds.

Improving ecological indicators of mangrove canopy height and aboveground biomass through multi-source data fusion on the Amazon coast →

Reliable ecological indicators of mangrove structure and carbon storage are essential for monitoring coastal ecosystem conditions, yet their accuracy remains uncertain in tall, structurally heterogeneous forests, where Earth observation products differ in sensor physics, spatial resolution, and acquisition dates. Here, we present a multi-scale framework to evaluate, calibrate, and improve two widely used ecological indicators of mangrove condition—canopy height and aboveground biomass (AGB)—across approximately 7000 ha of mangroves in the Marapanim estuary, northern Brazil. The framework integrates UAV photogrammetry, radar-derived digital elevation models (TanDEM-X and SRTM), and field measurements to quantify cross-scale discrepancies and identify the main sources of uncertainty affecting indicator retrieval. High-resolution UAV canopy-height models revealed exceptionally tall Avicennia forests reaching up to 53 m, among the tallest mangroves reported globally. At the local scale, mean AGB reached approximately 648 Mg ha −1 in the southern Avicennia -dominated sector and 430 Mg ha −1 in the northern mixed Rhizophora–Avicennia sector, with local maxima of ∼800 Mg ha −1 . In contrast, radar-derived products yielded substantially lower estimates of canopy height and biomass, with height differences of 8–10 m in tall and structurally heterogeneous stands. These discrepancies reflect the combined effects of sensor-dependent canopy representation, spatial averaging, and temporal mismatch between historical radar acquisitions and recent UAV observations. To improve the ecological interpretation of these products, we implemented a calibration strategy linking field and UAV measurements to satellite observations and complemented it with UAV-based three-dimensional volumetric reconstruction of individual trees as an independent structural check on allometric biomass estimates. Our results show that canopy height and AGB derived from coarse-resolution radar products can systematically underestimate mangrove structural condition and carbon storage in tall forests unless locally calibrated. Beyond documenting exceptionally tall and carbon-dense Amazonian mangroves, this study provides a transferable framework for evaluating and improving ecological indicators of forest structure and biomass in complex coastal ecosystems.

Why it matches plant phenotyping methodsUAV photogrammetry・レーダー・現地測定を統合し、マングローブの樹冠高と地上部バイオマスという植物形質の推定を評価・較正・改善する方法論が研究の中心である。

abstractHere, we present a multi-scale framework to evaluate, calibrate, and improve two widely used ecological indicators of mangrove condition—canopy height and aboveground biomass (AGB)—across approximately 7000 ha of mangroves in the Marapanim estuary, northern Brazil.

A Single-Operator Push-Cart Multi-Beam LiDAR Platform for Multi-Trait Field Phenotyping →

Here, we present a single-operator push-cart platform equipped with a 16-beam LiDAR. A push-button interface controls data acquisition, and the data processing pipeline removes ground points, filters noise, performs 5-cm voxelization, and produces plot-level canopy metrics. We validated biomass estimation in hairy vetch (Vicia villosa) and corn (Zea mays) leaf- and whole-plant thinning experiments. In vetch, voxelized estimation of plant volume correlated strongly with destructively measured biomass (r2 = 0.88), showing that the multi-beam LiDAR can produce biomass estimates comparable to previously reported methods. In corn, comparisons of perpendicular (0°) and multi-angle LiDAR beams showed significantly greater voxel counts in the upper canopy when angled beams were used (beam angle × height interaction, p < 0.001), demonstrating that multi-beam scanning provides greater penetration into the upper canopy than a single perpendicular scan plane. We also extended the suite of LiDAR-derived traits to include apparent leaf area index (LAI), mean tilt angle (MTA), persistent homology-based stand density, and plot-bounded foliage area density (FAD). The persistent homology algorithm distinguished between leaf-removal and plant-removal treatments (removal type × removal amount, p = 0.0039). LiDAR-derived LAI has been used to estimate canopy leaf area, but gap-fraction approaches do not fully exploit the ability of LiDAR to resolve distance. Plot-bounded FAD used ray length and interception distance within defined plot volumes and was more sensitive to plot-level treatments than apparent LAI or MTA, detecting differences associated with both the removal amount and removal type. These results show that a robust, portable, multi-beam LiDAR cart can reproduce plot-level canopy measurements and improve trait especially in research-sized plots.

Why it matches plant phenotyping methods携帯型マルチビームLiDARプラットフォームと処理パイプラインを開発・検証し、バイオマス、LAI、葉面積密度などの作物形質を推定しているため、フェノタイピング手法が研究の中心である。

titleA Single-Operator Push-Cart Multi-Beam LiDAR Platform for Multi-Trait Field Phenotyping

PlaFe: an outdoor platform for crop phenotyping under progressive drought →

The selection of genotypes adapted to water stress requires experimental facilities that allow environmental control without compromising physiological and yield relevance. The objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe, which comprised sixty-two high-volume prismatic lysimeters arranged in rows 1.2 m long and spaced 0.6 m apart. Soil water dynamics were monitored weekly using a weighting system. To validate PlaFe, two soybean genotypes were exposed to two water scenarios for forty days from R2 + 7d, during two growing seasons. Two irrigation treatments were applied: irrigation to keep soil water content over 60–70 % of field capacity (EH0), and irrigation equivalent to 35 % of that applied in EH0 (EH1). Water consumption, crop biomass, and pod number were determined at maturity. On average, water stress reduced both biomass and pod numbers by 40 %. However, reproductive efficiency varied among genotypes. Canopy temperature increased by 0.56 °C as daily water consumption decreased, demonstrating its potential to assess drought. These results demonstrate PlaFe’s potential for the accurate evaluation of crop response and adaptation to diverse water scenarios without compromising the complex plant-environment interactions inherent to field conditions.

Why it matches plant phenotyping methodsPlaFeという屋外半制御型フェノタイピングプラットフォームを設計・検証しており、植物の水消費、バイオマス、莢数、群落温度などの表現型評価が研究の中心である。

abstractThe objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe

Bayesian optimized color filter: A fast method for segmentation of plant phenotypes from 3D point cloud images →

Abstract Multispectral three‐dimensional (3D) imaging offers substantial potential for plant phenotyping, yet manual segmentation of plant organs remains a bottleneck in breeding programs. We developed a color‐based filtering workflow for faba bean ( Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband brightness via Bayesian optimization. Rather than maximizing geometric segmentation accuracy, thresholds are selected to maximize correlations between retained points and yield‐related traits, outperforming manual filtering, reducing user effort, and standardizing decisions. Across multispectral 3D point clouds, Bayesian optimization recovered index‐specific threshold ranges that yielded strong in‐sample correlations with grain yield ( r = 0.72), bean number ( r = 0.61), pod number ( r = 0.53), and straw biomass ( r = 0.72). Peak associations occurred at harvest for straw biomass, at 41 days before harvest (DBH) for seed yield, 33 DBH for bean number, and 34 DBH for pod number. Across the season, greenness‐based indices and broadband brightness metrics consistently showed stronger links with seed yield than pigment ratio or water status indices. For straw biomass and pod number, pigment ratio indices showed consistently lower correlations. Targeting trait‐relevant canopy signals via Bayesian optimization enables reliable, nondestructive assessment of relationships between spectral signals and yield‐related traits in faba bean. By optimizing thresholds to maximize trait correlations rather than geometric accuracy, the workflow can support earlier, more cost‐efficient identification of high‐performing genotypes under drought stress and contribute to strengthening high‐throughput phenotyping in breeding programs. This enables faster identification of canopy signals most relevant to target traits.

Why it matches plant phenotyping methods植物の3D点群画像から表現型を抽出するセグメンテーション手法を開発し、ベイズ最適化による閾値選択と性能評価を行っており、フェノタイピング手法が研究の中心である。

abstractWe developed a color‐based filtering workflow for faba bean ( Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband brightness via Bayesian optimization.

RGB-UAV-based phenotyping reveals the influence of planting density on physiological traits, biomass, and yield in buckwheat. →

Accurate estimation of crop yield and biomass using UAV-based remote sensing is influenced by flight altitude, plant density, canopy structure, vegetation index (VI) selection, and crop growth stage. This study evaluated how the joint optimization of these factors influences biomass and yield prediction in common buckwheat (Fagopyrum esculentum Moench var. 'Yangjeol'). Field experiments were conducted using broadcast seeding and drill seeding at four row spacings (12.5, 20, 30, and 40 cm) following the complete Latin square design. UAV RGB imagery was acquired at the third-leaf and full-flowering stages from 30, 50, and 60 m altitudes, and vegetation indices Excess Green Index (ExG), Green Leaf Index (GLI), and Normalized Green-Red Difference Index (NGRDI)were extracted. ANOVA with Tukey's HSD revealed significant variations in biomass and yield traits among sowing treatments (p < 0.005). The highest fresh weight was recorded under drill seeding at 12.5 cm spacing, while seed weight was consistently higher under all drill seeding treatments compared with broadcast seeding. The number of seeds per plant peaked under 40 cm spacing, indicating a trade-off between planting density and reproductive output. Strong and significant correlations between vegetation indices and ground-measured traits were observed (r = 0.82-0.98), but these relationships were highly dependent on growth stage, sowing configuration, and UAV altitude. The third-leaf stage under broadcast seeding and full flowering stage under drill seeding at 20 cm spacing showed the strongest and most consistent VI-trait associations. Among UAV altitudes, 50 m provided the most stable predictive performance across traits. ExG and GLI exhibited more consistent relationships with biomass and yield parameters than NGRDI. These findings demonstrate that no single UAV altitude, vegetation index, or growth stage is universally optimal. Instead, coordinated optimization of UAV operational parameters and sowing configurationsubstantially improves the reliability of UAV-based yield and biomass estimation in buckwheat.

Why it matches plant phenotyping methodsUAV RGB画像と植生指数を用いたバイオマス・収量推定の条件最適化と精度評価が研究の中心であり、植物形質の取得手法を実質的に検証している。

abstractAccurate estimation of crop yield and biomass using UAV-based remote sensing is influenced by flight altitude, plant density, canopy structure, vegetation index (VI) selection, and crop growth stage.

A sugar flow model predicts cell dynamics, weight and quality of tomato at varying sink-source ratios and temperatures. →

A new tomato fruit model predicts cell numbers, cell sizes, sugar contents, and fresh weight. Transport of water and saccharides from plant stem to fruit cells is computed following biophysical rules. Saccharide fruit sink is based on sugar metabolism, rates of cell division and expansion, and starch and cell wall dynamics. Osmotic and hydraulic potentials in cells and their vacuoles drive water import at given cell-wall extensibility. The interaction of demand and transport determines saccharide flow and biomass. We incorporated physiological responses to temperature, pruning, and plant shading. Existing and new parameters were calibrated with data from fruit heating and fruit pruning experiments of contrasting tomato cultivars. Model validation for different strategies of fruit heating and pruning, and plant shading was successful. Increased fruit temperature was shown to reduce fruit weight, as expected. Growth response to fruit pruning or shading were fully explained by changes in phloem sucrose concentration. Hydraulic conductivity of vascular tissue as well as sucrose and hexose carrier capacities were crucial fruit properties determining sugar flux. Model scenarios on knockdown of sucrose synthase and active hexose uptake affected sugar composition. The model creates an important step towards predicting fruit quality and taste under diverse growth conditions.

Why it matches plant phenotyping methodsトマト果実の細胞動態、糖含量、重量、品質を予測する新規モデルを開発し、複数条件・品種で較正および検証しているため、植物形質推定手法が中心です。

abstractA new tomato fruit model predicts cell numbers, cell sizes, sugar contents, and fresh weight.

Pasture Biomass Monitoring in Queensland Rangelands with UAV and Satellite Cascades →

Operational satellite monitoring of pasture biomass demands models that extrapolate beyond the range of properties on which they were calibrated. We show that the dominant failure mode of Sentinel-2 pasture-biomass models in tropical Australian rangelands is the saturation and phenological inversion of greenness-based vegetation indices across sites, and that this failure can be substantially repaired with open climate and topsoil covariates from public archives. The work builds on a hierarchical pipeline that scales clip-and-weigh ground truth (n=1120 tare-corrected samples across eleven sites on five Queensland properties) through UAV digital-surface-model imagery to Sentinel-2 predictions, using TabPFN – a pre-trained transformer foundation model for small tabular data – as the regressor at all three nested spatial scales, and a seven-class deep-learning pasture mask (overall accuracy 98.6 %) to suppress mixed-pixel noise. Under a leave-one-site-out (LOSO) cross-validation protocol on twenty site-date aggregates across nine sites, spectral-only models failed to transfer across sites (R2=−0.21, RMSE=4.79 t ha-1). Appending open climate (Open-Meteo ERA5) and soil (SoilGrids 2.0) covariates, and switching to a gradient-boosted regressor on log-transformed biomass, lifted LOSO R2 to +0.07 and reduced RMSE to 4.19 t ha-1. A leaf-nitrogen growth trajectory, predicted by the TabPFN nitrogen regressor developed in our earlier pasture chemistry work, reduced LOSO error by a further 11 % relative to greenness-only growth features. Three additional covariate classes – BARRA-R2 reanalysis climate, three independent fractional-cover products, and Sentinel-1 C-band SAR backscatter – were tested and rejected, all hitting the same RMSE floor. The symmetric negative results suggest that the residual LOSO ceiling at the current nine-property footprint is a sample-size and sensor-saturation limit rather than a feature-engineering one, and that the most tractable operational path forward is to stratify the production model by climatic zone and Queensland Land Type rather than pursue further covariates within a single global learner. Expanding UAV calibration footprints and integrating open climate, soil and plant-chemistry data are complementary, not competing, investments for operational rangeland remote sensing.

Why it matches plant phenotyping methodsUAV・衛星画像、マスク処理、回帰モデルを組み合わせて牧草バイオマスを推定する測定パイプラインを構築し、サイト外交差検証で性能評価しているため、植物形質取得手法が中心である。

abstractThe work builds on a hierarchical pipeline that scales clip-and-weigh ground truth (n=1120 tare-corrected samples across eleven sites on five Queensland properties) through UAV digital-surface-model imagery to Sentinel-2 predictions

Dynamic, adaptive and modular Digital Twin framework for resource-efficient Controlled Environment Agriculture. →

In Controlled Environment Agriculture (CEA), traditional fixed set-point control is replaced by dynamic control strategies. These strategies enable joint optimization of resource use efficiency and biomass output, leverage electricity price fluctuations to reduce energy costs, and employ targeted environmental stressors to enhance crop quality and physiological resilience. Implementation of dynamic control strategies, however, builds upon real-time monitoring, robust data integration and management, and high-fidelity predictive modeling. These capabilities can be effectively provided through a Digital Twin (DT). This study introduces a novel open-source DT framework designed to support dynamic control strategies in CEA, addressing challenges in scalability, generalizability and interoperability. The framework is centered on the IoT platform ThingsBoard, providing unified, scalable data acquisition and management across heterogeneous sensor and actuator networks through vendor-agnostic integration and standardized interfaces. A significant contribution is its physics-based modeling backend, built on ordinary differential equation models developed in Modelica and exported as Functional Mock-up Units (FMUs). To ensure model accuracy across varying biological conditions, a parameter estimation pipeline is developed to calibrate and adapt these FMUs against experimental data. Building on this, a dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control. Furthermore, the framework incorporates a Multirate Moving Horizon Estimation (MMHE) state estimator to estimate critical unmeasured variables, such as plant biomass. This estimator is specifically designed to handle multirate data, maintaining continuous estimates even when certain sensors provide frequent data while others are sparse or infrequent. Demonstrated through a simulation-based case study modeling lettuce growth in a vertical hydroponic farm, the DT framework's architectural feasibility and virtual modeling capabilities are verified. Using synthetic data generated from a known true parameter set, the calibrated growth model achieved a low cross-validation prediction error, with an RMSE of 0.221g and an NRMSE of 6.62% on an independent test set. The MMHE-based state estimator effectively maintained continuous biomass estimates despite sparse synthetic measurements and model mismatch. These findings underscore the framework's potential as a robust and extensible foundation for future physical DT implementations in CEA, enabling a 31 transition toward dynamic, data-driven, and energy-aware operations.

Why it matches plant phenotyping methods植物バイオマスという観測可能な植物形質を、デジタルツインの状態推定器と動的モデルで継続的に推定する方法を開発・検証しており、単なる栽培制御や routine measurement ではない。

abstracta dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control.

ClearDepthIAS enables automated high-throughput quantification of roots in soil-grown taproot crops. →

Understanding root system architecture (RSA) is critical for improving crop productivity and resilience, yet phenotyping root traits such as root growth angle and rooting depth remains technically challenging, especially at high throughput. Here, we present ClearDepthIAS, a high-throughput imaging and analysis platform that enables non-destructive, automated quantification of root architecture traits in taproot system crops. By capturing and stitching 360° images of roots growing along the transparent walls of pots and applying deep learning-based segmentation (ClearDepth-WRT), we measured wall root shallowness (WRS)-a proxy for root growth angle-with high precision. We demonstrated for the tap root systems of soybean and canola that the system accurately detects root tips, quantifies their vertical distribution, and extracts biologically meaningful traits such as root area, distribution indices, and growth angles. Validation experiments in canola and soybean demonstrated that WRS can correlate with root crown architecture in mature plants, both in greenhouse and field settings. Furthermore, WRS and root distribution indices derived from ClearDepthIAS are predictors of early root architecture and can be correlated with root biomass distribution across soil depths under field conditions; however, environmental interactions may influence these relationships and weaken or even negate such correlations, as observed when comparing field to field variation in root system architecture. Our system enables efficient phenotyping of genetically diverse populations, with medium to high trait heritability, supporting its utility for genome-wide association studies and breeding. ClearDepthIAS accelerates the development of root ideotypes for improved resource acquisition and carbon sequestration, offering a scalable tool for supporting climate-resilient agriculture.

Why it matches plant phenotyping methods植物根系形態を自動取得・定量化する画像解析プラットフォームを開発し、精度と圃場での妥当性を検証しており、フェノタイピング手法が研究の中心である。

abstractwe present ClearDepthIAS, a high-throughput imaging and analysis platform that enables non-destructive, automated quantification of root architecture traits

Machine learning-assisted non-destructive prediction of root architecture in early-stage tomato rootstocks. →

The architecture of the root system is a primary factor in determining rootstock performance, affecting water and nutrient uptake, biomass accumulation, and overall vigor. However, direct root phenotyping is destructive, labor-intensive, and difficult to do routinely in breeding programs. The present study investigated early root morphological variation among developed interspecific tomato rootstock candidates (Solanum lycopersicum x S. habrochaites). The ability of linear regression and machine learning models to predict root traits from easily measured plant growth parameters was assessed. Nineteen interspecific hybrid rootstock candidates, two commercial rootstocks, and one scion were grown under optimal greenhouse conditions and evaluated at 0, 10, 20, and 30 days after planting. Root length, root surface area, root diameter, and root volume were determined by digital image analysis. In contrast, genotype, plant length, and stem diameter were used as input variables. Significant genotype x sampling date effects were observed for most morphological and biomass traits, indicating dynamic changes in root and shoot development during the first 30 days of growth. The rootstock candidates RSH-17 and RSH-6 generally showed relatively higher root length, surface area, root volume, and biomass accumulation than the commercial rootstocks and scion. XGBoost and OLR were the best predictive models, with R 2 values as high as 0.95 for root length, surface area, and volume. Root diameter was predicted less accurately than root length, surface area, and volume, suggesting that it might be a more independent or less variable root trait during early development. Overall, results suggest that vigor-related traits can serve as useful proxies for estimating major root architectural traits in early-stage tomato rootstock selection. Both XGBoost and OLR performed well, suggesting that root and shoot development were highly coordinated under optimal (non-stress) conditions. Hence, predictive modeling may help prioritize promising rootstock candidates before destructive root analysis. However, more validation under stress conditions and for longer periods of development is needed to determine the greater applicability of these models.

Why it matches plant phenotyping methods根系形態形質をデジタル画像解析で取得し、線形回帰・機械学習による非破壊予測モデルを評価・比較しており、植物フェノタイピング手法が研究の中心である。

abstractThe ability of linear regression and machine learning models to predict root traits from easily measured plant growth parameters was assessed.

DESENVOLVIMENTO DE APLICATIVO COM APRENDIZADO DE MÁQUINA PARA PREDIÇÃO DO IMPACTO DE RIZOBACTÉRIAS NO CRESCIMENTO DE PLANTAÇÕES DE ARROZ →

O uso de rizobactérias promotoras de crescimento de plantas (RPCPs) apresenta-se como alternativa sustentável para a agricultura, porém a predição de seus efeitos envolve múltiplas variáveis. Este trabalho teve como objetivo desenvolver um aplicativo móvel apoiado por aprendizado de máquina para análise preditiva do impacto de RPCPs no crescimento de arroz. A metodologia abrangeu quatro fases: levantamento de requisitos com especialista, análise exploratória de uma base de dados com 6.038 registros experimentais, desenvolvimento e avaliação de modelos de classificação e implementação do sistema. Foram comparados os algoritmos KNN, Random Forest e XGBoost, sendo este último selecionado por apresentar maior acurácia (0,945) e menor desvio padrão (0,010) na validação cruzada. A arquitetura Cliente-Servidor integrou um aplicativo Android em Kotlin com Jetpack Compose a uma API RESTful em FastAPI, operando em duas modalidades: não destrutiva, baseada em medições de campo, e destrutiva, com métricas de biomassa seca. Os resultados indicam que a ferramenta pode auxiliar a tomada de decisão ao reduzir a necessidade de coletas destrutivas em determinadas situações, contribuindo para práticas agrícolas mais sustentáveis.

Why it matches plant phenotyping methodsイネの生育影響という植物形質を、非破壊測定および乾物バイオマスから機械学習で予測するアプリケーションの開発・評価が研究の中心であり、単なる生育実験ではない。

abstractdesenvolver um aplicativo móvel apoiado por aprendizado de máquina para análise preditiva do impacto de RPCPs no crescimento de arroz

Integration of remote sensing and artificial intelligence for carbon quantification in macauba palm trees →

Accurate measurement of carbon is stored in plants is essential for evaluating strategies to reduce greenhouse gas emissions. The macauba palm (Acrocomia aculeata) is a native species of South America with high potential for oil production, ecological restoration, and carbon sequestration. Traditional methods to estimate carbon stocks, such as cutting down trees and performing laboratory analysis, are destructive, expensive, and impractical on a large scale. This study tested an alternative approach that combines UAV imagery, computer vision, and artificial intelligence to estimate carbon in macauba palms without damaging the plants. High-resolution aerial images were collected at four sites in Brazil, and a deep learning model (YOLOv8s-seg) was trained to automatically detect and measure palm crowns, achieving a mean Average Precision (mAP@50) of 0.956, precision of 0.946, and recall of 0.944. Plant height was estimated from canopy height models derived from photogrammetric processing of the UAV imagery. Crown diameter and height estimates showed strong correlations with field measurements (r = 0.81 and r = 0.82, respectively), with mean absolute errors of 0.26 m for diameter and 0.61 m for height. Both parameters were used in allometric equations to calculate carbon stock. Carbon estimates ranged from less than 1 ton per hectare in young plantations to more than 190 tonnes per hectare in older stands. These results demonstrate that artificial intelligence and UAV imagery provide a fast and scalable approach for estimating structural variables of macauba palms. However, carbon stock estimates remain sensitive to the allometric equations used and should be interpreted with caution, particularly at the individual level. Therefore, the proposed approach should be understood as a non-destructive monitoring framework rather than a fully generalized carbon prediction model, while still offering a valuable tool to support sustainable agriculture, carbon markets, and land-use policies.

Why it matches plant phenotyping methodsUAV画像、コンピュータビジョン、深層学習を用いてヤシの樹冠径・樹高を自動推定し、現地測定と比較検証している。植物の構造形質取得と炭素量推定の技術が研究の中心である。

abstractThis study tested an alternative approach that combines UAV imagery, computer vision, and artificial intelligence to estimate carbon in macauba palms without damaging the plants.

Estimation of Seaweed Biomass in Shallow Coastal Waters Using UAV Bathymetric LiDAR and Automated 3D Point Cloud Segmentation →

Accurate and wide-area estimation of seaweed biomass is essential for evaluating blue carbon. Conventional diver surveys and two-dimensional (2D) aerial imagery analysis face challenges such as intensive labor and biomass underestimation. While Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-LiDAR) provides dense 3D spatial data, classifying point clouds in extremely shallow coastal waters with dense kelp and artificial structures remains difficult. This study establishes a high-accuracy biomass estimation method using UAV-LiDAR and PointNet. A heuristic hybrid filtering approach combining physical constraints and local statistics was developed to automatically generate high-quality reference data. The trained PointNet successfully segmented complex point clouds into four classes with an overall accuracy of 94.2%. To calculate biomass, we introduced a volume correction model based on point cloud density (coverage) to mitigate overestimation caused by internal canopy gaps. This correction yielded estimated wet weights nearly identical to the in situ measurements (an approximate 3% difference), confirming highly accurate biomass reproduction. Furthermore, while the conventional 2D maximum likelihood method underestimated total biomass, our 3D point cloud analysis successfully quantified the dense, overlapping canopy. This framework significantly improves the efficiency and accuracy of blue carbon monitoring.

Why it matches plant phenotyping methodsUAV-LiDARとPointNetによる海藻の3D点群分割および biomass 推定手法を開発・検証しており、植物体のバイオマスという形質の取得が研究の中心である。

abstractThis study establishes a high-accuracy biomass estimation method using UAV-LiDAR and PointNet.

A Stage-Aware Cascaded Detection-Segmentation Framework for Leaf Phenotyping and Leaf Dry Biomass Estimation of Pepper Seedlings. →

Quantitative phenotyping of pepper seedlings is important for greenhouse plug tray seedling cultivation, but it remains constrained by inefficient manual monitoring, complex greenhouse backgrounds, and growth-stage-dependent discrepancies between two-dimensional image traits and actual leaf biomass. In this study, a cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping. The framework integrated Visual Dynamic Momentum YOLO (VDM-YOLO) for individual seedling localization and growth-stage recognition, Variance Guided Strip Ghost Gated UNet (VSG-UNet) for lightweight, high-resolution leaf segmentation, and a stage-aware correction model for leaf dry biomass estimation. In performance evaluation, VDM-YOLO achieved a mean average precision at an intersection over union threshold of 0.5 (mAP0.5) of 89.27%, improving mAP0.5 by 1.82 percentage points over YOLOv12. VSG-UNet achieved a mean intersection over union (mIoU) of 83.9% and a Dice coefficient of 81.8%, while reducing floating point operations (FLOPs) and parameters by 44.2% and 61.2%, respectively, compared with U-Net. After stage-aware calibration, the coefficient of determination (R2) between segmented area and leaf dry weight increased from 0.764 to 0.813, and the root mean square error (RMSE) decreased from 0.0210 g to 0.0190 g. These results demonstrated that the proposed framework provided a proof of concept approach based on RGB images for the nondestructive assessment of leaf area and leaf dry biomass in pepper seedlings under restricted experimental conditions.

Why it matches plant phenotyping methodsRGB画像による葉の検出・セグメンテーションと、葉面積から葉乾燥バイオマスを推定する手法を開発・評価しており、植物表現型取得が研究の中心である。

abstracta cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping.

Canopy Structure and Water Use Efficiency Variations Between Short- and Long-Day Strawberry Cultivars Revealed by Non-Destructive 3D Phenotyping →

Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R2 = 0.628; biomass: R2 = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g−1) than in ‘Favori’ (2.5 mg g−1). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits.

Why it matches plant phenotyping methods3D点群による非破壊フェノタイピングと連続重量計測を組み合わせ、植物形態・バイオマス・水利用形質を定量化し、測定精度も検証しているため、手法が研究の中心である。

abstractUsing 3D point-cloud phenotyping, this study compared the canopy structure and WUE

Editorial: Plant phenotyping for agriculture →

Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-

Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。

titleEditorial: Plant phenotyping for agriculture

Bridging Magnetic Field Agriculture and UAV-Based Precision Monitoring: A Systematic Review and Framework for Field-Scale Validation →

Magnetic field (MF) technologies have been applied in agriculture for decades. However, they have not achieved mainstream adoption, partly because no validated methodology exists for evaluating their effects under realistic field conditions. UAV-based multispectral sensing represents a potential pathway to address this limitation: by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, it could provide the monitoring infrastructure through which MF treatment responses are, for the first time, systematically evaluated and validated under open-field conditions. To exploit this complementarity, however, a common evidential ground must first be established, identifying which crop physiological variables are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream systematic review of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency/nitrogen assimilation, above-ground biomass, leaf area index, and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R² = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The results reveal a complete absence of integration between the two research domains despite their strong biological and methodological compatibility. The proposed framework provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems.

Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングによる作物生理形質の推定を体系的にレビューし、地上検証と機械学習を含むフィールドスケール評価フレームワークを提案しており、植物フェノタイピング手法が中心である。

abstractUAV-based multispectral sensing represents a potential pathway to address this limitation: by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale

Leaf movements as a quantitative metric for early stress detection →

Early, precise, and non-destructive stress detection is essential for maintaining crop productivity, particularly in high-density plant growth systems like controlled environment agriculture (CEA), where manual monitoring is often impractical. Using plant motion as a proxy for growth and plant health, we demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis in lettuce and five other CEA relevant crops. Leaf-movement dynamics under stress were imaged with a low-cost, scalable Raspberry Pi imaging setup and quantified using a repurposed open-source motion estimation algorithm; Tracking Rhythms in Plants (TRiP). Our system detected stress-induced changes in leaf-movement within 1 hour of stress, with the timing dependent on the nature of the stress. Sustained reductions in leaf-movement coincide with decreased biomass accumulation. This approach offers a non-invasive, rapid, scalable, and cost-effective solution for continuous crop monitoring, with potential for application in both terrestrial and space farming CEA systems. Abstract Figure Graphical abstract: Quantification of leaf-movement dynamics as a high-throughput proxy for plant physiological status, enabling early stress detection and timely intervention to mitigate yield penalties in CEA settings (image made with biorender.org).

Why it matches plant phenotyping methods低コスト撮像と既存アルゴリズムを用いて葉の動きを定量化し、植物ストレス・生理状態を早期推定する方法が研究の中心である。

abstractwe demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis

Coupled quantity–structure fluctuations of riparian vegetation under geomorphic constraints: the integration of field surveys and unmanned aerial vehicle mapping →

Introduction Riparian vegetation is critical to river stability, but quantity–structure risk under geomorphic constraints remains poorly quantified. Methods Using the Chishui River Basin as a case study, this study combined quarterly field surveys from 25 riparian transects with high-resolution unmanned aerial vehicle imagery to develop an integrated framework for identifying coupled fluctuations in vegetation quantity and community structure and their spatiotemporal risk patterns. Results A total of 263 plant species from 185 genera and 65 families were recorded. The results showed that riparian vegetation in the Chishui River exhibited pronounced spatial fluctuation at the basin scale. Biomass peaked upstream in autumn at 425 g m -2 and in the middle–lower reaches in summer at 522 g m -2 , but remained below 400 g m -2 downstream year-round. UAV-derived FVC mean and Texture std were significantly correlated with field-based vegetation-quantity and community-structure indicators, respectively (R 2 = 0.72, p 2 = 0.48, p RI values generally above 0.45, and maximum RI values reached 0.793 and 0.788 in two typical high- RI sections associated with observed human activity and possible engineering-related disturbance. Discussion This integrated framework provides a quantitative basis for cross-scale vegetation monitoring and ecological restoration prioritization in complex basins.

Why it matches plant phenotyping methodsUAV画像から植生量・群落構造を推定し、現地調査指標との相関で検証する統合的な植物状態測定フレームワークが研究の中心であるため。

abstractcombined quarterly field surveys from 25 riparian transects with high-resolution unmanned aerial vehicle imagery to develop an integrated framework for identifying coupled fluctuations in vegetation quantity and community structure

Stable Seasonal Trends in Satellite-Derived Vegetation Indices over Vineyards: Preliminary Results from Trinity Canyon, Armenia →

Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development.

Why it matches plant phenotyping methods衛星画像の植生指数を放物線フィッティングし、ブドウ園の植生活動・地上部バイオマス・構造発達を推定する分析手法が研究の中心であるため。

abstractthis study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices

Image-based Phenotyping and Machine Learning Prediction of Rice Genotypes to Combined Drought-Salinity Stresses →

Abstract Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in coastal areas due to insufficient water resources and inadequate irrigation infrastructure. Consequently, combined salinity-drought stress poses a major threat to rice production. To investigate the effects of combined salinity-drought stress, a two-season study was conducted utilizing soil media. The first season involved screening 58 rice genotypes, while the second season focused on validating the consistency of response in 20 selected tolerant and susceptible genotypes. These included established tolerant checks (Pokkali and Salumpikit) and susceptible checks (IR 29 and IR 20). Both drought and salinity treatments were given at an electrical conductivity (EC) of 10 dSm⁻¹ and 75% field capacity at the seedling stage. The experimental design was arranged in a modified lattice design in each season, with six blocks and three replications in the first season and two blocks and five replications in the second season. The data collected are leaf symptoms, biomass weight, and shoot length. A number of 330 images captured by a smartphone camera. Machine learning models were employed to predict drought-salinity tolerance criteria. The study revealed that XGBoost model achieved an accuracy of 90.62%. The study identified two genotypes, IR18A1925-SKI-0 and Inpari 30, that exhibited insignificance to Pokkali, based on assessment of shoot length, biomass, and leaf symptoms. These two genotypes were also consistently clustered with Salumpikit. These findings highlight potential of machine learning techniques in predicting rice tolerance to combined salinity-drought stress, with the XGBoost model demonstrating superior predictive capability in this study.

Why it matches plant phenotyping methodsスマートフォン画像から葉症状・バイオマス・草丈などの表現型を取得し、機械学習で複合ストレス耐性を予測する手法の適用が研究の中心である。

titleImage-based Phenotyping and Machine Learning Prediction of Rice Genotypes to Combined Drought-Salinity Stresses

Species responses to nutrient loading promote resistance but not temporal stability in floating macrophyte communities →

Determining the drivers of ecological stability amid accelerating global environmental change is a critical goal of contemporary ecology. Various candidate drivers have been suggested, with recent attention turning to response diversity—the variation among organism-environment responses. However, despite conceptual interest in response diversity as a driver of stability, there remain few field tests of this relationship. Using multi-species competitive communities of floating aquatic macrophytes as an experimental model for measuring temporal stability and response diversity to nutrient loading, we show that response diversity does not promote temporal stability of total macrophyte cover, but that communities with an uneven distribution of species responses were more resistant to an exogenous shock. To quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology. We measured response diversity as the balance of positive and negative biomass growth responses to dissolved nitrate concentration, weighted by species’ relative contributions to biomass, and tested its effect on temporal stability and resistance to an unexpected pulse disturbance (a large typhoon that disrupted our outdoor mesocosms). Response imbalance predicted typhoon resistance, but species asynchrony and mean population stability best predicted community stability, with no direct or indirect effect of species responses. Overall, our results provide new experimental evidence for how the structure of species responses promotes stability, and we aim our LeafMosaic workflow to empower future field experiments using floating macrophytes to study response diversity and ecological stability.

Why it matches plant phenotyping methods浮遊水生植物の写真時系列から種組成と成長動態を抽出する、オープンソースでスケーラブルな機械学習ワークフローを開発しており、植物表現型取得・解析法が中心的です。

abstractTo quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology.

Physics-informed machine learning and Vision Transformer for predicting photosynthetic traits, biomass, and grain yield in winter wheat using UAV multispectral imagery →

Predicting crop photosynthetic traits from UAV imagery requires frameworks that connect canopy-level spectral observations to leaf-level physiological processes. Existing approaches rely on empirical vegetation indices (VIs) and standard machine learning models, lacking physical interpretability and appropriate deep learning architectures for image data. We developed a physics-informed multi-output machine learning framework that combines PROSAIL radiative transfer model inversion-derived biophysical parameters with spectral VIs and texture features (TFs), applies two spatial deep learning architectures, a Vision Transformer (ViT) and a 2D convolutional neural network (CNN), to multispectral image patches, and introduces a hybrid architecture that fuses PROSAIL-derived features with ViT spatial embeddings. The framework was evaluated for predicting CO 2 assimilation rate ( A ), stomatal conductance ( g sw), Photosystem II efficiency ( F v’/ F m’), aboveground biomass (AGB), and grain yield in a subset of seven European winter wheat varieties selected from a larger 18-variety field experiment across two growing seasons (2022–2024). Model performance was evaluated using random hold-out tests and leave-one-variety-out (LOVO) validation with bootstrap confidence intervals. For grain yield, the best tabular models achieved R 2 = 0.92–0.96, and the ViT on image patches achieved a competitive R 2 = 0.92. ViT delivered the best performance in predicting g sw. BorutaSHAP selected PROSAIL-derived features alongside empirical VIs, confirming that physics-informed features provide complementary information. The hybrid ViT+PROSAIL model matched or outperformed ViT-only for most traits under LOVO validation, with the clearest gain observed for grain yield, indicating that physics-based features can help regularize spatial representations for improved cultivar-level transferability. This study demonstrates that integrating radiative transfer model physics with spatial deep learning advances UAV-based high-throughput phenotyping of photosynthetic traits in breeding programs.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から光合成形質、バイオマス、収量を推定する物理情報機械学習・画像解析フレームワークを開発し、複数の検証法で性能評価しており、植物表現型取得・推定法が中心である。

abstractWe developed a physics-informed multi-output machine learning framework that combines PROSAIL radiative transfer model inversion-derived biophysical parameters with spectral VIs and texture features (TFs), applies two spatial deep learning architectures, a Vision Transformer (ViT) and a 2D convolutional neural network (CNN), to multispectral image patches, and introduces a hybrid architecture that fuses PROSAIL-derived features with ViT spatial embeddings.

Non-destructive imaging-based estimation of fresh weight and quality of Stevia rebaudiana during the early growth stage in a plant factory →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methods非破壊画像によるステビアの生体重・品質推定が題名の中心であり、植物形質の取得・推定手法に該当する。

titleNon-destructive imaging-based estimation of fresh weight and quality of Stevia rebaudiana during the early growth stage in a plant factory

Individual plant-level (IPL) soybean biomass estimation and spatial mapping through UAV multisource feature-driven machine learning framework →

Abstract Rapid and accurate quantification of crop biomass using multisource UAV imagery–derived features, such as plant height, vegetation indices, and texture indices demonstrates strong potential for soybean high-throughput phenotyping. The indeterminate growth habit of soybean, alongside extensive nodulation and intense inter-plant competition, necessitates individual plant level (IPL) monitoring to quantify plant-specific nitrogen fixation and competitive vigor. However, most studies aggregate measurements at multi-plants at the plot level, thereby masking these soybean-specific traits. This study aims to develop and evaluate a UAV imagery-based framework for estimating soybean biomass at the IPL, with the objective of characterizing high-resolution spatial variability and supporting high-throughput phenotyping. Regions of interest (ROIs) for IPL data acquisition were defined as rectangular plots based on planting density and were generated from early-stage imagery before canopy overlap occurred. Using these ROIs, structural information (SIs) including plant height (PH) and vegetation fraction (VF)), vegetation indices (VIs), and texture indices (TIs) were derived for each individual plant from RGB and multispectral imagery and organized into sensor-specific feature groups. Recursive feature elimination was applied to select optimal features, which were then used as inputs for machine learning architectures including support vector regression (SVR) with a linear kernel, Random Forest (RF), and XGBoost (XGB). Among them, the SVR model using fused multisource features (SIs + VIs + Tis) achieved the best performance, with R² = 0.88, RMSE = 55.34 g, and rRMSE = 8.50% on an independent test dataset. The results show that: (1) tree-based models, including XGB and RF may suffer from overfitting due to limited sample size and feature redundancy, whereas linear SVR showed better generalization; (2) fusing RGB and multispectral features consistently improved biomass estimation accuracy. Inparticular, near-infrared and red-edge-based indices such as RECI and NDRE, along with VF and PH, were identified as important predictors, while texture indices were not selected as significant features; and (3) the proposed framework enabled spatially explicit IPL biomass mapping and time-series analysis, revealing variability in growth conditions and distinct growth trajectories. The framework provides a reliable solution for IPL soybean biomass estimation with practical potential for UAV-based agricultural decision-making.

Why it matches plant phenotyping methodsUAV画像由来の特徴量と機械学習により個体レベルのダイズ biomass を推定・評価する枠組みが研究の中心であり、植物表現型の取得・抽出手法に該当する。

abstractThis study aims to develop and evaluate a UAV imagery-based framework for estimating soybean biomass at the IPL

Construction of a Virtual Sensor-Driven Digital Twin System for Plant Growth Monitoring on Rooftop Farms →

Rooftop farms are urban green infrastructure integrating food production, ecological regulation, and public services, and their management increasingly relies on data-driven approaches. However, open built environments, microclimatic heterogeneity, and limited sensor deployment challenge continuous monitoring and short-term prediction of rooftop plant growth. This study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China. The system adopts a five-layer architecture comprising data acquisition, transmission, modeling, processing, and application service layers. By coupling a Long Short-Term Memory (LSTM) weather prediction model with the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth model, a predictive virtual sensor module was developed to forecast leaf area index (LAI), aboveground biomass, phenology, and yield for seven days. Results show that the system links environmental data acquisition, LSTM–DSSAT prediction, database storage, and three-dimensional visualization, transforming rooftop plant growth into an updatable, predictable, and visualized digital twin object. The coupled model showed high predictive accuracy, with R2 values of 0.9814 for LAI and 0.9966 for aboveground biomass, while supporting phenology and yield prediction. The system supports irrigation optimization, landscape management, and activity planning in sensor-constrained rooftop farms.

Why it matches plant phenotyping methods植物成長のLAI、地上部バイオマス、フェノロジー、収量を予測する仮想センサー・デジタルツインを開発し、精度検証しており、表現型推定手法が研究の中心である。

abstractThis study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China.

Non-destructive Spatial Reconstruction of Plant Leaf Starch Using Reduced-Band SWIR Spectroscopy and Chemometric Modeling →

1 Abstract Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900–1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R 2 ) of 0.771 ± 0.066 and a root mean squared error (RMSE) of 0.743 ± 0.098 mg g −1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.

Why it matches plant phenotyping methods植物葉のデンプン状態を非破壊推定・空間再構成するSWIR画像計測とケモメトリック解析ワークフローを開発・検証しており、フェノタイピング手法が中心です。

abstractHere, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves.

Physiology-informed LSTM framework integrating crop model and Sentinel-2 time series for rice nitrogen status estimation. →

Accurate assessment of plant dry matter (PDM) and plant N accumulation (PNA) provides essential indicators for precision nitrogen (N) management in rice production. However, purely data-driven models struggle to generalize due to the spatial scarcity of ground-truth physiological data. To address this, a physiology-informed long short-term memory (PI-LSTM) framework was developed for robust regional N diagnosis and variable-rate fertilization. First, the model was pretrained to internalize crop growth dynamics using a DSSAT-based simulation library, which spanned 2000 representative fields and 700 management scenarios to provide physiologically consistent pseudo-labels. Subsequently, the framework was fine-tuned using multi-year field observations (2020, 2023, 2024), Sentinel-2 time-series data, and meteorological inputs. The proposed LSTM framework outperformed conventional machine learning approaches in estimating PDM and PNA, achieving five-fold cross-validation R 2 values of 0.87 and 0.83, respectively. Based on these biophysical estimations, the N nutrition index (NNI) diagnosis achieved a 67.3% overall classification accuracy. Furthermore, by integrating the critical N dilution curve, the critical PNA and accumulated N deficiency (AND) were quantified, which served as the basis for developing the AND-based N recommendation algorithm (ANDA). Finally, variable-rate topdressing field experiments conducted across seven sites in 2024 and 2025 demonstrated that the ANDA reduced N input by 13.4% compared with farmers' practices, while maintaining or increasing yield and improving N partial factor productivity by 18.6%. This study provides a reliable, physically consistent decision-support framework for regional-scale precision N management.

Why it matches plant phenotyping methodsSentinel-2時系列とLSTMにより、イネの乾物量および窒素蓄積量という植物形質を推定する方法の開発・検証が研究の中心であり、施肥管理への応用も技術評価として記述されている。

abstracta physiology-informed long short-term memory (PI-LSTM) framework was developed for robust regional N diagnosis and variable-rate fertilization.

Diagnostic system for tebuthiuron soil ecotoxicity using morphophysiological indicators of Mucuna pruriens validated by Lactuca sativa. →

This study developed an integrated diagnostic system for tebuthiuron-induced soil ecotoxicity based on morphophysiological indicators of Mucuna pruriens, using the germination index (GI) of Lactuca sativa as a sensitive ecotoxicological validation endpoint. The experiment was conducted under greenhouse conditions using a completely randomized design with 12 treatments and 360 individual pots (independent samples evaluated via destructive sampling), which were distributed across five evaluation periods at 14, 28, 42, 56, and 70 days after sowing. Morphophysiological variables, including plant height, root length, shoot and root dry mass, chlorophyll content, nodule number, and visual phytotoxicity, were quantified and integrated with multivariate and probabilistic modeling approaches. Given the multifactorial nature of the germination index, Principal Component Analysis (PCA) was applied to identify ecological and physiological gradients associated with plant vigor, stress, and symbiotic functioning. The PCA outputs were subsequently used as inputs for Probabilistic Neural Networks (PNNs), enabling the classification and prediction of bioindicator-based ecotoxicological levels using mathematically defined low, medium, and high GI classes. Model performance was internally assessed using training and validation datasets, confusion matrices, overall accuracy, sensitivity, specificity, and ROC curves. Because no independent external dataset was available, the predictive performance should be interpreted as evidence of internal consistency rather than definitive generalizability across different soils, climates, herbicide doses, or field conditions. Multivariate analyses revealed that ecotoxicological attenuation trajectories in tebuthiuron-contaminated soils are inherently nonlinear, being structured by coordinated shifts in morphophysiological traits rather than isolated responses of individual variables. The integrated PCA-PNN framework demonstrated that aboveground traits. Particularly plant height, chlorophyll content, and shoot dry mass, were more sensitive indicators of tebuthiuron-induced stress than root traits alone. Higher GI values were associated with PCA regions characterized by increased shoot biomass, greater plant height, reduced phytotoxicity, and improved physiological performance, whereas lower GI classes corresponded to suppressed growth and multidimensional stress signatures. The progressive convergence between plant vigor and GI across evaluation periods suggests a gradual mitigation of ecotoxicological stress signals on the indicator plants, indicating transitions from acute injury to physiological adaptation states. These findings confirm that M. pruriens functions as an effective bioindicator for diagnosing soil ecotoxicological status and monitoring tebuthiuron-induced impacts. However, as tebuthiuron residues were not chemically quantified, these responses should not be interpreted as direct evidence of herbicide degradation, dissipation, or removal. These findings confirm that M. pruriens functions as an effective bioindicator for diagnosing soil ecotoxicological status and monitoring tebuthiuron-induced impacts. However, as tebuthiuron residues were not chemically quantified, the observed improvements should be interpreted as evidence of physiological adaptation and/or ecological attenuation rather than definitive proof of herbicide degradation or removal. Overall, this approach provides a robust framework for early detection of soil contamination and supports its application in monitoring and guiding soil rehabilitation processes, with potential for future validation under field conditions.

Why it matches plant phenotyping methods植物の形態・生理形質を統合し、PCA-PNNで植物ストレスおよび土壌生態毒性レベルを診断する手法の開発・内部検証が中心であり、単なる生物学的測定ではない。

abstractThis study developed an integrated diagnostic system for tebuthiuron-induced soil ecotoxicity based on morphophysiological indicators of Mucuna pruriens

Data Fusion of Sentinel-2 Spectral and Meteorological Data for Field-Scale Sugarcane Biomass Prediction in Humid Tropical Mexico Using Machine Learning →

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R2 values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce.

Why it matches plant phenotyping methodsSentinel-2と気象データの融合および機械学習により、サトウキビの茎・葉・総バイオマスという植物形質を推定する手法を開発・比較検証しており、フェノタイピング手法が中心である。

abstractThis study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions.

A Measurement-Driven Digital Twin Architecture for Plant-Level Biomass Estimation and Growth Forecasting in Hydroponic Systems →

Alternatives to soil-based horticulture, such as hydroponics, have been developed to respond to food distribution concerns for dense urban centers. A new system was developed to track an individual lettuce plant's growth in a hydroponic environment, utilizing streams of measured information and available models to continuously update the growth trajectory estimates for a plant. These "digital twin" models were integrated into an operating hydroponic greenhouse, with custom horticultural and sensor hardware to grow and measure relevant information. To aid in updating model parameters, plant yield was continuously measured with a custom neural network, using RGB-D images of the plants as an input. The network, trained on a collected dataset of 1300 images, was able to estimate mass within 1.5 g of the ground-truth value. After integration into the custom system, digital twin growth projections could approximate future yield between one and four days in the future, maintaining around a 2 g forecasting error.

Why it matches plant phenotyping methodsRGB-D画像から個体レタスの収量・質量を推定するニューラルネットワークと、センサー統合型の成長追跡基盤が研究の中心であり、植物形質の取得・予測手法を実質的に開発・検証している。

abstractA new system was developed to track an individual lettuce plant's growth in a hydroponic environment, utilizing streams of measured information and available models to continuously update the growth trajectory estimates for a plant.

Three-dimensional photogrammetric analysis of shoot growth dynamics and spatial competition in in vitro–grown kiwifruit explants →

Because conventional vegetative propagation methods for Kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson) often are constrained by their need for extensive plantation areas, high labour inputs, and intensive weed management. Therefore, in vitro micropropagation has emerged as an effective approach for the large-scale production of uniform, disease-free kiwifruit plant material. In the present study the shoot growth dynamics and spatial competition between explants for kiwifruit (cv. Hayward) in vitro-grown were investigated considering different explant densities (3, 5, and 7) and two subculture durations (30 and 45 days). Growth performance was assessed integrating traditional measurements (shoot viability, number and length, callus formation, fresh and dry biomass) with high-resolution three-dimensional photogrammetric reconstruction. Image acquisition was performed using a smartphone-based system (iPhone+viDoc RTK rover), and dense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis. Consistent correlations were observed between manually measured growth traits and smartphone-derived morphometric parameters at both 30 and 45 days of subculture. Specifically, point cloud–based estimates of surface area, height, and volume were significantly associated with shoot number, shoot length, and biomass accumulation, supporting the reliability of 3D photogrammetry as a non-destructive tool for phenotyping in vitro kiwifruit growth. The proposed approach demonstrates the potential of 3D photogrammetry to enhance the objectivity, resolution, and repeatability of growth assessment in in vitro culture systems, offering new insights into shoot development and density-dependent interactions. Graphical Abstract

Why it matches plant phenotyping methodsスマートフォン撮影とSfMによる3Dフォトグラメトリで、キウイフルーツ苗条の表面積・高さ・体積を非破壊推定し、手動測定およびバイオマスとの相関で信頼性を検証しており、表現型取得手法が中心である。

abstractdense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis.

Trait-Dependent Effects of Band Selection on Predicting Soybean Biomass, Leaf Area Index, and Canopy Cover from Hyperspectral Reflectance →

Predicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency. Hyperspectral reflectance provides detailed spectral information, but the role of band selection in regression-based trait prediction at the canopy scale remains unclear. In this study, we evaluated the effects of different band-selection algorithms on the prediction accuracy of aboveground biomass (AGB), leaf area index (LAI), and canopy cover (CC) in soybeans across multiple sites, years, cultivars, and irrigation treatments. We compared a full-band partial least squares regression (PLS) model with three band-selection methods (PLS-Variable Importance in Projection (VIP), Bootstrapped least absolute shrinkage and selection operator (LASSO) (BoLASSO), and an ensemble approach), and model performance was assessed using independent validation datasets. The results showed that the effectiveness of band selection depended on the target trait. Full-band PLS provided the highest accuracy for AGB, whereas BoLASSO achieved comparable accuracy to PLS for LAI and CC using a reduced number of selected bands. The selected wavelengths were located mainly in the visible, red-edge, and near-infrared regions. These results indicate that band-selection strategies should be tailored to the target trait and provide a basis for efficient band design in crop phenotyping.

Why it matches plant phenotyping methodsハイパースペクトル反射を用いた作物形質推定について、バンド選択アルゴリズムと回帰モデルを比較・独立検証しており、フェノタイピング手法の技術評価が中心である。

abstractPredicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency.

Synthesizing crop modelling and deep learning for remote estimation of wheat biomass dynamics from multispectral and weather observations →

Improving crop productivity while maintaining low environmental impact is essential for sustainable food production under increasing population pressure and irreversible climate changes. Dynamic biomass prediction is critical for effective crop growth monitoring and management, yet existing approaches struggle to provide consistent and reasonable predictions across diverse environments in a rapid, economic, and practical manner. Here, we propose SpecWeaNet, a biophysics-informed neural network framework that integrates explicit biophysical principles governing biomass accumulation with implicit mechanisms learned from representative training data. The framework enables dynamic prediction of wheat biomass from sowing to harvest using daily weather data and limited in-season spectral observations, without requiring model recalibration. From a systematic perspective, SpecWeaNet is designed as a flexible framework, from which we further developed three ready-to-use pre-trained variants with different input configurations tailored to commonly used sensors. Our comprehensive evaluation demonstrates the robustness and generalizability of pre-trained models for seasonal prediction of biomass dynamics from non-daily spectral observations (with random interval between two consecutive observations) and daily weather data, with coefficient of determination (R 2 ) higher than 0.99, relative mean absolute error (RMAE) within 26% and relative root mean square error (RRMSE) within 35% on more than 250,000 in-silico simulation scenarios across diverse environmental conditions, including different years, geographical locations, and crop varieties. Furthermore, validation on multiple field experiments showcases the capability of pre-trained models to provide reliable predictions at both trial (R 2 = 0.77–0.88, RMAE = 20–26%, RRMSE = 29–40%) and plot (R 2 = 0.83–0.93, RMAE = 12–19%, RRMSE = 15–26%) scales, utilizing daily weather observations and available satellite or drone-based imagery. This work demonstrates how integrating crop modelling with artificial intelligence can enable scalable estimation of crop biomass dynamics, advancing remote sensing–based crop phenotyping and monitoring for sustainable agricultural systems.

Why it matches plant phenotyping methodsSpecWeaNetは気象・スペクトル観測からコムギのバイオマス動態を推定する計算フェノタイピング手法であり、モデル開発、シミュレーション評価、複数圃場での検証が中心である。

abstractwe propose SpecWeaNet, a biophysics-informed neural network framework that integrates explicit biophysical principles governing biomass accumulation with implicit mechanisms learned from representative training data.

An early detection model of wheat stripe rust utilizing two-dimensional correlation spectroscopy for eliminate growth-related interference. →

Wheat stripe rust, caused by Puccinia striiformis f. sp. Tritici (Pst), represents a significant threat to global wheat production. Early detection, particularly during the asymptomatic phase, is critical for effective disease management. Hyperspectral sensing can detect subtle physiological alterations associated with initial infection; However, its effectiveness is frequently limited by substantial background interference from normal plant growth. In this study, the reliability of hyperspectral data obtained from early asymptomatic leaves was first validated using quantitative real-time polymerase chain reaction (qPCR). To mitigate background the interference, generalized two-dimensional correlation spectroscopy (2D-COS) was employed, utilizing infection time as the perturbation variable. This approach surpasses conventional dimensionality reduction techniques such as principal component analysis (PCA) and the chemometric feature selection algorithm known as competitive adaptive reweighted sampling (CARS). Through this methodology, six feature bands exhibiting distinct absorption changes were identified. Analysis of synchronous and asynchronous 2D-COS correlation features from 1 to 6 days post-inoculation (dpi), enabled effective discrimination between spectral variations attributable to growth and those specific to disease responses. The biological significance of these spectral dynamics was empirically validated using steady-state chlorophyll fluorescence imaging and destructive biomass measurements. This combined evidence confirmed that Pst-induced chloroplast functional impairment strictly precedes macroscopic tissue structural collapse. This process effectively suppressed background noise while preserving critical infection-related signals. Subsequently, three classifiers-support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)-were evaluated using the extracted 2D-COS features. Asynchronous features generally produced superior classification performance, with XGBoost achieving the highest accuracy (86.79%) and area under the receiver operating characteristic curve (AUC) (94.12%). Compared to conventional methods like PCA and CARS, 2D-COS more effectively attenuated growth-related interference and accentuated early disease signatures. These results demonstrate that the integrated framework of "multiplicative scatter correction (MSC) + Asynchronous Correlation Features + XGBoost" offers substantial potential for accurate, non-destructive, and early diagnosis of wheat stripe rust.

Why it matches plant phenotyping methods小麦赤さび病の無症状期を対象に、ハイパースペクトル計測と2D-COS・機械学習による植物病害状態の抽出手法を開発・評価しており、表現型取得が中心である。

abstractIn this study, the reliability of hyperspectral data obtained from early asymptomatic leaves was first validated using quantitative real-time polymerase chain reaction (qPCR).

MA-UQNet: A multi-modal uncertainty quantification neural network for remote sensing-based wheat aboveground biomass estimation →

Accurate aboveground biomass estimation with quantified uncertainty is essential for precision agriculture, enabling risk-aware decision-making and strategic model improvement. Existing approaches predominantly provide point estimates without uncertainty quantification, limiting their operational utility for trustworthy Artificial Intelligence (AI) deployment. This study presents a Multi-modal Attention-based Uncertainty Quantification Network (MA-UQNet), which achieves superior prediction accuracy (R 2 = 0.856) with well-calibrated uncertainty (97.18% coverage) for wheat aboveground biomass estimation through integrated multi-modal attention, growth stage-specific processing, and epistemic–aleatoric uncertainty decomposition. The framework integrates hyperspectral remote sensing with environmental variables via joint attention mechanisms that adapt to phenological variations. Model development employed a decade-spanning dataset (2012–2022, 1272 samples) collected under factorial combinations of nitrogen rates (0–270 kg/ha), irrigation levels (0–384 mm), and wheat cultivars across four growth stages. Temporal extrapolation validation using chronological partitioning (2012–2019 for training and 2020–2021 for testing) demonstrated robust generalization, substantially outperforming Random Forest (R 2 = 0.751, coverage = 76.61%) and nine representative baselines, including Bayesian Neural Networks (R 2 = 0.805, coverage = 38.31%). Uncertainty decomposition revealed epistemic uncertainty to be moderately dominant (53%) relative to aleatoric uncertainty (47%), indicating that strategic data collection offers greater potential for uncertainty reduction than improving measurement precision alone. These findings provide validated tools for uncertainty-aware biomass estimation in precision agriculture.

Why it matches plant phenotyping methods小麦の地上部バイオマスという植物形質を、ハイパースペクトルリモートセンシングと不確実性推定ネットワークで抽出する手法を開発し、時系列分割と既存手法との比較で検証しているため、植物フェノタイピング手法が中心である。

abstractThis study presents a Multi-modal Attention-based Uncertainty Quantification Network (MA-UQNet), which achieves superior prediction accuracy (R 2 = 0.856) with well-calibrated uncertainty (97.18% coverage) for wheat aboveground biomass estimation

Genomic selection for nitrogen use efficiency in perennial ryegrass ( Lolium perenne L.) under field conditions →

Abstract Background Improving the nitrogen use efficiency (NUE) of pastures has the benefit of reducing costs of production and reducing nitrogen loss to the environment. Genetic variation has been shown to exist for NUE, and hence NUE is a trait for breeding programs. Methods In this study, we develop genomic selection methods for NUE in perennial ryegrass through developing high‐throughput sensor‐based phenotyping and genotyping by sequencing (GBS) technologies. NUE of an advanced perennial ryegrass breeding population was screened in a spaced plant field trial which contained 644 genotypes, 3 nitrogen treatment levels (0, 20, and 40 kg ha −1 per application), and 3 replicates. The trial was conducted for 2 years with a total of 6 nitrogen applications. An unmanned aerial system (UAS) equipped with multispectral sensors was deployed weekly over the trial. Approximately 4–5 weeks after nitrogen fertilizer application, 75–675 selected samples were cut for ground truthing. Prediction models for biomass were developed based on spectral and ground truth data and biomass for each plant was computed. Plants were genotyped by GBS transcriptomics. Results NUE, defined as biomass production per unit of N application, varied significantly with N application level, season and among genotypes. Moderate broad‐sense heritability (0.61–0.72) for NUE was observed. Genomic prediction accuracies were in the range of 0.3–0.5. Conclusions Our results demonstrated that genomic selection for NUE was possible. The genomic prediction developed in these advanced breeding lines may be tested in other genetic backgrounds. The technologies are ready to be extended into other perennial pasture grass species.

Why it matches plant phenotyping methodsUASマルチスペクトルセンシングと予測モデルによるバイオマス推定を開発し、NUE表現型の高スループット取得に用いており、フェノタイピング手法が中心的である。

abstractwe develop genomic selection methods for NUE in perennial ryegrass through developing high‐throughput sensor‐based phenotyping

Image-based phenotyping of faba bean genetic resources for water deficit responses under controlled conditions →

Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-adapted germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, high-throughput plant phenotyping under stress conditions remain a major bottleneck in crop genetics and breeding programs. In this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions. Standardized, monitored stress conditions were achieved by watering-by-weighing for drought onset, duration, and intensities allowing genotype-level comparisons. The genotypes showed a range of stress responses in growth and physiology, including traits such as plant height, biomass, water use efficiency (WUE), and chlorophyll fluorescence parameters. Digital biomass, derived from combined top- and side-view plant imaging, was strongly correlated with biological biomass at the experimental endpoint, validating its use as a non-destructive proxy for growth assessment in faba bean. Time-resolved generalized additive modelling further revealed genotype-specific differences in the timing and magnitude of water deficit response. Genotypes that maintained growth and WUE under water deficit conditions may serve as valuable pre-breeding materials for development of drought-adapted faba bean.

Why it matches plant phenotyping methods多センサー表現型プラットフォームを用いた画像ベースのデジタル biomass 推定を検証し、植物形質評価への利用可能性を示しており、表現型取得法が中心的です。

abstractIn this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions.

RSCM: A Bayesian remote sensing-integrated crop model software framework for yield estimation →

RSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization. This approach automates estimation of leaf area index, aboveground dry matter, and grain yield without extensive ground-based calibration. The system couples a Python data interface with a high-performance C simulation engine, enabling efficient regional-scale processing. Validation using independent datasets for rice, wheat, and maize demonstrated robust performance: yield Model Efficiency reached 0.99, with a minimum ME of 0.67 for wheat. The Bayesian prior regularization constrained parameter estimates while maintaining predictive accuracy. Regional applications in South Korea, North Korea, and the U.S. Corn Belt captured spatial yield gradients and inter-annual variability across millions of pixels. RSCM provides a computationally efficient tool bridging process-based modeling and remote sensing for precision agriculture and food security monitoring.

Why it matches plant phenotyping methods衛星データと作物モデルを統合し、LAI・地上部乾物量・収量という植物形質を推定するソフトウェア手法を開発・検証しており、形質取得・推定法が研究の中心である。

abstractRSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization.

Integration of Aerial Photogrammetry, UAV LiDAR and Terrestrial LiDAR Point Clouds for Individual Tree Measurement and Individual Tree Carbon Storage Estimation →

Urban forest carbon sequestration is vital for environmental health, climate change mitigation, and enhancing the quality of life in urban areas. This paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation. The aerial photos, UAV-based LiDAR and terrestrial LiDAR were observed based on common ground control points and combined using Iterative Closest Point (ICP) algorithm. The combined point cloud was iltered to separate ground points and normalized based on Digital Terrain Model (DTM). The normalized point cloud was used for individual tree segmentation from which individual tree measurements such as, tree height, Diameter at Breast Height (DBH) and crown diameter were estimated. The estimated tree parameters were used for individual carbon estimation. The results show that the individual tree segmentation method signi icantly underestimated the number of trees. The estimation of DBH, tree height, and crown diameter achieved the Root Mean Square Error (RMSE) value of 0.107m, 1.385m and 2.650m respectively. However, in general the estimates experience underestimation as shown by Mean Bias Error (MBE) with 0.003m, -0.636m and 0.001m for DBH, tree height and crown diameter respectively. The estimated values for each individual tree were used for individual tree biomass and carbon storage recording the Root Mean Square Error (RMSE) at 1970.236 kg and 886.606 kgC respectively while attaining the Mean Bias Error (MBE) measure of 140.019 kg and 63.009 kgC each. The proposed framework showed promising results for individual tree carbon estimation. Nonetheless, further attention should be given on individual tree delineation process.

Why it matches plant phenotyping methods航空写真、UAV・地上LiDARを統合し、個体樹木の分離と樹高・DBH・樹冠径を推定して精度評価する手法が中心であり、植物形質計測の技術的検証に該当する。

abstractThis paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation.

Estimation of Ramie Key Phenotypic Traits Based on UAV Remote Sensing →

UAV-based phenotyping enables efficient high-throughput measurement of field crops. Phenotypic monitoring of ramie is critical for its cultivation management and variety breeding. However, ramie exhibits characteristics including multiple annual harvests, short growth cycles and rapid dynamic growth change, all of which increase the difficulty of growth monitoring and yield estimation. This study aims to utilize UAV-based multispectral remote sensing to estimate ramie plant height (PH), leaf area index (LAI), and above-ground biomass (AGB) over multiple time series, and to assess the influence of seasonal effects and different data processing strategies on the accuracy of ramie digital phenotyping. Over three ramie growth cycles, a total of 15 UAV flights were conducted over an experimental field consisting of 72 plots. The structure from motion (SfM) algorithm was applied to estimate PH. Remote sensing features derived from UAV imagery were used with background segmentation and machine learning to estimate LAI. The AGB was estimated by combining remote sensing-derived PH, LAI, and climate data. The results showed that the estimated and measured phenotypes were highly correlated, with optimal coefficients of determination of 0.961 for PH and 0.873 for LAI. Background segmentation improved LAI accuracy. Integrating climate data, remote sensing-derived PH and LAI significantly enhanced the accuracy of AGB estimation. In conclusion, this study provides a feasible method for extracting ramie phenotypes from UAV remote sensing imagery, providing methodological support for large-scale management of the crop industry and intelligent, precise monitoring of crop growth.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像、SfM、背景分割、機械学習を用いてラムーの草丈・LAI・地上部バイオマスを推定し、精度評価まで行う手法研究であり、表現型取得・抽出が中心である。

abstractThis study aims to utilize UAV-based multispectral remote sensing to estimate ramie plant height (PH), leaf area index (LAI), and above-ground biomass (AGB) over multiple time series, and to assess the influence of seasonal effects and different data processing strategies on the accuracy of ramie digital phenotyping.

Validation and application of high-throughput 3D multispectral phenotyping platform for evaluating seasonal adaptation in Chinese cabbage →

Climate change poses increasing challenges to Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production through unpredictable weather patterns that induce premature bolting and physiological disorders. Traditional breeding programs rely on labor-intensive visual assessment that cannot capture continuous developmental dynamics or precisely quantify stress responses across variable environments. This study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes across contrasting autumn (favorable) and spring (stressful) seasons in Taiwan. The system, based on a FieldScan gantry platform equipped with multispectral 3D scanners, operated autonomously 2-3 times daily, continuously monitoring morphological parameters (3D leaf area, digital biomass, plant height) and spectral indices (NDVI, PSRI) throughout the growth cycle. The system’s automated components -continuous data acquisition and real-time parameter extraction – generated approximately 100,000 data points from 63 morphological, spectral, and structural parameters during 6-week pre-harvest period. Subsequent quality and statistical analysis enabled objective genotype classification and breeding decisions. Automated measurements showed season-dependent associations with visual assessment scores (R² = 0.37-0.56 in autumn; R² = 0.73-0.80 in spring), with spring models substantially outperforming autumn models due to enhanced physiological differentiation under stress. Spring cultivation induced severe stress responses, evidenced by 71% increase in PSRI (0.12 vs. 0.07) and 26% increase in plant height, with bolting resistance emerging as the critical determinant of adaptation. A quantile-based multi-dimensional classification framework integrating seasonal composite scores and Euclidean distances stratified germplasm into actionable breeding categories: stable genotypes (3.7%), spring-specific types (0.7%), poor performers (13.4%), and intermediate materials (82.1%). Continuous temporal monitoring enabled early stress detection, with binned PSRI measurements predicting subsequent morphological development one week in advance (R² = 0.62). This integrated phenotyping framework provides efficient tools for accelerating climate-resilient breeding through objective genotype classification, early stress detection, and data-driven decision support, with potential adaptation to other vegetable crops and integration with IoT-based collaborative breeding network.

Why it matches plant phenotyping methods高スループット3D・マルチスペクトル表現型計測プラットフォームの検証と応用が研究の中心であり、形態・スペクトル形質の自動取得、抽出、予測性能、遺伝子型分類を評価している。

abstractThis study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes

LiDAR-Guided Semantic 3D Gaussian Splatting for Forest Digital Twins →

Forest digital twins play a crucial role in modern precision forestry by supporting biomass estimation and carbon cycle monitoring. However, existing 3D reconstruction methods struggle to simultaneously achieve metric-level structural accuracy and visual realism in complex understory environments. This study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery. First, a multi-modal fusion mechanism is constructed to extract geometric anchors from registered LiDAR data for precise 3DGS spatial initialization, which mitigates rendering artifacts and geometric drift caused by poor initialization in purely visual methods. Second, a semantic regularization optimization strategy is proposed to realize differentiated modeling of tree trunks and canopies, effectively balancing the structural accuracy of rigid trunks and the photorealistic rendering of non-rigid canopies. Experiments conducted on three study plots demonstrate that the proposed approach achieves an average PSNR of 24.94 dB, SSIM of 0.773, and LPIPS of 0.231 across all plots, outperforming standard NeRF and baseline 3DGS, while enabling DBH estimation with R2 = 0.848 and RMSE = 2.705 cm. This method provides a solution for high-fidelity forest digital twin construction in open-canopy forest environments such as urban and campus forests.

Why it matches plant phenotyping methodsLiDAR・UAV画像を統合した3D再構成法を開発し、樹幹・樹冠の構造モデル化とDBH推定を評価しており、植物形質取得が中心的な技術貢献である。

abstractThis study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery.

Explainable machine learning to predict root biomass of field crops using UAV multispectral data →

Understanding below-ground biomass dynamics is essential for improving crop performance in water-limited regions. Yet field-scale root monitoring remains constrained by destructive and labor-intensive sampling. This study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits. Across 405 samples collected during the 2024 growing season, eight algorithms were evaluated, among which Random Forest and XGBoost achieved the highest predictive accuracy (R² = 0.763 for millet, 0.688 for maize, and 0.659 for sorghum). SHAP analysis revealed that leaf area was the dominant predictor across all crops, with 2-3 times greater influence than other traits, while leaf water content and chlorophyll-related parameters exhibited species-specific effects associated with drought adaptation. Under the conditions tested, these results suggest that UAV-based multispectral phenotyping, combined with interpretable machine learning, can enable non-destructive estimation of root biomass at the field scale. Within the limits of this single-site, single-season study, the approach demonstrates potential for large-scale root phenotyping and for supporting crop improvement in semi-arid regions. We quantify a 15-25% reduction in R² relative to above-ground trait prediction, which we term the 'cost of indirect inference'-highlighting the inherent challenge of estimating below-ground biomass from canopy-level data. These findings offer insights for precision agriculture, subject to broader validation.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と説明可能な機械学習を用いて根 biomass という植物形質を非破壊推定する手法が研究の中心であり、実証・比較評価も行っている。

abstractThis study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits.

Total Fresh Weight Estimation Model for Herbaceous Plant Based on Improved YOLOv5 and 3D Point Clouds →

The real-time quantitative estimation of herbaceous plant growth status holds significant potential for investigating fertilization effects, predicting growth curves, and enhancing crop yield. This study constructed a growth quantification model using an improved YOLOv5 architecture integrated with 3D point cloud processing, with pak choi as an exemplar crop. To improve the recognition accuracy while reducing the number of parameters, we employed a lightweight YOLOv5 model enhanced with Atrous Spatial Pyramid Pooling and Ghost convolution modules for individual pak choi plant localization and growth stage classification. We also developed a segmentation method based on the HSV color space to segment leaves. To estimate the total fresh weight of individual plants, we first calculated the leaf surface area by generating a triangular mesh from the corresponding leaf point clouds and predicted the chlorophyll content using a stacking ensemble model. Subsequently, to address the leaf occlusion issues, the leaf pixel ratio in the images, leaf surface area, and mean leaf chlorophyll content were collectively used as independent variables. Finally, a multiple linear regression model was developed to accurately estimate the total fresh weight of individual pak choi plants. Experimental results demonstrate that the modified YOLOv5 architecture achieves a 3.5% improvement in mAP@0.5 (reaching 96%) and a 4.66% increase in F1-score (attaining 90.26%), while significantly reducing the computational complexity compared to the baseline model. Statistical tests verified that the fitted equation could explain 79% of the variation in the total fresh weight, with an average relative error of 12.16%. This enables non-contact and accurate measurement of the pak choi growth status.

Why it matches plant phenotyping methodsYOLOv5、3D点群、葉面積・クロロフィル推定を統合し、個体の生体重という植物形質を非接触推定する手法が研究の中心である。

abstractThe real-time quantitative estimation of herbaceous plant growth status holds significant potential

Phenomic prediction in drought-stressed faba bean across spectral, structural, and fused canopy predictors →

Abstract Background Faba bean is an important grain legume in temperate cropping systems because it provides protein-rich seed and contributes biological nitrogen fixation. However, its productivity is highly sensitive to drought, and breeding for improved drought performance is constrained by complex genotype by environment interactions and the difficulty of measuring relevant traits at scale. This study evaluated whether scanner-derived vegetation indices (VI), 3D canopy traits, and their combination can predict key agronomic and physiological traits in drought-stressed faba bean, and how predictive ability changes when information is used from single dates or cumulatively across the season. Results Predictive performance was strongly trait dependent and varied with predictor set and temporal strategy. Combined VI + 3D predictors generally produced the highest and most consistent predictive ability for major traits. Total grain yield reached 0.75 under cumulative VI + 3D prediction at 93 days after sowing (DAS 93), cumulative water uptake peaked at 0.80 at DAS 97, and total straw biomass reached 0.66 at DAS 104. In contrast, some component traits were predicted equally well or better by 3D information alone, including grain number with 0.70 and pod number with 0.55 under cumulative 3D prediction. Useful prediction windows also differed among traits, with broad late-season windows for major agronomic traits but narrower, more stage-specific windows for productive tillers, thousand kernel weight, and water-use efficiency. Conclusion Phenomic prediction under drought in faba bean was strongly shaped by trait type, predictor composition, and temporal design. Combined VI + 3D predictors were most effective for integrative traits, whereas several component traits were predicted equally well or better by 3D information alone. These findings highlight the potential of scanner-based multisensor phenotyping to support drought-related selection in faba bean breeding.

Why it matches plant phenotyping methodsスキャナー由来のスペクトル指標と3Dキャノピー形質を用いたマルチセンサー表現型解析・予測が研究の中心であり、乾燥ストレス下の収量、バイオマス、水利用などの植物形質を技術的に評価している。

abstractThis study evaluated whether scanner-derived vegetation indices (VI), 3D canopy traits, and their combination can predict key agronomic and physiological traits in drought-stressed faba bean

Image-based phenotyping of faba bean genetic resources for water deficit responses under controlled conditions →

Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-resilient germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, high-throughput plant phenotyping under stress conditions remain a major bottleneck in crop genetics and breeding programs. In this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions. Standardized, monitored stress conditions were achieved by watering-by-weighing for drought onset, duration, and intensities allowing genotype-level comparisons. The genotypes showed a range of stress responses in growth and physiology, including traits such as plant height, biomass, water use efficiency (WUE), and chlorophyll fluorescence parameters. Digital biomass, derived from combined top- and side-view plant imaging, was strongly correlated with biological biomass at the experimental endpoint, validating its use as a non-destructive proxy for growth assessment in faba bean. Time-resolved generalized additive modelling further revealed genotype-specific differences in the timing and magnitude of water deficit response. Genotypes that maintained growth and WUE under water deficit conditions may serve as valuable pre-breeding materials for development of drought-adapted faba bean.

Why it matches plant phenotyping methods多センサー表現型プラットフォームを用いた画像由来バイオマスの抽出と生物量との検証が研究の中心であり、表現型取得・検証に該当する。

abstractIn this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions.

Plant and substrate-based indices for salinity monitoring in Cestrum nocturnum →

This study proposes salinity indices based on plant and substrate measurements to define reference thresholds for salinity management in potted crops, using Cestrum nocturnum as a model species. A greenhouse experiment was conducted with plants grown in containers and irrigated with nutrient solutions at three electrical conductivity (EC) levels (2.0, 4.5, and 7.0 dS m - ¹). Plant responses were assessed through vegetative growth, visual quality, flowering intensity, continuous stem diameter variation (maximum daily stem shrinkage, MDS), cumulative evapotranspiration (ETa), and substrate bulk EC monitored with sensors. Increasing salinity reduced vegetative growth, particularly shoot biomass, while enhancing flowering intensity at 4.5 dS m - ¹, indicating a shift from vegetative to reproductive development. The moving average of MDS (avgMDS) responded to salinity, showing both increases and decreases depending on stress intensity, and, when expressed as signal intensity (SI: control/salinity), discriminated between stress levels, establishing alert (1.10) and critical (1.38) thresholds. Salinity decreased ETa by 35% and 65% at 4.5 and 7.0 dS m - ¹, respectively, and ETa-based SI defined alert (1.20) and critical (1.55) thresholds. The hourly moving average of bulk EC (avgECb) enabled continuous assessment of salinity dynamics, minimizing the influence of substrate moisture variability. The use of avgMDS, ETa, and avgECb enables the detection and interpretation of salinity stress by integrating plant physiological responses with substrate conditions, while the combined use of two or more indices improves the robustness of the assessment, providing a quantitative framework for salinity management in potted crops.

Why it matches plant phenotyping methods植物の生理応答とセンサー計測から塩ストレスを定量検出する指標を開発し、警戒・臨界閾値を設定して技術的に評価しているため、単なる生育測定ではない。

abstractThis study proposes salinity indices based on plant and substrate measurements to define reference thresholds for salinity management in potted crops

Remote Sensing Estimation of Plant Diversity in Sandy Ecosystem Based on Sentinel-2 Data →

Plant diversity is a key indicator of ecosystem structure, function, and restoration status, yet its rapid assessment remains challenging in sandy ecosystems where vegetation is sparse, spatially heterogeneous, and strongly affected by exposed soil backgrounds. In such environments, conventional greenness-based spectral indices may not adequately capture species-level variation because plant communities are controlled not only by photosynthetic biomass but also by soil moisture, micro-topography, and dune-related habitat heterogeneity. This study evaluated the potential of Sentinel-2-derived spectral indices for estimating plant α-diversity in the Hunshandak Sandland, northern China. Based on field observations from 888 plots collected during 2017–2024, four α-diversity metrics—species richness, Shannon–Wiener index, Simpson index, and Pielou evenness index—were calculated and compared with 21 spectral indices using correlation analysis, partial least squares regression (PLSR), and random forest (RF) models. The results showed that model performance varied substantially among diversity metrics. Species richness was estimated with the highest accuracy, whereas Shannon–Wiener, Simpson, and Pielou indices showed weaker predictability, indicating that remotely sensed spectral indices were more sensitive to species number than to abundance distribution and evenness. Moisture- and soil-background-sensitive indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BSI/BRI), and Chlorophyll Absorption Ratio Index (CARI), showed relatively stable relationships with plant diversity across different vegetation gradients. Although the overall explanatory power was moderate rather than high, the results demonstrate the practical value of Sentinel-2 spectral indices for regional screening of plant diversity patterns in sandy ecosystems. This study provides empirical evidence for biodiversity monitoring and ecological restoration assessment in semi-arid sandy landscapes and highlights the need to integrate environmental covariates, multi-source remote sensing, and phenological information in future studies.

Why it matches plant phenotyping methodsSentinel-2のスペクトル指標と回帰・機械学習により、圃場プロットの植物α多様性を推定・検証する測定ワークフローが研究の中心であり、単なる生物学的実験のルーチン測定ではない。

abstractThis study evaluated the potential of Sentinel-2-derived spectral indices for estimating plant α-diversity in the Hunshandak Sandland, northern China.

Macronutrient deficiency reduces growth and influences vegetation indices of greenhouse grown ornamental and vegetable plants as measured by the TraitFinder digital phenotyping system. →

Introduction: Recent technological advances in high resolution image capture and analysis have led to increased adoption of high-throughput digital phenotyping in plant science research. High-throughput digital phenotyping provides a nondestructive method to quantify changes in plant growth and health in response to environmental factors or developmental cues. Moreover, it allows researchers to conduct large experiments in a time- and cost-efficient manner. The TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information. Leaf reflectance is presented as five vegetation indices (e.g., normalized difference vegetation index). Methods: This project evaluated nitrogen (N), phosphorus (P), and potassium (K) deficiency in greenhouse grown ornamental and vegetable plants using the TraitFinder. Plant species included celosia, coleus, marigold, petunia, and tomato. Plants were fertilized with a complete Hoagland's solution (control), and three modified solutions: Hoagland's solution without nitrogen (-N), phosphorus (-P), or potassium (-K). Each plant species was evaluated separately with eight replicate plants per treatment, organized as a randomized complete block design. Results: Treatment with -N, -P, and -K solutions resulted in reduced vegetative growth and decreased concentration of the corresponding macronutrient in leaf tissue for all species evaluated. We observed that the presence of flowers would negatively affect calculations of the vegetation indices due to their distinct spectral properties; therefore, flowers must be excluded to accurately quantify plant health parameters. In general, we observed a common trend where GLI (green leaf index) and NDVI (normalized difference vegetation index) decreased, and NPCI (normalized pigment chlorophyll index) and PSRI (plant senescence reflectance index) increased in response to macronutrient deficiency. The measure of GLI, NDVI, NPCI, and PSRI were different from the control plants, but these observations were dependent on the nutrient deficiency and species tested. Discussion: Our results underscore the importance of accounting for species-specific spectral signatures when assessing plant responses to nutrient deficiencies. This project also provides reference values for interpreting vegetation indices, offering valuable guidance for scientists implementing digital phenotyping in their experimental protocols. Digital phenotyping can significantly improve experimental throughput and provide quantitative insights into plant health.

Why it matches plant phenotyping methodsTraitFinderによる形態・スペクトル形質の取得と、花の除外や種特異的スペクトルへの対応を含むデジタルフェノタイピングの実質的な適用・評価が中心である。

abstractThe TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information.

Rapid Forest Fuel Load Estimation via Virtual Remote Sensing and Metric-Scale Feed-Forward 3D Reconstruction →

Accurate quantification of forest coverage and combustible biomass (fuel load) is critical for wildfire risk assessment and ecosystem management. However, traditional methods relying on airborne LiDAR or field surveys are cost-prohibitive and time-intensive, while satellite imagery often lacks the vertical resolution required for canopy volume analysis. This paper proposes a novel, automated pipeline for rapid forest inventory using virtual remote sensing data derived from Google Earth Studio (GES). Our approach first generates low-altitude orbital imagery and camera poses for a target region. For dense 3D reconstruction, we employ Pi-Long, developed within the VGGT-Long framework. This model serves as a scalable extension of the Pi-3 feed-forward Transformer architecture. To address the inherent scale ambiguity in monocular reconstruction, we introduce a metric recovery module that aligns the reconstructed trajectory with GES ground truth poses via Sim(3) Umeyama optimization. The metric-scale point cloud is then orthogonally projected into Bird's-Eye-View (BEV) height and density maps. Finally, we employ a watershed-based segmentation algorithm combined with height variance analysis to classify tree species (conifer vs. broadleaf), calculate Leaf Area Index (LAI), and estimate total fuel load. Experimental results demonstrate that this pipeline offers a scalable, cost-effective alternative to physical scanning, enabling near-real-time estimation of forest biomass with high geometric consistency.

Why it matches plant phenotyping methods森林の3D再構成、BEVマップ、分割・高さ分散解析を組み合わせ、LAIと燃料量という植物・群落形質を推定するパイプライン自体が中心的な技術貢献であるため。

abstractThis paper proposes a novel, automated pipeline for rapid forest inventory using virtual remote sensing data derived from Google Earth Studio (GES).

Genetic Variation for Autumn-Winter Forage Yield in a Segregating Tetraploid F 1 Population of Paspalum notatum . →

Autumn-winter forage scarcity limits subtropical livestock systems. This study aimed to: (1) develop a segregating F 1 population from parents contrasting in autumn-winter biomass yield (WBY) in tetraploid Paspalum notatum ; (2) estimate phenotypic and genetic variability for WBY across environments; and (3) evaluate the relationship between WBY and spring-summer biomass yield (SBY), and the feasibility of unmanned aerial vehicle (UAV)-derived vegetation indices as non-destructive estimators of WBY. A population of 182 tetraploid F 1 hybrids was evaluated at two sites in Corrientes Province, Argentina (2022-2024). WBY exhibited wide genotypic variability across locations and years ( p H 2 ) ranged from 0.41 to 0.64, reflecting sensitivity to the thermal and moisture conditions of each environment. WBY showed a positive, moderate association with SBY ( R 2 = 0.20-0.26), indicating that selection for cool-season yield does not compromise summer productivity. The Normalized Difference Red Edge Index (NDRE) was the most robust WBY predictor ( R 2 up to 0.67 at MES-2022 vs. 0.58-0.59 for ARVI, GNDVI and NDVI at the same site-year), though predictive accuracy varied with environmental conditions. The results demonstrate substantial and exploitable genetic variation for cool-season forage yield in P. notatum .

Why it matches plant phenotyping methodsUAV由来の植生指数を用いた非破壊的な飼料収量推定を評価し、複数指数の予測性能を比較しているため、植物形質取得法の実質的な適用・検証を含む。

abstractevaluate the relationship between WBY and spring-summer biomass yield (SBY), and the feasibility of unmanned aerial vehicle (UAV)-derived vegetation indices as non-destructive estimators of WBY.

From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series. →

Product demand and climate variability are progressively increasing the need for real-time, scalable crop monitoring to support varietal selection and in-season input optimisation. However, producers still have limited information on the temporal and spatial variability of cotton health and performance beyond point-scale field surveying. In addition, given cotton's high phenotypic plasticity, near real-time derived metrics are essential to improve input efficiency and strengthen long-term sustainability of the cotton industry in Australia. Therefore, we proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m). Scalability was validated by applying the proposed framework to estimate and map cotton yield across commercial fields. To do this, we explored the accuracy of high-resolution multispectral imagery from two platforms (unmanned aerial vehicle (UAV) and PlanetScope (PS)) collected across two 144-plot trials for two cotton seasons. These were designed with a large range in nitrogen rates (N), shading, and two growth-regulator doses, thus, creating variable environments. Sensing metrics were obtained from UAV imagery (1.3-1.6 cm pixel size) acquired once in 2022/23 and eight times in 2023/24, while PS composites (3 m pixel size) provided near-daily coverage in both seasons. Time-series gaps were imputed using Savitzky-Golay smoothing in thermal time (GDD), enabling extraction of growth dynamic metrics (GDMs) as single-date (SD; e.g., peak canopy) and multi-date (MD; e.g., daily average growth rate) metrics. After reducing collinearity and dimensionality, random forest (RF), support vector regression (SVR), and gaussian process regression (GPR) were trained and interpreted with SHAP, for feature contribution. UAV single-date models (SD_ UAV) achieved strong accuracy for height (R 2 = 0.77), biomass (R 2 = 0.73), and yield (R 2 = 0.81). Incorporating UAV time-series metrics (MD_UAV) improved the performance R 2 = 0.87, 0.86, and 0.85 for height, biomass and yield, respectively. Application of the derived models using high resolution satellite data (MD_PS) for different farming systems showed highly significant accuracy (R 2 = 0.67) to predict cotton yield at aggregated field scale. As such, enabling the detailed spatial prediction of cotton yield within a field. It is anticipated that the proposed functional sensing framework will improve the estimation of key cotton production traits, supporting field- and within-field decision-making, ultimately contributing to more resilient and sustainable cotton production in Australia.

Why it matches plant phenotyping methodsUAV・衛星時系列画像から綿花の形態・生産性形質を推定するセンシング／予測フレームワークを開発・検証し、商業圃場へ適用しており、表現型取得手法が中心である。

abstractwe proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m).

Real‐time monitoring of root dielectric properties for assessing crop plant damage caused by foliar application of glyphosate →

Abstract Premise There is a knowledge gap regarding how foliar injury and restricted water uptake can be detected by measuring root dielectric response. This pot study nondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying. Methods Root dielectric properties were recorded on a minute scale in control and glyphosate‐treated maize, cucumber, and pea. Chlorophyll, stomatal conductance, and biomass measurements were taken to interpret the dielectric changes. Results Electrical capacitance and conductance varied diurnally due to the circadian regulation of water uptake and hydraulic conductance. Glyphosate application reduced capacitance, indicating the impeded root growth and activity caused by impaired amino acid synthesis, foliar damage, and restricted transpiration. The dissipation factor decreased in response to glyphosate due to impeded apoplastic water flow, suppressed root lignification, and hampered water absorption. The enhanced leaf and root hydraulic resistance caused by glyphosate was manifested in sharply reduced electrical conductance. Changes in the species’ dielectric response were consistent with physiological symptoms and biomass loss. Discussion Real‐time dielectric measurement proved suitable for the nondestructive monitoring of plant responses to foliar stress through altered root traits. This method could be employed to evaluate herbicide tolerance in crops and to develop and determine dosage of herbicide ingredients.

Why it matches plant phenotyping methods植物の根の誘電特性をリアルタイム・非破壊で測定し、ストレス応答や根形質を評価する方法が研究の中心であるため。

abstractnondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying

LiDAR and UAV Photogrammetry for Three-Dimensional Canopy Reconstruction: A Comparative Study for Precision Agriculture Under Mediterranean Conditions →

This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture. Experiments were conducted in Sicily (Italy) on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV data were used to generate canopy models and estimate canopy height, volume, and vegetation density. A voxel-based approach was applied to LiDAR point clouds to analyze internal canopy structure. LiDAR significantly outperformed UAV photogrammetry, achieving lower errors in canopy height estimation (RMSE = 0.19–0.21 m vs. 0.52–0.60 m) and canopy volume (3.5–4.2% vs. 13.7–16.1%). UAV photogrammetry provided reliable estimates of canopy surface but underestimated structural parameters in dense vegetation due to occlusion effects. Differences were more pronounced in Ficus macrophylla than in Moringa oleifera, highlighting the influence of canopy complexity. These findings demonstrate that LiDAR-derived structural metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, and canopy management in Mediterranean cropping systems.

Why it matches plant phenotyping methodsLiDARとUAVフォトグラメトリによる樹冠の3次元再構成と、樹冠高・体積・密度推定を比較検証しており、植物形質取得手法が研究の中心です。

abstractThis study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture.

Monitoring Carbon Stock Change at the Individual-Plant Scale: A Methodological Review and Integrative Framework →

With increasing demand for fine-scale ecological management under carbon neutrality frameworks, multi-temporal assessment of carbon stock change (ΔC) at the individual-plant scale has become essential for understanding plant-level carbon dynamics and supporting management decisions. However, methodologies for repeated monitoring at this scale remain fragmented, showing limited cross-temporal comparability, weak cross-scale consistency, and insufficient integration across methods. Existing approaches can be grouped into three pathways: (i) process-based methods derived from CO2 exchange measurements, (ii) state-based approaches estimating biomass and ΔC, and (iii) sensing-based approaches using structural, spectral, thermal, and fluorescence signals. These approaches offer complementary strengths, yet none simultaneously achieve high accuracy, temporal continuity, and operational scalability for multi-temporal ΔC estimation. Among these, stock-based and structural approaches form the primary estimation pathways, while flux-based and functional sensing methods provide complementary constraints. This review synthesizes and compares these approaches in terms of their theoretical basis, spatial support, temporal characteristics, and uncertainty structures. To address the lack of methodological integration, we propose a structure–function–scale framework that links heterogeneous observations across spatial and temporal domains and emphasizes cross-scale consistency as a prerequisite for reliable ΔC estimation. Within this framework, we further examine how multi-source integration can connect structural and functional observations through segmentation, co-registration, scaling, temporal alignment, and uncertainty propagation. By integrating traditional measurement logic with emerging remote sensing technologies, this review provides a unified methodological framework for ΔC estimation and identifies key directions for advancing fine-scale carbon monitoring, spatiotemporally consistent data fusion, uncertainty-aware inference, and MRV-oriented verification systems.

Why it matches plant phenotyping methods個体植物スケールの炭素蓄積変化を推定するための測定・センシング・統合手法を体系的に比較し、セグメンテーションや不確実性伝播を含む統合枠組みを提案する方法論レビューであり、植物状態の取得・推定が中心である。

abstractmethodologies for repeated monitoring at this scale remain fragmented

Machine vision RGB phenotyping reveals divergent and real‐time responses to different watering regimes in near‐isogenic wheat genotypes →

Abstract This study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences under well‐watered and reduced watering conditions in genetically uniform wheat ( Triticum aestivum L.) populations. It aims to support the design of breeding populations by identifying parents with complementary coping mechanisms that can be combined in crosses to produce superior progeny. We used RGB imaging to monitor side‐projected area (SPA) in BC 2 F 6 wheat progenies under well‐watered, pre‐anthesis, and post‐anthesis reduced watering conditions. SPA was modeled with logistic growth curves per genotype to extract dynamic canopy traits, which, together with the area under the SPA‐based growth curve, were then correlated with yield, straw biomass, harvest index, and spike traits measured at maturity. Despite genetic similarity, RGB‐based imaging revealed distinct phenotypes under normal conditions and stress response strategies among wheat lines, highlighting the value of dynamic, non‐destructive phenotyping for identifying complementary response patterns. Under well‐watered conditions ( n = 36), area under the curve was strongly associated with grain weight ( R 2 = 0.76, 95% confidence interval [CI]: 0.59–0.87), but relationships weakened under reduced watering, especially post‐anthesis, indicating a reduced association of canopy size with reproductive output. The data revealed contrasting response patterns among breeding lines based on characteristics of the logistic growth curve under normal conditions, their recovery slope after pre‐flowering reduced watering, or conversion of their straw biomass into harvestable grains. RGB imaging enables real‐time, non‐destructive detection of reduced watering responses in genetically similar wheat lines and provides complementary in‐season data to design next‐generation breeding populations for climate‐resilient cultivars.

Why it matches plant phenotyping methodsRGB画像で動的なキャノピー形質を抽出し、育種利用に向けた非破壊・リアルタイム表現型解析を実質的に評価しているため。

abstractThis study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences

Monitoring Crop Structure and Moisture Using GNSS Interferometric Reflectometry Based on SNR Modeling →

This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity.

Why it matches plant phenotyping methodsGNSS-IRによる作物構造・水分の推定手法が研究の中心であり、植生高やバイオマス密度などの植物形質を地上測定と比較して技術検証している。

abstractThis study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture.

Deep learning identifies optimal single spectral band for non-destructive fresh weight estimation in cabbage: A novel paradigm for postharvest management technology →

Hyperspectral imaging (HSI) systems offer rich spectral information for precision agriculture applications such as yield forecasting, but their high cost and complexity limit widespread adoption. The relationship spectral data complexity and prediction accuracy in non-destructive crop monitoring remains unclear, challenging the assumption that more complex spectral data inherently yields better predictions. We developed a workflow integrating UAV-based HSI (150 bands) with deep learning to non-destructively estimate fresh weight of cabbage heads (n = 680). A two-dimensional convolutional neural network (2D-CNN) was deployed to identify the single most predictive wavelength through systematic feature extraction. Performance was benchmarked against 20 conventional multi-band vegetation indices (VIs). The CNN identified a single spectral band at 565.63 nm as the optimal predictor. A predictive model using only this single-band input achieved a coefficient of determination (R²) of 0.49 on an independent test set, with a root mean square error (RMSE) of 0.99 kg and a mean absolute error (MAE) of 0.84 kg. This performance substantially surpassed the best-performing conventional multi-band VI (MCARI2, R² = 0.34), representing a 44% improvement in explained variance using single-band data input. This AI-driven approach autonomously distill HSI complexity into a single optimal wavelength that outperforms established multi-band indices. The findings provide a methodological framework for designing simplified, cost-effective spectral sensors for precision agriculture, potentially improving accessibility and scalability of crop monitoring technologies.

Why it matches plant phenotyping methodsキャベツ結球の生体重という植物形質を、UAVハイパースペクトル画像とCNNで非破壊推定するワークフローを開発し、独立テストと既存指標との比較で検証しており、形質取得手法が中心である。

abstractWe developed a workflow integrating UAV-based HSI (150 bands) with deep learning to non-destructively estimate fresh weight of cabbage heads (n = 680).

Enhancing rapeseed biomass and yield estimation with ensemble learning and synergistic multidimensional features. →

Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting. However, there is a scarcity of structured research on the estimation of rapeseed biomass yield. This study aims to address this gap by focusing on rapeseed in Jiangsu Province. Multispectral and RGB images captured by unmanned aerial vehicles (UAVs) were taken during key growth stages (budding, flowering, and podding stages). Using the extracted multidimensional features, we developed biomass-yield estimation models using four machine learning techniques. Subsequently, we employed ensemble learning with multidimensional, multi-stage data and used Shapley additive explanation (SHAP) for feature contribution analysis, thereby constructing a framework for predicting rapeseed harvest characteristics with high estimation accuracy and interpretability. Our analysis indicates that spectral‒texture is the most effective feature combination for biomass estimation, whereas the optimal combination for yield estimation includes three-dimensional (3D) spectral‒textural‒structural features. The synergy of these features, coupled with an ensemble learning model, significantly enhanced the accuracy of rapeseed biomass-yield estimation (biomass: coefficient of determination ( R 2 )=0.72, relative root mean square error (rRMSE)=14.35%; yield: R 2 =0.68, rRMSE=13.67%). The proposed model also achieved stable prediction results across the variety‒density interaction. Overall, this study presents an accurate and generalizable approach for estimating rapeseed biomass yield across various planting patterns, offering new insights for precision harvesting.

Why it matches plant phenotyping methodsUAV画像から抽出した多次元特徴とアンサンブル学習により、ナタネのバイオマス・収量を推定する方法が研究の中心であり、精度評価も実施しているため、植物フェノタイピング手法として適格です。

abstractUsing the extracted multidimensional features, we developed biomass-yield estimation models using four machine learning techniques.

Saving water for more crop per drop: high-throughput phenotyping reveals plastic regulation of transpiration in wheat under natural evaporative demand →

Improving transpiration efficiency (TE) offers a mechanistic pathway to enhance yield under drought by modulating the balance between carbon assimilation and water loss. This study quantified genotypic variation in TE and dissected the physiological processes underlying this variation across a diverse panel of wheat genotypes. Following an initial experiment with six cultivars, 105 genetically diverse lines were evaluated under well-watered conditions across naturally fluctuating vapour pressure deficit (VPD). Transpiration was measured at 10-minute intervals using a high-throughput lysimeter platform and normalised daily at low VPD to minimise confounding effects arising from genotypic differences in canopy size. Variation in TE was strongly associated with reduced normalised transpiration rate at high VPD (TR norm-highVPD ). No relationship was detected with maximum photosynthetic capacity, indicating that TE differences were driven primarily by regulation of water loss rather than carbon gain. High-TE genotypes achieved either greater biomass for a given water use or equivalent biomass with reduced water use, consistent with conservative stomatal regulation under high evaporative demand. A complementary experiment conducted under low VPD revealed limited genotypic variation in intrinsic TE, suggesting that genetic control of TE is predominantly expressed under high atmospheric demand. The consistency of TE–TR norm-highVPD relationships across experiments highlights TR norm-highVPD as a robust and physiologically meaningful phenotyping target. Several high-TE genotypes outperformed modern cultivars, offering novel sources of allelic variation for transpiration regulation. Collectively, these results define a mechanistically grounded, scalable phenotyping framework to target VPD-responsive water-use traits and support breeding strategies aimed at improving drought resilience and water productivity.

Why it matches plant phenotyping methods高スループットライシメータで蒸散を定量し、VPD応答性の水利用形質を検証・評価するスケーラブルな表現型解析枠組みが研究の中心である。

abstractTranspiration was measured at 10-minute intervals using a high-throughput lysimeter platform

Turning Mediterranean Farmlands Into Priority Habitats: Natural Expansion of Juniper Woodlands After Agricultural Abandonment →

ABSTRACT Aims Natural woodland expansion into former agricultural land contributes to conservation and global reforestation goals. However, tree colonisation and woodland persistence depend on interactions among vegetation, land‐use history and suitable microclimatic conditions. Understanding these drivers is essential for anticipating woodland expansion outcomes over large spatial and temporal scales. Methods We combined airborne photogrammetry with field measurements to assess the long‐term success of Juniperus thurifera woodlands—a Natura 2000 priority habitat—across a 20‐km 2 region of central Spain. A canopy height model, calibrated with field biomass data, was used to map juniper biomass, while time series land‐cover maps estimated woodland age. This novel approach enabled evaluation of colonisation success using a space‐for‐time substitution along the expansion frontier. Results Over the past 34 years, land cover has changed markedly, with agricultural land declining by 75% and open woodland tripling. Juniper stands have expanded from steep slopes onto flatter terrain and into areas with lower irradiance. Increasing dwarf‐shrub density within stands reduced juniper biomass by up to 25% in the oldest woodlands. Higher solar exposure promoted faster biomass accumulation through time but limited biomass in younger stands. Contrarily, new stands under lower insolation showed greater biomass for their age, suggesting positive land‐use legacies where drought stress was reduced. Conclusion Overall, J. thurifera woodlands have expanded substantially over recent decades, yet growth constraints differ across the expansion front. With our innovative framework integrating high‐resolution photogrammetry and field‐based biomass models, our study underscores the interplay between local competition and abiotic factors in shaping woodland dynamics across space and time. This framework offers a valuable, scalable and transferable tool for monitoring and managing long‐term ecosystem dynamics across larger spatial and temporal scales and informing habitat restoration and conservation planning under changing environmental conditions.

Why it matches plant phenotyping methods航空写真測量とフィールド biomass データで樹木バイオマスを推定・地図化する手法が研究の中心であり、植物群落の形質を大規模に測定する実質的なフェノタイピング応用である。

abstractA canopy height model, calibrated with field biomass data, was used to map juniper biomass

Artificial intelligence in sugarcane breeding: A comprehensive review of applications, tools, and future prospects →

Sugarcane is a high-value industrial crop vital for sugar and biofuel production, yet increasingly constrained by climate variability, biotic and abiotic stresses, soil degradation, and inefficient input use. Traditional breeding and crop management approaches are often slow, labour-intensive, and less precise, emphasizing the need for digital transformation in sugarcane agriculture. AI now offers powerful tools to accelerate genetic improvement, enhance stress resilience, and optimize resource-use efficiency. This review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems. ML and DL models enable automated, accurate prediction of key traits such as biomass, canopy temperature, nitrogen status, and sugar recovery using UAV, satellite, and proximal sensing data. AI-powered genomic selection approaches leveraging convolutional networks, transformers, and attention mechanisms improve prediction accuracy for yield, ratooning ability, and stress tolerance by integrating SNPs, pedigree, and multi-environment datasets. Emerging innovations such as digital twins, multimodal data fusion, reinforcement learning-based irrigation scheduling, and climate-smart advisory models further strengthen real-time crop intelligence. The integration of blockchain-enabled breeding databases, FAIR data standards, and interoperable analytics pipelines supports scalable and collaborative research. Literature analysis reveals 15-30% gains in selection efficiency, >90% accuracy in disease detection, and phenotyping cost reductions of up to 70%. Key challenges remain, including scarce annotated datasets, genotype × environment complexity, model interpretability, and adoption barriers for smallholders. A future roadmap is proposed featuring multimodal foundation models, edge-AI deployment, and explainable breeder dashboards. AI is redefining sugarcane research from reactive to predictive, enabling climate-resilient, sustainable, and profitable production systems.

Why it matches plant phenotyping methodsサトウキビ育種におけるAI応用の総説であり、高スループット表現型解析、UAV・衛星・近接センシングによる形質推定を主要な対象として扱っているため、フェノタイピング手法レビューとして適格。

abstractThis review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems.

Application of unmanned aircraft remote sensing image processing method based on artificial intelligence algorithm in corn growth assessment →

This paper proposes a method based on UAV low-altitude photogrammetry and deep learning algorithms for corn crop growth monitoring. During the shooting process, a unified UAV photogrammetry strategy is set to ensure that the obtained images have high spatial resolution, and after pre-processing the original images, a convolutional neural network (CNN) model is utilized to extract features from the images and improve the accuracy of the CNN with the help of the idea of transfer learning. In addition, multi-scale feature fusion and attention mechanism are introduced to allow the model to focus on important location information, and weighted multi-task loss function is used to jointly optimize the multi-objective values such as plant height, leaf area index, and biomass. Experiments show that the method has good real-time performance and scalability while maintaining high prediction accuracy, providing an effective solution for crop monitoring in precision agriculture.

Why it matches plant phenotyping methodsUAV画像と深層学習を用いてトウモロコシの草丈、葉面積指数、バイオマスを推定する手法自体が研究の中心であり、植物形質推定の方法開発・応用に該当する。

abstractThis paper proposes a method based on UAV low-altitude photogrammetry and deep learning algorithms for corn crop growth monitoring.

Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model. →

Water stress is a global challenge that severely impacts crop production by hindering essential physiological processes. To address this issue, proximal sensing has emerged as a promising technique for the early identification of stress in vegetables, enabling timely management interventions and optimizing yield. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to a decrease in various plant traits. The study also revealed significant differences in RGB image indices between different irrigation levels, with strong positive relationships identified for the majority of RGB image indices incorporating green components (G) and R2 reaching 0.99 for various plant traits. However, the red-blue simple ratio (RB) index, which does not consider the G, did not significantly correlate with any of the plant traits. The ANN models achieved high prediction accuracy, with high R2 values reaching 0.99 for various plant traits and yields. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under limited water conditions.

Why it matches plant phenotyping methodsRGB画像指標とANNによる植物形質・収量の定量推定が研究の中心であり、予測精度も評価しているため、画像ベース形質推定の方法適用・検証に該当する。

abstractThis study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits

Sorghum plant height and yield prediction using multispectral data and sUAS →

Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic improvement, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, digital terrain models (DTMs), and digital surface models (DSMs). Manual plant height measurements were used for correlation and simple linear regression analyses, while biomass was predicted using random forest regression. Results. The DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE = 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE = 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. The DTMs and DSMs derived from multispectral imagery accurately predicted plant height during later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into predictive models enhanced biomass yield prediction. The findings demonstrate that sUAS-mounted sensors and multispectral indices are valuable tools for phenotyping in sorghum breeding programs in Costa Rica.

Why it matches plant phenotyping methodssUASマルチスペクトル画像とフォトグラメトリから草丈・バイオマスを推定し、実測値との相関・回帰および予測精度を評価する手法中心の研究である。

abstractObjective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights.

Extraction of Plant Physiological Features Using Multispectral Imaging and Spectrophotometry: A Systematic Review Highlighting Research Gaps for Stenocereus spp. →

Objectives: Multispectral imaging and spectrophotometry are widely used to estimate plant physiological characteristics, yet the literature remains fragmented across sensors, indices, and analytical approaches. Methods: This systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework). Web of Science, Scopus, Google Scholar, and Consensus were searched up to January 2025 for peer-reviewed studies and selected gray literature studies focused on plant physiological trait estimation using multispectral or spectrophotometric methods. From 256 identified records, 96 studies met the eligibility criteria. Methodological quality was assessed across five domains, and results were synthesized narratively owing to high heterogeneity. Results: A total of 96 studies met the eligibility criteria. Among these, multispectral sensors were the most commonly used (40.7%), followed by UAV-mounted platforms (25.9%), while hyperspectral sensors accounted for 18.5% of the studies. The most frequently used vegetation index was NDVI, reported in 87% of the studies, mainly for estimating vigor, biomass, and canopy structure. Discussion: Although multispectral indices reliably capture key agronomic traits, cross-study comparability is currently hampered by significant methodological variability and a lack of consistent validation protocols. Conclusions: Multispectral imaging and spectrophotometry are effective tools for estimating plant physiological traits, but greater standardization is needed across studies. Owing to the limited number of studies on Stenocereus spp., the review was expanded to plants in general; the shortage of reports addressing Stenocereus spp. highlights the need for future research in these species.

Why it matches plant phenotyping methods植物生理形質推定のためのマルチスペクトル画像・分光法を対象とした系統的レビューであり、方法の比較、品質評価、検証標準化を中心に扱っている。

abstractThis systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework).

Modelling mountain pine beetle-impacted forest wildland fire fuel distributions from remotely piloted aircraft systems imagery and point clouds →

This study compared 3D point cloud data derived from Structure-from-Motion (SfM) in 2021 and lidar in 2022 acquired using remotely piloted aircraft systems (RPAS). The overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution. The outcomes of the modelling framework were then applied to examine the impacts of mountain pine beetle (MPB) on canopy fuel load volumes in Jasper National Park prior to a high intensity wildfire in 2024. Tree species were classified using geographic object-based image analysis (GEOBIA) with an overall accuracy of ∼ 90%, with higher performance in relatively open canopies with minimal shadow. Photogrammetric and lidar point clouds resolved accurate individual tree height (R 2 = 0.96; 0.99, respectively) when compared to field measurements. Crown base height derived using a windowed point density approach improved agreement with field data (R 2 = 0.76; 0.91, respectively) and improved relative to previously reported methods. Across sites with varying MPB-induced tree mortality, plots dominated by dead conifers showed a redistribution of canopy fuels towards the ground compared to plots of mostly live conifers. This structural shift suggests increased ladder fuel development, reduced canopy continuity, and a heightened likelihood of surface to crown fire transition. The results demonstrate that RPAS point clouds can effectively characterize tree structure and improve crown base height estimation, supporting more accurate assessment of canopy bulk density. These measurements provide a viable alternative to labour-intensive field surveys and can then be used as calibration and validation data for broad-area forest assessment fuel modelling using airborne and satellite remotely sensed data.

Why it matches plant phenotyping methodsRPASのSfMおよびLiDAR点群を用いて樹高、樹冠基部高、林冠燃料構造を抽出し、現地測定と比較・検証している。個体・林分の植物構造測定法が研究の中心である。

abstractThe overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution.

Small Area Estimation of Forest Volume Using Mixed Effects Random Forests and Multi-Source Remote Sensing Data →

Accurate estimation of forest growing stock volume (GSV) at fine spatial scales is essential for sustainable forest management, carbon accounting, and local decision-making. However, traditional forest inventories often lack sufficient sampling density to provide reliable estimates for small areas. This study evaluates the performance of two small area estimation approaches: the Empirical Best Predictor (EBP) based on a nested-error linear regression model, and the Mixed-Effects Random Forest (MERF) for estimating GSV at the forest stand level using multi-source remote sensing data. The analysis was conducted in the Vallombrosa Nature Reserve (Italy), integrating field measurements from 101 plots with auxiliary variables derived from Sentinel-2 imagery and airborne LiDAR. Both methods were applied to estimate the mean and total GSV across 658 forest stands, many of which lacked direct observations. Model performance was assessed using spatial cross-validation, and uncertainty was quantified using root-mean-square error (RMSE). Results show that MERF outperformed EBP in predictive accuracy, achieving higher R2 (0.67 vs. 0.37) and lower RMSE (151 vs. 202 m3 ha{square}1). MERF also produced more stable and precise uncertainty estimates, with improved coverage of observed values. While both methods yielded comparable total GSV estimates, EBP exhibited greater variability and sensitivity to model assumptions. In contrast, MERF effectively captured non-linear relationships and handled multicollinearity among predictors, though at the cost of reduced interpretability and higher computational demand. Overall, findings highlight the advantages of integrating machine learning with mixed-effects modeling for SAE in forestry, particularly under conditions of sparse sampling and complex ecological variability.

Why it matches plant phenotyping methods森林スタンドの生長蓄積量（GSV）という明示的な植物群落形質を、衛星・LiDARデータと統計／機械学習手法で推定し、空間交差検証とRMSEで性能比較しているため、方法の適用・検証が中心である。

abstractThis study evaluates the performance of two small area estimation approaches: the Empirical Best Predictor (EBP) based on a nested-error linear regression model, and the Mixed-Effects Random Forest (MERF) for estimating GSV at the forest stand level using multi-source remote sensing data.

GrowScreen-Rhizo 3 - automated large-scale high throughput greenhouse phenotyping of plant root and shoot development. →

Roots play a pivotal role for plant performance, but they are difficult to access, which hampers quantitative measurements. Repeated imaging of rhizotrons, flat growth containers with a transparent side, has proven suitable to assess dynamics of root traits in indoor experiments. However, measuring hundreds of soil-grown plants with high temporal resolution remains a laborious challenge. We introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3. This platform was designed to image shoots and roots of individual plants simultaneously and derive digital proxy traits for biomass and growth. In addition, built-in weighing and watering stations deliver water use data for each rhizotron. To achieve the desired throughput (image all 896 plants once a day) a high degree of automatization and standardization was required. We realized a modular plant-to-sensor solution, using a fleet of automated guided vehicles (AGVs) to transport large rhizotrons (80x40x5 cm) to four measurement chambers for daily imaging, weighing, and watering. Simultaneous imaging of the root system with a high-resolution camera (116 μm per px) and the shoot from six different viewing angles allows to monitor plant growth with high spatial and temporal accuracy. First, we verified that moving plants to the measurement chambers did not significantly affect above- or belowground plant growth. Next, we measured phenotypic variation in root and shoot traits of 24 barley genotypes, parents of a nested association mapping population. Our analysis revealed that heritability of root traits such as root system depth and seminal root length was moderate to high (r 2 =0.52 and r 2 =0.93, respectively), enabling further assessment of increasing numbers of recombinant genotypes. The results demonstrate the suitability of GrowScreen-Rhizo 3 to phenotype a range of plant species characterized by various growth habits, including crop, niche, and wild plant species. We conclude that GrowScreen-Rhizo 3 will contribute significantly to the development of phenotyping pipelines for the identification of candidate genotypes with improved resource use efficiency and to pre-breeding processes of climate-resilient crops.

Why it matches plant phenotyping methods根とシュートを自動撮像し、バイオマス・成長などの形質を抽出する大規模フェノタイピング platform の開発・検証が中心である。

abstractWe introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3.

Turning Mediterranean Farmlands Into Priority Habitats: Natural Expansion of Juniper Woodlands After Agricultural Abandonment →

ABSTRACT Aims Natural woodland expansion into former agricultural land contributes to conservation and global reforestation goals. However, tree colonisation and woodland persistence depend on interactions among vegetation, land‐use history and suitable microclimatic conditions. Understanding these drivers is essential for anticipating woodland expansion outcomes over large spatial and temporal scales. Methods We combined airborne photogrammetry with field measurements to assess the long‐term success of Juniperus thurifera woodlands—a Natura 2000 priority habitat—across a 20‐km 2 region of central Spain. A canopy height model, calibrated with field biomass data, was used to map juniper biomass, while time series land‐cover maps estimated woodland age. This novel approach enabled evaluation of colonisation success using a space‐for‐time substitution along the expansion frontier. Results Over the past 34 years, land cover has changed markedly, with agricultural land declining by 75% and open woodland tripling. Juniper stands have expanded from steep slopes onto flatter terrain and into areas with lower irradiance. Increasing dwarf‐shrub density within stands reduced juniper biomass by up to 25% in the oldest woodlands. Higher solar exposure promoted faster biomass accumulation through time but limited biomass in younger stands. Contrarily, new stands under lower insolation showed greater biomass for their age, suggesting positive land‐use legacies where drought stress was reduced. Conclusion Overall, J. thurifera woodlands have expanded substantially over recent decades, yet growth constraints differ across the expansion front. With our innovative framework integrating high‐resolution photogrammetry and field‐based biomass models, our study underscores the interplay between local competition and abiotic factors in shaping woodland dynamics across space and time. This framework offers a valuable, scalable and transferable tool for monitoring and managing long‐term ecosystem dynamics across larger spatial and temporal scales and informing habitat restoration and conservation planning under changing environmental conditions.

Why it matches plant phenotyping methods航空写真測量と現地 biomass データで樹冠高モデルを較正し、ジュニパー biomass を広域推定する手法が研究の中心であり、植物群落の形態・生長形質を測定する実質的なフェノタイピング手法に該当する。

abstractA canopy height model, calibrated with field biomass data, was used to map juniper biomass

Unmanned aerial vehicle–based spatiotemporal phenotyping and growth modeling for forecasting potato yield →

Abstract Monitoring spatial variations in plant growth and forecasting yield before harvest provides valuable insights for optimizing agronomic decision‐making in potato ( Solanum tuberosum L.) cultivation. Although unmanned aerial vehicle (UAV)‐based remote sensing has recently enabled the development of tuber fresh weight (TW) estimation models, their integration into practical yield‐forecasting systems remains limited. In this study, we developed machine learning models to estimate tuber weights at multiple preharvest time points using RGB and multispectral UAV imagery. Image‐derived features were extracted from the orthomosaic and digital surface model images for each plot, and a random forest regression model was trained for TW estimation. The estimated values were subsequently used to fit the Gompertz growth curves, which were then used to forecast the yield at the expected harvest time. The correlation between the estimated and observed values was strong in the UAV‐based TW estimation, with correlation coefficients exceeding 0.8 and coefficients of determination ( R 2 ) above 0.6 at all time points. Yield forecasts based on fitted growth curves achieved a correlation of 0.78 and an R 2 of −0.17 in 2023 and 0.70 and an R 2 of 0.47 in 2024. These results demonstrate that UAV‐based sampling combined with machine learning is a feasible approach for monitoring spatiotemporal variations in tuber growth and forecasting potato yield at the plot level prior to harvest.

Why it matches plant phenotyping methodsUAV画像からジャガイモ塊茎重量を推定し、機械学習と成長曲線で収量を予測する手法が研究の中心であり、推定精度も検証している。

abstractwe developed machine learning models to estimate tuber weights at multiple preharvest time points using RGB and multispectral UAV imagery.

Boosting innovative microbial solutions by understanding the functional benefits of endophytic rhizobacteria on tomato growth and protection using plant phenomics →

Biological control represents a valuable tool for the sustainable management of soil-borne diseases in tomato cultivation and relies on the availability of effective microbial solutions. Digital technologies can support to the ecodesign stage by accelerating the screening and selection of high-performing microbial biocontrol agents. In this study, a collection of eleven endophytic bacteria strains recruited from the tomato root endosphere and proved to be compatible with Trichoderma spp. (non-target effect), was characterized for antagonistic and biofertilization/biostimulant traits, and evaluated in planta against two major tomato pathogens: Fusarium oxysporum f. sp. lycopersici and Sclerotium rolfsii. Plant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions. Multivariate analysis of 20 digitally computed phenotypic traits helped the detection of Peribacillus sp. C5NA and Neobacillus sp. TR12 as highly effective against wilt, and capable of counteracting the reduction in leaf angle surface and chlorophyll: typical tracheofusariosis symptoms. On the other hand, Peribacillus strains TR2 and C6 treatments caused partial phenotypic recovery in plants affected by Sclerotium rot. Interestingly, Microbacterium sp. TR9, appeared to be multifaceted. It showed mild multisuppressivity against both pathogens coherently with the exhibited N-acetyl-b-glucosaminidase, polysaccharide breaking and in vitro antifungal activities. In addition, it also acted as a putative biostimulant in the absence of pathogens, increasing digital biomass, plant height, and NDVI, in line with its proven strong ability to produce ammonia, fix nitrogen, solubilize phosphates, and release indoleacetic acid. Overall, the integration of phenomics supported the high-resolution detection of plant responses and supported the identification of multifunctional microbial strains with biocontrol and biofertilization potential for sustainable tomato production.

Why it matches plant phenotyping methodsPlantEye 500によるマルチスペクトル表現型取得と20形質のデジタル解析が、微生物資材のスクリーニングおよび植物応答評価の中心的手法として用いられているため。

abstractPlant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions.

Multi-Platform LiDAR Comparative Assessment for Above-Ground Biomass and Carbon Estimation in Mediterranean Woody Crops →

Reliable Aboveground Biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy cover proxies) were extracted from normalised point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha⁻¹; R² = 0.994) and Villaseca (RMSE = 0.872 Mg ha⁻¹; R² = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha⁻¹; R² = 0.994). TreeQSM closely matched field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops.

Why it matches plant phenotyping methods複数LiDARプラットフォームと3次元再構成・機械学習を比較検証し、樹木・圃場レベルの地上部バイオマスという植物形質を推定する手法が研究の中心である。

abstractwe benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS)

A terrestrial laser scanning‐based workflow for component‐wise estimation of individual tree above‐ground biomass →

Abstract Accurate estimation of individual tree above‐ground biomass (AGB) and its component‐wise allocation is crucial for advancing ecological research and forest management. However, current biomass estimation methods, such as destructive sampling and allometric equation–based approaches, face limitations in both operational efficiency and cost‐effectiveness, and only destructive sampling can provide component‐wise biomass measurements, which is impractical for large‐scale studies or repeated measurements. In this study, we present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation. A total of 68 trees were scanned to obtain high‐resolution TLS data and subsequently destructively harvested to acquire field reference measurements for validation. The results demonstrate that the workflow achieved high accuracy in predicting AGB at the individual tree level (coefficient of determination/ R 2 = 0.88, root mean squared error/RMSE = 16.83 kg, mean absolute error/MAE = 12.18 kg), significantly outperforming estimates derived from locally calibrated allometric equations ( R 2 = 0.61, RMSE = 29.86 kg, MAE = 24.52 kg). Furthermore, this study provides evidence of the strong capability of TLS in estimating branch‐level biomass, with high accuracy achieved across branch orders ( R 2 ranging from 0.66 to 0.91, RMSE from 3.55 to 380 g and MAE from 2.97 to 290 g). By providing precise, non‐destructive estimates of biomass distribution across branches and leaves, this workflow demonstrates strong potential for improving the accuracy of tree biomass quantification, supporting investigations of resource allocation strategies, and enhancing forest carbon monitoring.

Why it matches plant phenotyping methodsTLSによる樹木の地上部・器官別バイオマスを推定するワークフローを開発し、破壊的実測で精度検証しており、植物形質取得法が研究の中心である。

abstractwe present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation.

Phenotyping Root and Shoot Traits for Drought Response in Bambara Groundnut ( Vigna subterranea (L.) Verdc.). →

Drought stress poses a significant challenge to food security in sub-Saharan Africa, particularly for smallholder farmers in dryland systems. Bambara groundnut ( Vigna subterranea (L.) Verdc.), an underutilised legume with inherent drought tolerance, remains underexplored in terms of its root system traits. This greenhouse study investigated the early root and shoot responses of six Bambara groundnut genotypes under well-watered (100% field capacity) and water-stressed (50% field capacity) conditions using rhizotron-based phenotyping. Significant genotypic differences ( p < 0.01) were observed in root traits such as root system depth (RSD: 11.0-19.9 cm), root system width (RSW: 6.96-12.2 cm), and root dry mass (RDM: 0.42-1.27 g). The ARC genotype exhibited a strong drought-avoidance strategy, increasing RSD from 12.2 to 19.9 cm and RDM from 0.42 to 1.16 g under stress. The Tiga Nicuru DIP-C-F7471 genotype showed adaptive plasticity, maintaining deeper roots (11.0-14.5 cm), high convex hull area (CHA), and root-shoot ratio (RSR) values, despite a reduction in RDM, suggesting a resource-conserving strategy. Principal Component Analysis (PCA) captured 93.6% of the total variability among genotypes. Root traits, particularly total root length (TRL), convex hull area (CHA), root system width (RSW), and root dry mass (RDM), were the main contributors to genotype differentiation. Strong positive correlations (r = 0.88-0.97) between root and shoot traits suggest that genotypes with more developed root systems also supported greater shoot growth, highlighting the coordinated response of above- and below-ground traits under drought stress. These findings provide valuable targets for breeding and highlight the value of rhizotron-based screening for root trait selection. Future field validation and full-season studies are recommended to confirm their relevance for improving yield stability in dryland agriculture.

Why it matches plant phenotyping methods根系・地上部形質を取得するrhizotron-based phenotypingを用い、そのスクリーニング価値を主要な貢献として扱っているため、植物フェノタイピング手法の実質的応用に該当する。

abstractusing rhizotron-based phenotyping

Species-specific aboveground biomass estimation using semantic segmentation of UAV photogrammetric point clouds →

Accurate, species-specific estimation of aboveground biomass (AGB) at the individual plant level is essential for characterizing forest structure, supporting ecological and wildfire modelling, and enabling fine-scale carbon accounting. This study presents a methodological framework for estimating species-specific AGB at individual plant level in Mediterranean ecosystems using UAV-based digital aerial photogrammetry (UAV-DAP). High-resolution point clouds were processed through a multi-step workflow including object-based segmentation, thirteen species classification and AGB regression modeling. The overall accuracy of species classification across six study areas was 81.6%, with a maximum of 89.9%. The regression models for AGB estimation yielded an average R 2 of 0.69 across all species, highlighting species such as Anthyllis cytisoides (R 2 = 0.83, RMSE = 0.07 kg, n = 47), Juniperus oxycedrus (R 2 = 0.83, RMSE = 3.17 kg, n = 32); or Pinus halepensis (R 2 = 0.77, RMSE = 11.79 kg, n = 20). These findings demonstrate the potential of UAV-DAP for practical estimates of AGB. The study underscores UAV-DAP as a cost-effective tool for forest management, ecological monitoring, and biomass assessments, paving the way for broader applications in environmental science and resource management.

Why it matches plant phenotyping methodsUAV-DAP点群から個体レベルの樹種別地上部バイオマスを推定する画像・計算ワークフローが中心で、セグメンテーション、分類、回帰と精度評価を含むため、植物形質計測手法として適格。

abstractThis study presents a methodological framework for estimating species-specific AGB at individual plant level in Mediterranean ecosystems using UAV-based digital aerial photogrammetry (UAV-DAP).

UniTriRob: a robust machine learning regression model for predicting lettuce yields in aeroponic vertical farming. →

Aeroponic vertical tower farming is a cost-effective, sustainable method for optimizing the food crop-Lactuca Sativa (lettuce-a greeny leaf vegetable); yet accurate biomass prediction of the lettuce crop remains challenging due to the non-linear relationship between the climatic conditions and the variable lettuce growth parameters. To address this challenge, a robust machine learning model called UniTriRob regression model has been developed. This model primarily focuses on mitigating the effects of outliers and heteroskedastic errors across key growth-related parameters, including pH, total dissolved solids (TDS), temperature, electrical conductivity (EC), turbidity, humidity, light intensity and growth. The experimental validation highlights the model's capability with high R-squared value of 97.8386% and the minimized error rate of 0.46, that outperforms the conventional forecasting methods. Hence, the model presents a viable alternative for maximizing aeroponic lettuce production efficiency and increasing yield forecast accuracy, contributing to sustainable agricultural practices.

Why it matches plant phenotyping methodsレタスのバイオマス・収量という植物形質を予測する機械学習回帰モデルの開発と実験的検証が研究の中心であり、単なる農業実験のルーチン測定ではない。

abstractTo address this challenge, a robust machine learning model called UniTriRob regression model has been developed.

Biomass prediction and shoot growth characterization of single-staked yam plants using UAV imagery. →

This study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns in single-staked white Guinea yams ( Dioscorea rotundata ). Multi-angle aerial images from nadir and oblique views were used to extract vegetation- and height-related indices that served as predictors in machine learning models. Support vector regression using combined-view imagery provided the highest prediction accuracy (R² = 0.79) and remained robust across growth stages, years, fertilizer treatments, and genotypes. Notably, the combined-view configuration outperformed single-view imaging, demonstrating the advantage of capturing complementary canopy-structure information in complex staked-vine canopies. Time-series biomass estimates enabled the fitting of genotype-specific Richards growth curves using Bayesian inference. Significant genotypic variations were observed in parameters associated with maximum biomass and early growth rate, whereas phenology-related parameters showed comparatively minimal differences. These parameter differences may reflect variation in canopy architecture and growth allocation among genotypes. Overall, this integrated workflow provides a scalable tool for nondestructive monitoring of yam growth dynamics and for summarizing biomass trajectories with interpretable parameters, supporting breeding efforts aimed at improving yam productivity and yield stability across diverse cultivation conditions.

Why it matches plant phenotyping methodsUAV画像からヤムのシュートバイオマスと生育を推定する画像解析・機械学習ワークフローが研究の中心であり、複数視点画像の比較検証と時系列形質推定を行っている。

abstractThis study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns

Wheat biomass estimation by fusing color index and canopy volume based on UAV RGB images →

PURPOSE: Above-ground biomass (AGB) is a critical indicator for assessing the growth status of winter wheat. Since the range of extracted color indices (CIs) tends to remain constant after flowering and UAV RGB images cannot capture the lower and middle structures of the canopy under dense planting conditions, the efficiency of AGB estimation models is limited. Therefore, this study aimed to improve the accuracy of winter wheat AGB estimation by incorporating canopy volume information with RGB-based CIs. METHODS: RGB images were acquired to generate Digital Orthophoto Maps (DOM) and Digital Surface Models (DSM) at Feekes 10, Feekes 10.5.2, Feekes 10.5.4, and Feekes 11.3 growth stages. Eight biomass-related CIs were extracted from the DOM, and canopy volume (V) was calculated from the DSM for corresponding regions. The RReliefF algorithm was applied to rank feature importance and select optimal features. Eight statistical and machine learning regression algorithms, including Gaussian process regression (GPR), were used to construct AGB estimation models with different feature combinations. RESULTS: The results showed that the GPR algorithm outperformed other regression methods, achieving the highest estimation accuracy with R² values of 0.775, 0.741, 0.702, and 0.568 at the four growth stages, respectively. Compared with models using CIs alone, integrating canopy volume with CIs improved AGB estimation accuracy from Feekes 10.5.2 to Feekes 11.3, with R² increases of 7.31%, 6.55%, and 22.98%, respectively. CONCLUSION: Overall, combining canopy volume features derived from UAV-based RGB imagery with CIs and applying effective machine learning algorithms enables rapid and accurate estimation of winter wheat AGB.

Why it matches plant phenotyping methodsUAV画像から色指数とキャノピー体積を抽出し、冬コムギの地上部バイオマスを推定する取得・解析手法が研究の中心であるため。

titleWheat biomass estimation by fusing color index and canopy volume based on UAV RGB images

Comparative Evaluation of Allometric, Machine Learning, and Ensemble Approaches for Modeling Dynamic Structure-Fresh Weight Relationships in Sweet Pepper. →

Accurate fresh weight (FW) estimation is essential for growth monitoring and yield prediction in greenhouse fruit vegetables, but remains challenging due to the dynamic allocation between vegetative and reproductive organs. This study aimed to systematically evaluate modeling strategies for FW estimation in sweet pepper and identify which approach is most suitable under conditions of dynamic biomass partitioning. Non-destructive morphological measurements were collected under greenhouse cultivation, and allometric models based on geometric equations were established as baselines. Their performance was compared with machine learning (ML) models and ensemble learning frameworks. To address limited data availability, numerical data augmentation with Gaussian noise and a variational autoencoder was applied. Among the allometric models, the stick model combined with a sigmoid function showed the highest performance, with an R2 of 0.80 for shoot FW and 0.54 for fruit FW. All ML models outperformed the allometric models, and the ensemble model achieved the highest predictive accuracy, with an R2 of 0.96 for shoot FW and 0.89 for fruit FW. Data augmentation further improved predictive performance across all ML models, particularly for fruit FW prediction. Feature contribution analysis revealed that temporal progression was the dominant predictor of fruit FW, while structural traits played the primary role in shoot FW estimation. Ensemble-based ML, combined with data augmentation, provides a methodological framework for non-destructive FW estimation of sweet pepper in controlled environments such as greenhouses and smart farming systems.

Why it matches plant phenotyping methods非破壊形態計測からピーマンの地上部・果実の生重を推定するアロメトリック／機械学習／アンサンブル手法を比較・評価しており、表現型取得・推定手法が研究の中心である。

abstractThis study aimed to systematically evaluate modeling strategies for FW estimation in sweet pepper

Insights into biomass accumulation and challenges in grain yield prediction of elite breeding materials using UAV‐based vegetation indices in soft red winter wheat →

Abstract High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to understand the genetic basis of biomass accumulation in winter wheat and how this biomass is related to grain yield using unmanned aerial vehicle (UAV)‐based vegetation indices. A large panel of 596 soft red winter wheat genotypes was evaluated for agronomic performance in six environments to verify the ability of HTPs to predict grain yield using multivariate genomic prediction and random regression with Legendre polynomials to model growth through time. An additional set of 22 breeding lines was directly measured for above‐ground biomass, serving as a ground truth for the HTP‐derived biomass estimates. Cumulative vegetation indices were found to be a reliable method to infer biomass accumulation. Vegetation indices capture reliable phenotypes but exhibit low and inconsistent genetic correlation to grain yield, especially when incorporating residual covariance between traits. Predictive abilities of grain yield increased when using vegetation indices as a secondary trait in a multi‐trait genomic prediction model, but increases were highly variable across environments and growing stages, which may be confounded by micro‐environmental variation and lead to biased estimates of true genetic merit. Our results suggest that UAV‐based vegetation indices can be used to understand genetic parameters of biomass accumulation, but wheat breeders should use caution in their use as proxies for grain yield.

Why it matches plant phenotyping methodsUAV植生指数によるバイオマス推定を中心に、実測値を用いて検証し、遺伝解析・収量予測への利用可能性を評価しているため、植物フェノタイピング手法の実質的な適用・検証に該当する。

abstractCumulative vegetation indices were found to be a reliable method to infer biomass accumulation.

Enhancing Forest Biomass Estimation with Synthetic Airborne Laser Scanning via Voxel-based Forest Reconstruction →

Abstract Accurate estimation of aboveground biomass (AGB) is essential for forest monitoring and carbon stock assessment. Airborne laser scanning (ALS) is widely used for large-scale AGB estimation, yet acquiring reference biomass from field measurements for training biomass regression models remains time-consuming and labour-intensive. Here we explore the potential of synthetic ALS data to enhance forest biomass estimation. Two virtual forest plots were generated using a voxel-based forest reconstruction approach to simulate ALS data. We compared the model performances under varying amount and proportion of simulated and real samples in the training set. We find that models trained exclusively on simulated samples underperform models trained solely on real samples. When real samples are scare, incorporation of synthetic samples substantially improves the model performance, with coefficient of determination (R²) increased by 0.001–0.73 and the root mean square error (RMSE) decreased by 0.07–2.26 Mg ha–1. When sufficient real samples are available, adding a small number of simulated samples further improves model performance, with RMSE decreased by 0.12–1.46 Mg ha–1. The optimal performance (R² = 0.852, RMSE = 33.47 Mg ha–1) is obtained when real samples comprise about 83% of the training samples. These findings demonstrate that synthetic ALS data can effectively complement real datasets in AGB modelling, improving accuracy under diverse data availability conditions.

Why it matches plant phenotyping methods森林プロットの地上部バイオマスという植物形質を、合成ALSデータとボクセル再構成で推定する手法を開発・比較評価しており、形質取得・推定法が研究の中心である。

abstractTwo virtual forest plots were generated using a voxel-based forest reconstruction approach to simulate ALS data.

Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation →

Abstract Purpose Crop growth models (CGM) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. Methods A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. Results The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to reduce ensemble spread and improve robustness. Real-case results confirmed these findings. DA assimilation of SM especially contributed to improving overall biomass accuracy, particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation, the joint assimilation yielded consistent results. In 2022, it achieved a root-mean-square error (RMSE) of 2275.20 kg/ha, a normalized RMSE (nRMSE) of 44.99%, and a bias of 1081.90 kg/ha. In 2023, RMSE, nRMSE and bias were 1120.29 kg/ha, 14.79%, and 284.05 kg/ha, respectively. Furthermore, the joint assimilation led to a tighter ensemble spread than single source-assimilation. Conclusion The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.

Why it matches plant phenotyping methodsSentinel-2反射率とSMAP土壌水分をAPSIMへ同化し、トウモロコシのバイオマスを推定するデータ同化フレームワークが研究の中心であり、植物形質の取得・推定手法を提案・評価している。

abstractThis paper proposes a DA framework to improve maize biomass estimation.

Tree-based machine learning models to predict maize growth, nitrogen uptake, and yield using UAV remote sensing →

Unmanned aerial systems (UAS) equipped with multispectral sensors enable within-season crop phenotyping; however, conventional vegetation index–based approaches often lack accuracy in predicting crop growth and yield. This study evaluated the performance of three machine learning (ML) algorithms, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), for predicting maize leaf area index (LAI), relative chlorophyll content (SPAD), aboveground biomass (AGB), plant height, nitrogen (N) uptake, and grain yield, and compared their performance with stepwise multiple linear regression (SMLR). Multispectral imagery (five spectral bands) was collected across multiple growth stages during the 2021 and 2022 growing seasons near College Station, Texas, under varying split N-fertilizer applications. Band reflectance and eight vegetation indices were used as model inputs. All ML models outperformed SMLR in predicting LAI, SPAD, AGB, N uptake, and plant height. SVM, RF, and XGBoost showed comparable performance for LAI (R² = 0.82–0.83), AGB (R² = 0.86–0.92), and plant height (R² = 0.95–0.97). However, XGBoost exhibited overfitting, resulting in lower validation accuracy for SPAD (R² = 0.54) and N uptake (R² = 0.68) compared with SVM (R² = 0.73 and 0.80, respectively). Grain yield prediction accuracy increased with crop maturity, with reproductive-stage imagery producing the highest accuracy across models (R² = 0.88–0.98), where all ML models outperformed SMLR. However, at the V6 growth stage, SVM and RF did not perform as well as SMLR. Overall, integrating ML with UAS-based multispectral imagery improved prediction accuracy across key maize phenotypic and nutrient traits, providing a robust framework for non-destructive, high-throughput phenotyping and precision N management.

Why it matches plant phenotyping methodsUASマルチスペクトル画像と機械学習により、作物の形態・生理・収量関連形質を推定する方法を比較評価しており、フェノタイピング手法が研究の中心である。

abstractThis study evaluated the performance of three machine learning (ML) algorithms, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), for predicting maize leaf area index (LAI), relative chlorophyll content (SPAD), aboveground biomass (AGB), plant height, nitrogen (N) uptake, and grain yield

Comparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development →

Abstract Fitness costs of plant disease defence are often subtle and difficult to quantify. In this study, we therefore used comparative high-throughput phenotyping in two independent facilities to assess growth, morphology and physiology of potato (cv. Désirée) with high time-resolution monitoring different defence mechanisms under pathogen-free conditions. Plants were either treated weekly with the resistance inducers β-aminobutyric acid (BABA; 10 mM) or potassium phosphite (KPhi; 36 mM) or comprised six transgenic lines expressing late blight resistance genes (single Rpi genes or a three-gene stack) or reduced jasmonate perception (StCOI1-RNAi). Over four weeks, image-derived traits revealed consistent cross-facility effects for plant height and colour: BABA treatment increased plant height but reduced canopy area and induced a paler greenness signature, whereas KPhi caused minimal and transient growth effects. Chlorophyll fluorescence at the NaPPI facility indicated reduced vitality (Rfd_Lss) in BABA-treated plants and increased Rfd_Lss following KPhi, while maximum PSII efficiency was largely unchanged. Several transgenic lines showed somewhat reduced above-ground biomass. Enzyme activity profiling produced distinct treatment and genotype signatures, but was strongly modulated by facility conditions that overrode these specificities. Overall, high-throughput phenotyping robustly detected subtle growth–defence trade-offs across platforms. Highlight High-throughput optical phenotyping validated across two independent research facilities reveals that stacked resistance genes and resistance inducers in potato trigger subtle growth trade-offs. Graphical abstracts Experimental timeline for high-throughput plant phenotyping platforms. Created in BioRender. Poque, S. (2026) https://BioRender.com/nmkve7g

Why it matches plant phenotyping methods二つの独立施設で高スループット光学フェノタイピングを比較・検証し、画像由来形質と蛍光指標の再現性を評価しており、フェノタイピング手法が研究の中心である。

titleComparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development

Sentinel plants enable quantitative monitoring of bioavailable nitrate in soils and microbial environments →

Microbial transformations of nitrogen in soils strongly influence plant nutrition and ecosystem function, yet these processes remain difficult to monitor. Existing approaches rely largely on destructive soil sampling and laboratory analysis, limiting the ability to track nitrate dynamics in situ. Here, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate. The sensor uses a synthetic nitrate-responsive promoter coupled to a ratiometric luciferase reporter, enabling high-dynamic-range measurements. Sentinel plants exhibit a dose-dependent, reversible nitrate response with high specificity over alternative nitrogen sources. In agricultural soils from multiple California field sites, sensor output closely tracked analytically measured nitrate concentrations and resolved nitrate amendments without destructive extraction. Beyond environmental sensing, sentinel plants enabled screening of nitrogen-fixing microbial communities and the detection of microbially generated nitrate in both liquid culture and soil systems. Using this platform, we identified a minimal three-member microbial consortium capable of converting atmospheric nitrogen into nitrate via sequential nitrogen fixation and nitrification. This consortium increased tissue nitrate accumulation and plant fresh weight, demonstrating that sentinel plants can both monitor nitrate availability and identify microbial communities that enhance plant growth. Significance Statement Nitrogen availability in soils fluctuates across space and time, yet most measurements rely on extracting soil samples and analyzing them in the laboratory. Such measurements provide only snapshots of nitrogen status and do not necessarily reflect the nitrogen that plants themselves experience. Here, we engineer plants that act as living nitrate sensors by converting nitrate perception into a measurable optical signal. Because these sensors operate within intact plants, they report nitrate availability as integrated through plant uptake and physiology rather than through chemical extraction alone. Using this platform, we tracked nitrate levels in agricultural soils and identified a minimal microbial consortium capable of converting atmospheric nitrogen into plant-available nitrate. This plant-based sensing strategy enables direct monitoring of nitrogen dynamics in soils and microbial environments, providing a platform for identifying microbial communities that enhance nitrogen availability for crops.

Why it matches plant phenotyping methods植物を用いた遺伝子 encoded センサーを開発し、硝酸可給性を定量する光学的表現型取得法として検証・応用しているため、植物フェノタイピング手法が中心である。

abstractHere, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate.

Evaluation of Sugar Content and Yield in Sugar Beet Based on UAV Multi-Sensor →

Sugar beet is a major sugar crop in temperate regions and rapid, high-throughput, and accurate estimation of field phenotypes is essential for variety selection and production optimization. In this paper, ten commercial sugar beet varieties adapted to high latitudes are investigated using unmanned aerial vehicle (UAV) based red-green-blue (RGB), multispectral, and thermal infrared imaging across multiple growth stages. Canopy structural, texture, spectral, and temperature features are extracted, and three machine learning algorithms, random forest (RF), partial least squares (PLS), and support vector machine (SVM), are used to predict sugar content, root fresh weight, and yield. The results show that all three methods estimate sugar content well, with relative root mean square error (rRMSE) values below 11.0%, while RF and PLS outperform SVM. Multispectral features provide higher accuracy than RGB features, and multi-sensor feature combinations generally improve sugar content prediction compared with single-sensor inputs. For root fresh weight, SVM slightly outperforms RF and PLS, and RGB features are more informative than multispectral features. The integration of thermal infrared features does not notably improve RF or PLS models, but the combination of multispectral and thermal infrared features achieves the best SVM performance ( R2=0.58, RMSE = 75.3 g, and rRMSE = 23.7%). For yield estimation, RF achieves the highest accuracy, with rRMSE values ranging from 15.4% to 18.8%. Yield prediction accuracy increases as the time of image acquisition approaches harvest, and combining multi-temporal data from periods close to harvest further improves model performance. Overall, multi-sensor UAV data can effectively estimate sugar content, root fresh weight, and yield in sugar beet, providing a useful approach for phenotypic analysis, precision management, and variety selection.

Why it matches plant phenotyping methodsUAVマルチセンサー画像から糖含量、根 fresh weight、収量という植物形質を抽出・推定し、センサー特徴量と機械学習モデルの性能を比較しているため、表現型取得・推定手法が中心である。

abstractrapid, high-throughput, and accurate estimation of field phenotypes is essential for variety selection and production optimization.

Hybrid Modeling of Cercospora Leaf Spot Epidemiology: Integrating Mechanistic and Machine Learning Approaches Using Remote-Sensing and Environmental Data. →

Despite advances in modeling and sensing, no study has previously integrated mechanistic, meteorological, and uncrewed aerial vehicle (UAV) data into a unified predictive framework for Cercospora leaf spot. From 2020 to 2022, field trials with a susceptible variety under contrasting fungicide regimes and artificial inoculation were monitored for disease severity, airborne inoculum, and yield. Significant treatment differences emerged 44 days after sowing, with incubation lasting 7 to 12 days and spore peaks occurring from day 77, preceding rapid severity increases. Dissemination showed no prevailing direction but was favored by light, variable winds under conducive microclimates. Yield loss reached up to 0.0123 kg root fresh weight per plant per severity point, and both yield and sugar content decreased with earlier onset and higher final severity. Hybrid models were implemented at multiple levels, integrating multisource data. Severity was best predicted by climatic variables with UAV spectral-structural indices; fructification by humidity-temperature thresholds with stress traits; dissemination by wind-variability metrics with sporulation indicators; and yield and sugar content by UAV indices supplemented with mechanistic covariates. High-level hybridization reduced the root mean square error to 0.615 (on a 0 to 10 severity scale), 0.067 ng of Cercospora beticola DNA for actual spores, 2.033 ng for cumulative spores, 1.769° for dissemination direction, 0.015 ng day -1 for dissemination magnitude, 0.235% for sugar content, and 0.051 kg plant -1 for root fresh weight, achieving up to a 39% improvement over lower-level configurations. These results enhance disease prediction, improve the understanding of disease epidemiology, and could support more effective plant disease management. [Formula: see text] Copyright © 2026 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Why it matches plant phenotyping methodsUAVのスペクトル・構造指標と機械学習・機構モデルを統合し、植物病害重症度、収量、糖含量などを予測する手法を開発・評価しており、表現型取得・推定が研究の中心である。

abstractHybrid models were implemented at multiple levels, integrating multisource data.

Integrating Plant Height into Hyperspectral Inversion Models for Estimating Chlorophyll and Total Nitrogen in Rice Canopies →

Rice undergoes rapid growth and exhibits a high demand for nutrients during the tillering and booting stages. SPAD readings, which reflect relative leaf chlorophyll status, and leaf nitrogen content (LNC) are key indicators of plant nutritional status, directly influencing photosynthetic efficiency and biomass accumulation, while plant height (PH) reflects canopy structure and nutrient availability. Establishing quantitative relationships among these traits at key growth stages is essential for stage-specific precision rice management. In this study, Unmanned Aerial Vehicle (UAV) hyperspectral imagery and ground-truth measurements of SPAD, LNC, and PH were collected from rice fields in Qingbaijiang District, Chengdu, China. Twelve vegetation indices (VIs) were calculated, and three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR)—were employed to develop stage-specific retrieval models. A stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages. The optimal models for SPAD readings and LNC achieved R2 values of 0.916 and 0.936, respectively. The results indicate that integrating canopy structural information with hyperspectral features can improve the estimation accuracy of SPAD-related chlorophyll indicators and nitrogen status in rice. Under the controlled field conditions of this study, the proposed framework provides a plot-scale proof-of-concept demonstration for UAV-based stage-specific nitrogen monitoring.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習を統合し、イネのクロロフィル指標・窒素状態を推定する段階別フェノタイピング手法を開発・評価しており、方法が研究の中心である。

abstractA stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages.

Editorial: Innovative approaches in remote sensing for precise crop yield estimation: advancements, applications, and future directions →

Agriculture currently faces the dual pressures of ensuring global food security and adapting to rapid climate change. To cope with these challenges, researchers have introduced several modern mechanization technologies, including advanced farm machinery, autonomous navigation systems, artificial intelligence, sensing technologies, and communication tools, to enhance productivity and sustainability (Syed et al., 2025a). These technologies enable data-driven decision-making by allowing continuous, large-scale acquisition and analysis of crop and environmental information. Consequently, accurately predicting crop yields and monitoring plant health in real time have become critical prerequisites for precision agricultural management (Syed et al., 2025). Traditional measurement methods-often labor-intensive, destructive, and spatially limited-are increasingly unable to meet the demands of modern large-scale farming. In this context, the integration of Remote Together, these ten contributions illustrate the maturation of agricultural remote sensing, moving towards models that are not only more accurate but also lighter, more interpretable, and more resilient to environmental noise. By combining satellite and UAV data with advanced computational models, these innovative approaches are paving the way for a more resilient and productive global food system. Future research will increasingly focus on improving the precision of crop yield estimation models through multi-dimensional analyses. As agricultural environments grow more complex, integrating AI-powered models with multi-sensor fusion technologies will be essential. Innovations such as lightweight neural networks and multimodal cross-attention frameworks will enable the detection of small, occluded, and densely packed targets with greater accuracy, thereby refining crop-specific metrics such as photosynthetically active radiation (FPAR) and nitrogen content. This, in turn, will enhance crop health monitoring and yield predictions.Additionally, UAV-based remote sensing, combined with multitier feature selection, will improve nitrogen content analysis in crops such as cotton, while image dehazing models and light-use efficiency frameworks will bolster biomass estimation.Emerging technologies such as the Ta-YOLO framework will further optimize small fruit detection in dense canopies, advancing overall crop detection accuracy.A key challenge lies in adapting these models to handle real-world complexities, such as variable environmental conditions. Future work will focus on improving the robustness of these models through dynamic coding networks and performance optimization, ensuring they can operate in heterogeneous agricultural environments.Interdisciplinary collaboration between agriculture, AI, and remote sensing experts will accelerate the development and deployment of these approaches, paving the way for more efficient crop yield estimation systems that are critical for ensuring food security and sustainable agricultural practices.

Why it matches plant phenotyping methods作物収量・健康・バイオマス・窒素含量などの植物形質を、衛星・UAVリモートセンシングと計算モデルで推定する手法群を中心に扱う編集レビューであり、方法論的役割が明確。

titleInnovative approaches in remote sensing for precise crop yield estimation: advancements, applications, and future directions

Measuring forest inventory attributes using Faro Orbis Mobile laser scanner in managed boreal forests →

Abstract Mobile laser scanning (MLS) provides detailed point cloud reconstructions of forest environments and has potential for operational forest sample-plot surveying. This study evaluated the accuracy of MLS in deriving forest inventory attributes, including basal area (G), number of trees per hectare (TPH), total stem volume (V), basal area-weighted mean tree diameter (Dg) and height (Hg), and dominant height (Hdom). Experiments were conducted in managed boreal forests across 44 sample plots (370–2000 m2) using a Faro Orbis MLS system. Field measurements collected tree-by-tree (n = 4472) with callipers and clinometers during the previous summer served as reference data. We compared two alternative MLS data acquisition trajectories—closed loops (MLS-loop) and line transects (MLS-line)—and two processing workflows: (i) manually assisted tree detection followed by automatic tree measurements, and (ii) a fully automatic workflow. MLS-line provided similar or marginally improved accuracy compared with MLS-loop; however, the substantially shorter acquisition time of MLS-loop (19.0 min per plot on average) favoured its operational use over MLS-line (30.5 min). Clearer differences emerged between processing workflows. The fully automatic workflow identified and measured 74.1% of trees with diameter at breast height (DBH) > 5 cm, whereas manual assistance in tree detection increased this proportion to 97.1%. DBH accuracy was similar for both workflows (root-mean-square-error [RMSE] ≈ 2.4 cm), but tree-height estimates were substantially less accurate under automatic processing (RMSE 6.2 m) than under the assisted workflow (RMSE 2.1 m). These differences propagated to plot-level estimates. Using the automatic workflow, RMSEs were 4.2 m2/ha for G, 610 trees/ha for TPH, 29.3 m3/ha for V, 2.3 cm for Dg, 1.6 m for Hg, and 1.9 m for Hdom. The assisted workflow notably improved accuracy, yielding RMSEs of 3.5 m2/ha for G, 54.0 trees/ha for TPH, 20.2 m3/ha for V, 1.2 cm for Dg, 1.3 m for Hg, and 1.2 m for Hdom when using closed-loop trajectories. Overall, the results emphasize the importance of assisted workflows for attributes sensitive to detection completeness, particularly TPH, while showing that kinematic MLS can efficiently capture forest structure for sample plot measurements.

Why it matches plant phenotyping methodsMLSによる樹木・林分構造形質の抽出精度を、取得経路と処理ワークフロー間で比較検証しており、フェノタイピング手法が中心である。

abstractThis study evaluated the accuracy of MLS in deriving forest inventory attributes, including basal area (G), number of trees per hectare (TPH), total stem volume (V), basal area-weighted mean tree diameter (Dg) and height (Hg), and dominant height (Hdom).

Estimation of individual tree aboveground biomass of genetically diverse Catalpa bungei based on nonlinear mixed-effects models and UAV LiDAR data. →

Introduction Accurate estimation of individual tree aboveground biomass (AGB) is essential for tree species selection, carbon accounting, and precision forestry. Unmanned aerial vehicle (UAV) LiDAR provides rapid access to detailed tree structural information, offering a promising tool for high-frequency biomass assessment. Methods In this study, a nonlinear mixed-effects (NLME) model integrating UAV LiDAR and field measurements was developed to quantify the influence of genetic heterogeneity and environmental factors on AGB estimation of Catalpa bungei . Data from 2,941 trees across 79 genotypes were collected in Henan Province, including LiDAR-derived tree height ( L H ), LiDAR-derived crown diameter ( L CD ), and AGB. By incorporating genotype as a random effect and planting density as a dummy variable, the NLME model significantly outperformed traditional dummy-variable models. Results Genotype effects explained significant AGB variation, achieving high accuracy (R²=0.7916, RMSE = 3.7095) and reducing TRE by 23.29% compared to the basic power function model. Leave-one-genotype-out cross-validation confirmed robustness. Calibration with the four largest trees yielded the best performance (TRE = 13.09%), while a simplified scheme using only two trees per genotype maintained high accuracy (TRE = 13.24%), markedly reducing field effort. Discussion These results highlight the superiority of NLME AGB models over linear approaches and demonstrate that accounting for genotype effects is critical for reliable biomass estimation. The proposed framework provides an efficient and cost-effective solution for biomass monitoring, tree breeding, carbon sink assessment, and precision forestry.

Why it matches plant phenotyping methodsUAV LiDARと非線形混合効果モデルを統合し、個体樹木の地上部バイオマスという植物形質を推定する方法を開発・検証しており、フェノタイピング手法が中心である。

abstracta nonlinear mixed-effects (NLME) model integrating UAV LiDAR and field measurements was developed to quantify the influence of genetic heterogeneity and environmental factors on AGB estimation of Catalpa bungei

Full-Spectrum Hyperspectral Modeling of Leaf Dry Matter Content Using a Stacked Ensemble Framework. →

The objective of this study was to assess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data. The dataset encompassed leaves from multiple crops, including potatoes, beans, wheat, maize, peas, tomatoes, basil, and cucumbers, collected under varying growth conditions, cultivation systems, seasonal contexts, and developmental stages. As an initial benchmark, commonly used narrow-band spectral indices and their combinations were evaluated, but they exhibited limited predictive performance for dry matter content. Consequently, several full-spectrum machine learning models were trained and compared to assess their individual predictive ability. Given their complementary strengths, these models were integrated into a stacked ensemble framework to enhance overall accuracy. The resulting ensemble, combining the outputs of multiple base learners through a meta-learner, achieved a coefficient of determination of R2=0.896 on an independent test set, outperforming all individual models. The findings highlight the potential of a multi-model stacking approach to improve the accuracy and robustness of leaf biochemical property estimation from hyperspectral data.

Why it matches plant phenotyping methodsハイパースペクトル反射データから葉乾物含量を推定する機械学習手法を開発・比較・検証しており、植物形質の取得方法が研究の中心である。

abstractassess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data

Multi-Platform LiDAR Comparative Assessment for Above-Ground Biomass and Carbon Estimation in Mediterranean Woody Crops →

Reliable aboveground biomass (AGB) estimates for woody crops are required for carbon accounting and MRV; however, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed orchards. We benchmarked four LiDAR modalities across three Mediterranean woody-crop sites in Córdoba (Spain), IFAPA, Doña María, and Villaseca using open national airborne laser scanning (PNOA/ALS), Riegl ALS, unmanned laser scanning (ULS), and mobile laser scanning (MLS). The field inventory used 58 fixed-area plots (20×50 m; 0.1 ha) collected in December 2024-January 2025 (1,867 trees) and species-specific allometries based on D2r to derive tree and plot AGB; carbon was computed using wood carbon fractions (0.445 olive; 0.457 almond) and CO2e via IPCC conversion. Plot-level LiDAR metrics (e.g., mean height, p95, maximum height, and cover proxies) were extracted from normalized point clouds and modeled with Random Forest, XGBoost, and an ensemble under an 80/20 train-test split. Mean field AGB differed among sites (33.89, 30.94 and 12.76 Mg ha−1 for Villaseca, Doña María, and IFAPA). In the provided summaries, XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). The results support cross-platform LiDAR for orchard AGB mapping and identify conditions under which open national LiDAR can enable scalable MRV. In addition, we evaluated TreeQSM-based quantitative structure models (QSMs) as an independent tree-level 3D reconstruction approach and examined their site-dependent agreement with field inventory estimates.

Why it matches plant phenotyping methods複数のLiDARモダリティと解析手法を比較・ベンチマークし、樹木およびプロットの地上部バイオマスを推定する技術評価が研究の中心であるため。

abstractWe benchmarked four LiDAR modalities across three Mediterranean woody-crop sites

Field-based hyperspectral characterization of wetland plant diversity and vitality in Burullus Lagoon (Nile Delta, Egypt) →

Burullus Lagoon, situated in the Nile Delta of Egypt, is a Ramsar-listed wetland of high ecological importance, particularly in relation to its floristic diversity. This study presents a field-based hyperspectral characterization of wetland vegetation with the objective of establishing a reference spectral library to support biodiversity assessment and environmental monitoring. Hyperspectral reflectance measurements were obtained for 41 plant species selected from a total of 63 floristically identified taxa, based on ecological dominance, spatial recurrence across sampling sites (≥3 stands), and suitability for reliable field spectral acquisition. Spectroscopic data were collected from 44 stands representing lagoon shores, islets, and open-water habitats using an ASD FieldSpec spectroradiometer covering the 350–2500 nm spectral range. A set of vegetation indices was applied to evaluate key biophysical and biochemical properties associated with plant vitality, water status, and biomass. The results indicate that the red and near-infrared regions provide the highest discriminatory capability among species, whereas the shortwave infrared region exhibits more limited discriminatory capability. Dominant taxa, including Phragmites australis and Atriplex halimus , displayed elevated near-infrared reflectance, consistent with differences in canopy structure and biochemical composition. Most species showed vegetation index responses broadly indicative of healthy physiological conditions, although interspecific variability suggests contrasting stress responses among taxa. Overall, the study demonstrates the applicability of field-based hyperspectral data for species-level discrimination in wetland environments and delivers a curated spectral library to support biodiversity conservation and long-term ecosystem management at Burullus Lagoon.

Why it matches plant phenotyping methods野外ハイパースペクトル計測とスペクトルライブラリ構築が研究の中心で、植物の活力、水分状態、バイオマスなどの状態推定に用いているため、単なる生態調査ではなく植物表現型計測への実質的応用に該当する。

abstractThis study presents a field-based hyperspectral characterization of wetland vegetation with the objective of establishing a reference spectral library to support biodiversity assessment and environmental monitoring.

UAV-Based Remote Sensing and Artificial Intelligence for Climate-Smart Agriculture: A Systematic Review of Technologies, Analytics, and Applications in Smallholder Systems →

Unmanned aerial vehicle (UAV) remote sensing has evolved from experimental imaging into an operational diagnostic infrastructure supporting climate-smart agriculture through high-resolution, flexible, and timely crop observation. This review synthesizes advances in UAV platforms, multisensor payloads, artificial intelligence (AI) analytics, and multisource data fusion to evaluate their combined potential for monitoring heterogeneous smallholder systems. A PRISMA-guided analysis of 59 studies (2013–2024) classified sensing architectures, analytical approaches, and application domains across diverse agroecological contexts. Integrated UAV–AI frameworks improve detection of crop stress, yield variability, biomass distribution, and phenological dynamics compared with conventional monitoring, particularly when multimodal sensor data are fused with satellite and ground observations. Predictive performance and diagnostic reliability increase when spectral, thermal, and structural datasets are analyzed jointly using machine-learning or deep-learning models. However, scalability remains constrained by operational, infra-structural, and regulatory factors, especially in resource-limited systems. These findings demonstrate that integrated sensing–analytics systems form a critical foundation for scalable climate-smart agricultural transformation and data-driven decision support across farm, landscape, and institutional scales.

Why it matches plant phenotyping methodsUAVセンシングとAIによる作物ストレス、収量変動、バイオマス、フェノロジーの観測・推定技術を体系的にレビューしており、植物形質・状態の取得方法が中心である。

abstractThis review synthesizes advances in UAV platforms, multisensor payloads, artificial intelligence (AI) analytics, and multisource data fusion

Declining grassland canopy height in China under asymmetric biomass allocation. →

Grassland canopy height is one of the most important traits for determining plant diversity and community structure, directly affecting the resource use efficiency of livestock in grassland ecosystems. However, broad-scale changes in grassland canopy height are seldom reported due to the complex effects of species aggregation on both interspecific and intraspecific structures. Here, we decouple grassland aboveground biomass into vertical and horizontal allocations, thereby offering a pathway to mirror changes in grassland canopy height. Grassland aboveground biomass is estimated using a machine learning algorithm by fusing climatic factors, satellite-driving metrics, and 8-year consecutive ground-truth surveys; the horizontal allocation of grassland aboveground biomass is derived from optimized linear spectral mixture analysis. We find that changes in horizontal biomass allocation primarily accounted for increases in Chinese grassland aboveground biomass from 2001 to 2022, resulting in a significant decline in grassland canopy height. The decline in grassland canopy height is shaped by reduced radiation and, more importantly, by the combined effects of warming and grazing, while also being related to variations in plant diversity. The dwarfing grassland community with declining canopy height may increase the impact of livestock disturbances, thus diminishing the resistance of grassland ecosystems to climate fluctuations.

Why it matches plant phenotyping methods衛星指標、機械学習、地上調査、スペクトル混合解析を組み合わせて草地の canopy height という植物群落形質を推定する方法が研究の中心的要素であり、単なる routine 測定ではない。

abstractHere, we decouple grassland aboveground biomass into vertical and horizontal allocations, thereby offering a pathway to mirror changes in grassland canopy height.

Predictions of wheat phenotypic variability by integrating high-throughput phenotyping observations into a crop growth model. →

Accurate prediction of phenotypes across genotypes and environments is crucial for accelerating crop improvement. Process-based crop growth models (CGMs) can capture complex genotype-by-environment interactions, but their use is limited by labor-intensive genotypic parameter measurements. Here, we developed a faster data assimilation pipeline integrating high-throughput phenotyping (HTP) observations with the SiriusQuality wheat model to efficiently estimate key genotypic parameters and predict genotype performance. Using time-series RGB imagery from a ground-based Phenomobile, we assimilated intercepted photosynthetically active radiation (fIPAR), heading date, and final grain yield to jointly assimilated to calibrate twelve genotypic parameters governing phenology, canopy development, light interception, biomass accumulation, and grain filling. Two data assimilation strategies—a Bayesian DREAM (zs) algorithm and a lookup table (LUT) inversion—were compared through both in silico experiment and eight years of multi-environment field trials of nine durum wheat cultivars. The LUT method demonstrated superior computational efficiency, with prediction accuracy comparable to Bayesian inference on real field data. Multi-year field trials showed that two environments (year / site) were sufficient to reliably characterize genotypic parameters and predict performance across environments. By combining time-series HTP data with ecophysiological modeling, our data assimilation pipeline offers breeders a powerful tool for genotype characterization. It streamlines the process of capturing environmental variance and phenotypic stability, reducing time and effort in crop improvement.

Why it matches plant phenotyping methodsHTP画像を作物成長モデルへ統合するデータ同化パイプラインを開発し、複数アルゴリズムと実圃場データで性能比較・検証しており、表現型取得・推定手法が研究の中心である。

abstractHere, we developed a faster data assimilation pipeline integrating high-throughput phenotyping (HTP) observations with the SiriusQuality wheat model to efficiently estimate key genotypic parameters and predict genotype performance.

What drives yield formation in sugar beet? Quantifying functional components across genotypes and irrigation managements →

Improving sugar beet yield under variable environmental conditions requires a detailed understanding of the physiological mechanisms that drive yield formation. In sugar beet, canopy development determines resource capture, while radiation use efficiency (RUE) regulates the transformation efficiency of primary resources, and assimilate partitioning regulates the allocation of dry matter to the storage root. High-throughput phenotyping offers opportunities to quantify these physiological processes across diverse environments and genetic backgrounds, thereby identifying key traits for yield improvement. A scalable drone-based pipeline was established and validated to estimate physiological yield components – leaf area index (LAI), radiation interception efficiency (RIE), RUE, and harvest index (HI). Unmanned Aerial Vehicle (UAV)-derived multispectral imagery, combined with environmental records and harvest measurements, was used across more than 1300 field plots in Germany and Italy (2023–2024), covering three contrasting environments, two irrigation managements, and up to 171 genotypes. LAI estimation was calibrated and validated under different water regimes in northern Germany (mean absolute error, MAE = 0.30 m² m⁻²). Dynamic UAV-based LAI enabled continuous estimation of radiation interception and biomass accumulation. Total dry matter correlated strongly with cumulative effective (temperature-dependent) radiation interception (R² = 0.81), indicating a comparatively stable RUE across diverse conditions. Genotypic variation in yield formation was mainly driven by canopy-level processes: RIE accounted for 65 % of variation under water-limited conditions, while RUE accounted for 46 % under irrigation. Partitioning traits (HI and Sugar HI) contributed minimally in both irrigation managements. The results highlight the dominant role of canopy development and radiation use in sugar beet yield formation under contrasting environmental conditions. The proposed UAV-based framework provides a transferable, high-throughput approach to quantify physiological yield drivers in field settings. This enables targeted trait selection for breeding and facilitates integration of functional yield components into crop improvement strategies.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるLAI等の生理的形質推定パイプラインを構築・検証しており、フェノタイピング手法が研究の中心である。

abstractA scalable drone-based pipeline was established and validated to estimate physiological yield components – leaf area index (LAI), radiation interception efficiency (RIE), RUE, and harvest index (HI).

Non-destructive monitoring method for protected-lettuce yield using deep learning →

Accurate acquisition of phenotypic characteristics in protected crops is a crucial prerequisite for intelligent control and digital breeding in greenhouses. To accurately assess the phenotypic traits of protected lettuce, a specialized in situ phenotypic detection method has been developed. The Multimodal Features and Attention Mechanism for Phenotype Detection Model (MFAMNet) was developed for protected lettuce, employing a segmented multi-source image dataset for synchronous regression testing. The results revealed that the predicted values generated by MFAMNet exhibited a strong correlation with the measured values, achieving coefficients of determination of 0.96, 0.92, 0.95, 0.94, and 0.95 for plant height, crown width, leaf area, fresh weight, and dry weight, respectively. Ablation tests demonstrated that the deep learning detection framework based on multi-modal feature fusion significantly outperformed single-feature detection models, highlighting the advantages of integrating diverse data modalities. In addition, the multi-modal feature attention mechanism (MMF) facilitates both inter-modality and intra-modality interactions by capturing the global correlations between modalities and employing dynamic sparse spatial attention. The effectiveness of MMF has been validated through comparative experiments, demonstrating its suitability for the phenotypic detection of artificially cultivated lettuce. In summary, the method proposed in this study facilitates real-time monitoring of facility crops, enabling precise control of environmental parameters in protected agriculture and optimizing resource allocation. This approach contributes to the development of a comprehensive intelligent agriculture system and establishes a foundation for unmanned farms.

Why it matches plant phenotyping methodsレタスの草丈、株幅、葉面積、 fresh weight、dry weightを推定するマルチモーダル画像ベース手法を開発し、実測値との比較およびアブレーション・比較実験で検証しており、フェノタイピング手法が研究の中心である。

abstracta specialized in situ phenotypic detection method has been developed

A high-performance biocompatible biomass-based fish gelatin organohydrogel strain sensor for long-term accurate plant growth monitoring →

Organohydrogel-based plant strain sensors hold significant potential for enabling accurate and real-time monitoring of plant growth processes. However, existing strain sensors typically face challenges such as inferior biocompatibility, trade-off between sensing performance and mechanical properties, as well as poor long-term stability, leading to inaccurate monitoring and plant tissue damage and thus hindering their practical applications. Herein, we propose a synergistic metal ion and multiple hydrogen bond dual crosslinking strategy to develop a biomass-based fish gelatin organohydrogel as a strain sensing material. The resultant organohydrogel simultaneously exhibits excellent mechanical properties (Young's modulus of 99.9 kPa and strong adhesiveness of 60 kPa), high sensing performance (GF = 2.13, stable response across a wide temperature range from -80 °C to 25 °C), outstanding plant tissue and human cell biocompatibility, and long-term stability (over 5000 loading-unloading cycles under 100% strain), demonstrating superior overall performance to most existing organohydrogels. To harness these unique material performances, we fabricate a sandwich-structured plant strain sensor for long-term monitoring of plant growth. The fabricated strain sensor enables successful real-time monitoring of the growth dynamics of lotus stems and pomelo fruits with high accuracy and long-term stability up to three weeks. Our novel design strategy of high-performance organohydrogels enables high-fidelity plant growth monitoring, unlocking new potentials for advancing data-driven smart and precision farming practices.

Why it matches plant phenotyping methods植物成長を長期・リアルタイムに測定するひずみセンサーの材料設計、性能評価、植物での検証が研究の中心であり、植物フェノタイピング手法に該当する。

abstractwe propose a synergistic metal ion and multiple hydrogen bond dual crosslinking strategy to develop a biomass-based fish gelatin organohydrogel as a strain sensing material

Fusing UAV multiple data and phenology to predict crop biomass →

Robust quantification of crop status in real-time is essential for agile decision-making. While use of unmanned aerial vehicle data (UAV) appears promising in this vein, the contribution and transferability of various features (e.g. vegetation indices, plant height and texture features) in crop above-ground biomass (AGB) prediction remain poorly understood. Here, our objectives were to (1) evaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features, (2) elicit the contribution of various UAV features, (3) assess the transferability of features across growth stages and sites. Four field experiments, incorporating several water and nitrogen treatments across two sites, were assembled for use in AGB prognostics. We invoked four ML algorithms—Random forest (RF), Lasso regression (LR), K-nearest neighbors (KNN) and a stacked ensemble integrating the three methods (SML)—to predict wheat AGB using multiple UAV data and phenological information. Additionally, interpretable ML techniques were employed to elucidate the influence of UAV features on AGB prediction across growth stages. Our results showed that all algorithms exhibited robust performance in predicting wheat biomass, with RMSE values of 1.64, 1.71, 1.71, and 1.57 Mg ha −1 for RF, LR, KNN, and SML, respectively. RF predominantly relied on plant height features, LR leveraged vegetation indices, and KNN prioritized texture features, while SML synthesized the advantages of multiple ML algorithms. Fusion of multiple datasets amplified model prognostic capacity and scalability, with R 2 and rRMSE of 0.92 and 22 % when using data from external sites. Features pertaining to vegetation indices and plant height during vegetative growth and around flowering had seminal contributions of model predictions. Texture features significantly reduced the saturation effect during the reproductive stage but diminished the model’s transferability during the vegetative stage. Complementarity among data types enhanced effectiveness of ensemble machine learning, which leverages strengths of diverse data to improve the accuracy and robustness of AGB predictions. Future studies could combine multiple sources of remote sensing, such as LiDAR and thermal infrared alongside system modeling, to improve ML accuracy and generalization capability.

Why it matches plant phenotyping methodsUAV由来の植物高・植生指数・テクスチャ等から小麦バイオマスを推定する機械学習手法を比較・検証し、異なる生育段階や圃場への転移性も評価しており、表現型推定法が中心である。

abstractevaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features

Innovative photosynthesis model twinning after intelligent interpretation of complex sensor analytics →

Accurate canopy photosynthesis modeling is essential for understanding and optimizing crop growth and yield in greenhouse agriculture. Current models have limited predictive capability due to inadequate responsiveness to dynamic environments and delays in parameter acquisition, making accurate predictions challenging under the complex conditions of solar greenhouses. This study aimed to develop a dynamic canopy photosynthesis model for greenhouse tomatoes, leveraging an IoT sensor network for real-time biological feedback and parameterization. By integrating real-time monitoring with dynamic feedback, the model facilitates precision management of greenhouse tomato cultivation, thereby optimizing plant growth, resource use efficiency, and yield predictability. To achieve this, a non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R² ≥ 0.94) and the photosynthetic leaf area index (LAIₚ, R² ≥ 0.91), continuously providing parameters for updating modelling (validated against destructive sampling and actual measurements for trait specifics). Based on accurate parameter acquisition, a dynamic canopy photosynthesis model was developed using LAIₚ as the core variable, integrating above-canopy radiation. A newly developed parameter, which integrates the radiation component of transpiration, serves as a key factor for estimating photosynthesis. This innovative approach allows for accurate daily prediction and assessment of assimilated biomass. Experimental results from 2022 and 2023 showed that the LAIₚ model performed better than the comparison model, showing higher accuracy and adaptability (R² = 0.87 and 0.89, NRMSE = 0.17 and 0.12 vs. R² = 0.70 and 0.80, NRMSE = 0.26 and 0.15). These results confirmed the reliability of the integrated modeling framework, which forms a closed-loop system connecting real-time plant monitoring, statistical parameter inversion, online model adaptation, and biomass feedback verification. This modeling approach provides a solid foundation for precise growth simulation, sustainably improving yield and quality in solar greenhouse tomatoes, and advancing digital twin-enabled intelligent production.

Why it matches plant phenotyping methods植物キャノピーのLAIおよび光合成LAIを非破壊・連続推定するセンサー／逆解析法を開発し、破壊サンプリング等で検証している。植物形質取得とモデル連携が研究の中心である。

abstracta non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R² ≥ 0.94) and the photosynthetic leaf area index (LAIₚ, R² ≥ 0.91)

Advancing fire regime assessment in the Indian Himalayan forests using GEDI LiDAR →

Wildfires are the leading cause of forest degradation and reduce the carbon sequestration capacity of forest ecosystems. As climate change intensifies fire regimes, accurately mapping burn severity and biomass loss remains difficult, particularly in complex mountainous terrain. Traditional remote sensing approaches based on optical data often fail to capture sub-canopy and structural variations, while field-based methods are constrained by scale, access, and logistical complexity. To address these limitations, this study utilises NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data to capture three-dimensional forest structure and monitor changes in aboveground biomass. Focusing on the Indian Himalayan Region (IHR), we utilised GEDI structural metrics for fire severity classifications derived from NASA’s Fire Information for Resource Management System (FIRMS) data. Spatial fire density mapping revealed that Evergreen Needleleaf (ENL), Evergreen Broadleaf (EBL), and Deciduous Broadleaf (DBL) plant functional types (PFTs) are particularly vulnerable to recurring fires. The GEDI Level 4 A AGBD product, when tested in the IHR, showed a tendency to underestimate biomass relative to regional field studies, indicating the need for localised calibration in rugged terrain. We found that the pre-monsoon fire season resulted in a biomass loss of 14.33 Mg/ha for the ENL PFT, while the EBL PFT showed a biomass gain of 3.15 Mg/ha. A Random Forest model utilising GEDI structural inputs for fire severity classification achieved an overall accuracy of 85 %. We found that vegetation structural complexity, rather than canopy height, is a more important predictor of high fire severity. We also evaluated the Waveform Structural Complexity Index (WSCI), a novel GEDI metric that effectively characterised fire severity across the under-storey vegetation. These findings underscore the importance of incorporating 3D vegetation structure into fire impact assessments, moving beyond the limitations of conventional two-dimensional optical datasets.

Why it matches plant phenotyping methodsGEDI LiDARによる森林構造・バイオマス損失・火災重症度の推定を中心に、既存AGBD製品の地域較正と新規指標WSCIの評価を行っており、植物状態の取得・推定手法が中核である。

abstractwe utilised GEDI structural metrics for fire severity classifications

Physiological, vegetation, and biochemical indicators for biomass, nitrogen uptake and lint yield forecast in cotton →

Forecasting crop performance through non-destructive tools is crucial for enabling timely, data-driven decisions for sustainable cotton production. This study evaluated physiological, PIs [chlorophyll index (CI), nitrogen balance index (NBI)], vegetation, VIs [normalized difference vegetation index (NDVI), normalized difference red edge (NDRE)], and biochemical, BIs [leaf nitrogen, petiole nitrate-N] indicators to forecast aboveground biomass accumulation (AGB), nitrogen (N) uptake, and lint yield in cotton. We hypothesized that the predictive strength of each indicator would vary by type and growth stage. Field experiments were conducted across five site-years in West and North Florida, USA, using six N rates (0–252 kg N/ha) in four replications. Quadratic regression (QR) model identified PIs at peak flowering and VIs at first flower and cutout growth stages as the most robust predictors of AGB. A random forest regression (RF) model also showed similar results, with PIs at peak flowering and VIs at peak flowering and first flower as the best indicators for AGB prediction. A strong relationship of BIs with N uptake at bloom and post-bloom growth stages evaluated through QR and RF supports their use for early reproductive assessment. Lint yield predicted using QR and RF was best forecasted by VIs at peak flowering, and cutout. Principal component analysis confirmed mid-to-late season VIs as major drivers of AGB and lint yield variability, while as early reproductive BIs as major indicators of N uptake. This research establishes a robust framework for real-time indicator and growth stage-based biomass, N uptake, and yield forecasting in cotton.

Why it matches plant phenotyping methods綿花の非破壊的な生理・植生指標を用いて、バイオマス、窒素吸収、収量という植物形質を予測し、複数地点年および回帰モデルで指標の予測性能を評価しているため、測定・予測手法が中心である。

abstractForecasting crop performance through non-destructive tools is crucial for enabling timely, data-driven decisions for sustainable cotton production.

Sorghum plant height and yield prediction using multispectral data and sUAS →

Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic breeding, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, DTMs (digital terrain models), and DSMs (digital surface models). Manual plant height measurements were used for correlation and simple linear regression analysis, while biomass was predicted using random forest regression. Results. DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE= 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE= 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. DTMs and DSMs derived from multispectral imagery predicted plant height accurately in later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into models enhanced biomass prediction. The findings showed that sUAS-mounted sensors and multispectral indices are promising tools for phenotyping in sorghum breeding programs in Costa Rica.

Why it matches plant phenotyping methodssUASマルチスペクトル画像と写真測量からソルガムの草丈・バイオマスを推定し、手測定との相関、回帰、精度評価を行う方法中心の研究である。

abstractTo predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights.

Automatic tree-level based forest inventories retrieval via ultra-high resolution UAV images and deep learning →

Forest inventories play an essential role in managing and protecting forest resources as well as quantifying carbon stocks. Recent advances in Uncrewed Aerial Vehicles (UAVs) have enhanced capabilities for efficiently monitoring forest dynamics across large geographic areas. RGB cameras are typically preferred for rapid and scalable forest inventory missions owing to three distinct advantages, including low cost, ease of use, and high resolution. However, compared with multispectral or hyperspectral sensors, the limited spectral signals of RGB cameras pose challenges for tree crown detection and classification. The ability of deep learning methods to capture structural and contextual cues from imagery helps alleviate some of the limitations of RGB data. In this study, we propose an Individual Tree Crown (ITC)-based framework leveraging UAV data and advanced deep learning models for inventories of individual trees in dense and natural forests. First, we develop the ITC-based Multi-Task Convolutional Neural Network (ITCMNet), which incorporates multi-scale contexts to simultaneously and accurately identify individual tree crowns, discriminate tree species, and assess tree vitality. Second, structural parameters for each individual crown are extracted to estimate forest carbon storage using species-specific allometric models. Unlike conventional pixel-based methods, our proposed ITCMNet enables precise forest investigations at the ITC level, enhancing both performance and interpretability. We collected a comprehensive dataset consisting of 2456 ultra-high resolution (1.6 cm) UAV RGB images and 27,160 labeled trees across 105 plots distributed in three dense forests and one city park in Germany to evaluate our framework. The ITCMNet demonstrated robust tree crown delineation performance, achieving an F1 score of 0.81. Additionally, our method attained an F1 score in species classification (i.e., 0.54 for angiosperms and 0.76 for gymnosperms) and vitality identification (0.66). Utilizing precise tree parameters, species information, and species-specific allometric models, our carbon storage estimation surpassed current satellite-based carbon products. The carbon stock estimation achieved an R 2 of 0.83 and the carbon storage range in the Bamberg forests is approximately 50 to 110 Mg C/ha. These results show that our proposed framework provides detailed, cost-effective forest inventories, highlighting its potential to support various downstream forestry applications. The dataset and source code are available ( https://www.dlr.de/en/eoc/about-us/remote-sensing-technology-institute/photogrammetry-and-image-analysis/public-datasets/bamforests ; https://github.com/WendyFan52/ITCMNet ).

Why it matches plant phenotyping methodsUAV画像と深層学習により個体樹冠を検出し、樹冠構造、樹種、樹勢などの植物状態を抽出する枠組みを開発・評価しており、植物フェノタイピング手法が中心である。

abstractwe propose an Individual Tree Crown (ITC)-based framework leveraging UAV data and advanced deep learning models for inventories of individual trees in dense and natural forests.

A two-phase evaluation system integrating hydroponic and field screening identifies nutrient-efficient sweetpotato ( Ipomoea batatas (L.) Lam.) germplasm. →

Sweetpotato ( Ipomoea batatas (L.) Lam.) is a crucial crop for global food security. However, its sustainable production is hindered by low nutrient use efficiency. Reliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking. To bridge this gap, we established a novel two-phase evaluation system integrating hydroponic seedling screening with multi-nutrient field validation. We conducted principal component and regression analyses of 35 germplasms lines under controlled deficiencies of nitrogen (N), phosphorus (P), and potassium (K). Five conserved seedling traits were identified, including leaf number per plant, shoot fresh weight, root fresh weight, shoot dry weight, and net photosynthetic rate (Pn). These traits consistently correlated with tolerance to N, P, or K deficiency, thereby supporting their utility as reliable early indicators of nutrient stress. Field validation further confirmed that storage root fresh and dry weight, nutrient content, accumulation, and use efficiency varied significantly among nutrient treatments and genotypes, serving as key indicators of field performance. This integrated approach successfully identified elite germplasm with specific nutrient use efficiency: XN1985-7 as a low-N-tolerant and N-efficient utilization genotype, XN17104-132 as low-K-tolerant and K-efficient utilization, XN2141-3 as low-P-tolerant and P-efficient utilization, and notably XN2153-5, which exhibited concurrent tolerance to low N, P, and K with broad-spectrum efficiency. Our integrated two-phase framework provides a scalable model for screening nutrient-efficient germplasm in root crops, thereby contributing to sustainable breeding programs.

Why it matches plant phenotyping methods栄養効率遺伝資源を評価するための二段階スクリーニング系を構築し、複数の形態・生理形質を初期指標として検証しているため、植物フェノタイピング手法が中心的です。

abstractReliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking.

Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture. →

Efficient nutrient management is critical for crop growth and sustainable resource consumption (e.g., nitrogen and energy). Current approaches require lengthy analyses, preventing real-time optimization; similarly, imaging facilitates rapid phenotyping but can be computationally intensive, preventing deployment under resource constraints. This study proposes a flexible, tiered pipeline for anomaly detection and status estimation (fresh weight, dry mass, and tissue nutrients), including a comprehensive energy analysis of approaches that span the efficiency-accuracy spectrum. Using a nutrient depletion experiment with three treatments (T1-100%, T2-50%, and T3-25% fertilizer strength) and multispectral imaging, we developed a hierarchical pipeline using an autoencoder for early warning. Further, we compared two status estimation modules of different complexity for more detailed analysis: vegetation index features with machine learning (random forest, RF) and raw whole-image deep learning (vision transformer, ViT). Results demonstrated high-efficiency anomaly detection (73% net detection of T3 samples 9 days after transplanting) at substantially lower energy than embodied energy in wasted nitrogen. The state estimation modules show trade-offs, with ViT outperforming RF on phosphorus and calcium estimation ( R 2 0.61 vs 0.58, 0.48 vs 0.35) at higher energy cost. With our modular pipeline, this work opens up opportunities for edge diagnostics and practical opportunities for agricultural sustainability.

Why it matches plant phenotyping methodsマルチスペクトル画像から植物の異常、重量、組織栄養状態を推定する階層的パイプラインを開発・比較しており、表現型取得・推定法が研究の中心である。

abstractimaging facilitates rapid phenotyping but can be computationally intensive

Drone-Based Phenotyping and its Utilization in Crop Improvement: A Review →

Drone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement. This technology integrates advanced imaging sensors—including RGB, multispectral, hyperspectral, and thermal cameras—with sophisticated image processing and artificial intelligence algorithms to non-destructively capture key phenotypic data such as plant height, biomass, canopy temperature, maturity timing, and disease symptoms under natural field conditions. Compared with traditional manual phenotyping and satellite-based remote sensing, UAV phenotyping offers superior spatial and temporal resolution, enabling dynamic monitoring of complex traits such as drought tolerance and disease resistance. Applications span early stress detection, quantitative trait assessment, yield prediction, and accelerating breeding cycles by facilitating objective, rapid selection of superior genotypes across multiple crop species. Despite its transformative potential, challenges remain in standardizing protocols, managing large-scale complex datasets, integrating phenotypic with genomic and environmental data, and providing training resources for widespread adoption. Ongoing advancements in sensor technology, data analytics, open-source tools, and capacity building are poised to cement drone-based phenotyping as a cornerstone technology for sustainable, climate-resilient crop breeding and global food security.

Why it matches plant phenotyping methodsUAV画像・センサーによる植物形質計測を中心に扱う明示的なフェノタイピングレビューであり、手法の応用、技術、課題を総合的に論じている。

abstractDrone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement.

Comprehensive phenomics and vegetative yield analysis of global kale (Brassica oleracea var. acephala) germplasm in controlled environment agriculture. →

BACKGROUND: Kale (Brassica oleracea var. acephala) is a high value leafy vegetable with an extensive domestication history and germplasm diversity, making it an ideal target for genetic improvement. To meet growing food security needs particularly with controlled environment agriculture (CEA) systems, specialized breeding strategies are required. The goal of this study was to survey the phenotypic architecture of a global kale germplasm collection under commercial CEA conditions. This study establishes a phenotypic baseline and serves as a hypothesis generating resource for future genetic and physiological studies in kale and other leafy vegetables grown under CEA. RESULTS: A total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods. Significant differentiation was observed across all traits, with coefficient of variation ranging from 2.5% to 180.7%, confirming broad genetic variability among accessions. Trait correlation networks and hierarchical clustering grouped phenotypes into seven biologically corresponding modules including leaf, stem and root morphology, plant architecture, hyperspectral indices, and seedling growth. These modules highlight coordinated phenotypic patterns among traits. Integrative yield analyses combining partial least squares variable importance in projection with differential trait analysis identified 28 phenotypes most strongly associated with total aboveground fresh weight, a robust proxy for CEA vegetative yield. Principal component analysis further distilled these traits into three orthogonal components explaining 87.1% of total yield variation. These components represented modules related to plant organ size, canopy structure, and density, emphasizing their biological contribution to harvestable biomass. CONCLUSIONS: This study generates a foundational phenomics resource and comprehensive dissection of kale’s yield architecture under CEA conditions. The composition of traits identified constitutes a targeted set of breeding traits to be further validated for improved leafy vegetable yield. By integrating large-scale germplasm resources with phenomics, this work establishes the utility of a high-throughput phenotypic analysis for further leafy crop research and improvement.

Why it matches plant phenotyping methods大規模なハイスループット植物表現型解析を中核とし、113形質の取得、統合解析、再利用可能なフェノミクス資源の構築を行っているため。

abstractA total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods.

Identification of salt-tolerant henna (Lawsonia inermis L.) germplasm using a fuzzy comprehensive evaluation model at the seed germination stage. →

Soil salinity is a major constraint for cultivating the economically important medicinal and ornamental shrub henna (Lawsonia inermis L.) in arid regions like Kerman Province, Iran. To address the lack of systematic germplasm evaluation for salinity tolerance, this study quantified the responses of ten geographically distinct henna populations to salt stress (0, 50, and 100 mM NaCl) during the critical germination and early seedling stages. A Fuzzy Comprehensive Evaluation (FCE) framework, based on the membership function values (MFV) derived from trait-specific Salt Tolerance Indices (STI), was used to integrate data from multiple germination parameters, e.g., germination percentage (GP), mean germination time (MGT), germination index (GI), germination vigor index (GVI) and seedling growth traits, e.g., radicle length (RL), plumule length (PL), total seedling length (TSL), seedling fresh weight (SFW). Results identified germplasm J-02 as the most salt-tolerant genotype (mean MFV = 0.536), demonstrating exceptional stability in RL (STI = 1.01) and SFW (STI = 0.90) under severe stress (100 mM NaCl). In contrast, K-01 was highly sensitive (mean MFV = 0.322), suffering severe GVI loss (STI = 0.56) and TSL reduction (34.1%) despite superior control performance. Regression analysis identified SFW as the optimal single-trait predictor for overall tolerance (R² = 0.714 at 50 mM; R² = 0.549 at 100 mM). The FCE model effectively resolved genotype-specific trait conflicts, a finding corroborated by principal component analysis (PCA) and hierarchical cluster analysis (HCA) which provided mechanistic insights: elite performers (e.g., J-02) prioritized seedling elongation, while others (e.g., F-01) excelled in germination under moderate stress. This study establishes J-02 as prime germplasm for saline zones and validates the integration of FCE with multivariate analysis for precision phenotyping in henna breeding programs.

Why it matches plant phenotyping methodsFCEモデルと多変量解析による複数形質の統合・塩耐性評価が研究の中心であり、単なる生物学的処理実験を超えた計算的フェノタイピング手法として扱われている。

abstractA Fuzzy Comprehensive Evaluation (FCE) framework, based on the membership function values (MFV) derived from trait-specific Salt Tolerance Indices (STI), was used to integrate data from multiple germination parameters

Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches. →

Optimizing biomass partitioning is essential for achieving sustainable yield improvement in wheat, particularly under increasing environmental stress. Traits such as spike partitioning index (SPI), harvest index (HI), and fruiting efficiency (FE) are central to understanding how assimilates are allocated between vegetative and reproductive organs. However, their complex physiology and the difficulty of manual phenotyping have limited their routine use in breeding programs. This study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat. Three trials of facultative soft wheat lines (2022-2024) and an independent validation set of advanced breeding lines were used to develop genomic prediction (GP), phenomic prediction (PP), and integrated multi-omic models combining genomic, phenomic, and environmental covariates (ECs). Kernel-based best linear unbiased prediction (BLUP), and machine-learning based, random forest regression and partial least squares regression were implemented to estimate predictive ability (PA). Phenomics-driven models markedly outperformed GP across most traits, achieving PA up to 0.61 for SPI, 0.56 for FE, 0.71 for grains/m 2 (GN), and 0.66 for grain yield (GY). Hyperspectral data provided higher accuracy than vegetation indices, and multi-omic integration slightly improved prediction (PA up to 0.73 for GN). These results demonstrate that UAV-based hyperspectral phenotyping can effectively capture canopy-level physiological signals associated with biomass partitioning, offering a scalable and data-driven approach for in-season selections. This can help wheat breeding programs to optimize biomass partitioning in modern wheat cultivars for long-term yield resilience and genetic gain.

Why it matches plant phenotyping methodsUAVハイパースペクトルデータによる作物形質推定と予測モデルの開発・検証が研究の中心であり、単なる生物学的実験の測定ではない。

abstractThis study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat.

UAV-based spatial sampling bridges ground measurements and satellite data for multi-scale estimation of sugar beet aboveground biomass. →

Aboveground biomass (AGB) is a critical indicator for assessing crop growth status and productivity, yet accurately linking fine-scale ground measurements with coarse-resolution satellite imagery remains challenging. Here, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model. Multi-variety sugar beet cultivated across heterogeneous habitats was used as a case study. Results indicate that a power-law model effectively captures the allometric relationships between AGB, plant height, and the Dreg vegetation index in sugar beet, achieving high accuracy and strong transferability. Incorporating phenological information from Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) codes and a thermal index further enhanced model robustness across independent habitat trials, yielding coefficients of determination ( R 2 ) of 0.80 and 0.83. The SS-GMI sampling procedure integrates systematic sampling with Global Moran's I to reduce spatial autocorrelation while ensuring uniform spatial coverage, thereby enabling the acquisition of representative and spatially independent samples from UAV-derived AGB maps. These samples were used to develop satellite-based AGB estimation models for PlanetScope and Sentinel-2A imagery, achieving R 2 values of 0.83 and 0.73, respectively. This study provides a practical and scalable framework for field-to-satellite AGB upscaling, offering new insights for the scale conversion of multi-source data in agricultural remote sensing.

Why it matches plant phenotyping methodsUAV・衛星観測とSS-GMIサンプリング、モデル化を組み合わせ、サトウダイコンの地上部バイオマスという植物形質を推定する統合手法が研究の中心である。

abstractHere, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model.

Root electrical capacitance method for the field monitoring of maize response to elevated carbon dioxide concentration →

Abstract Aims This study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment. Methods A two-year FACE study was conducted with maize grown under ambient and elevated [CO 2 ], and low and high nitrogen supply in three replicate plots. The saturation root electrical capacitance (C R *) was monitored during the plant growth cycle. Aboveground plant parameters were measured in situ at flowering. Results Capacitance measurements revealed a seasonal pattern in root development with a peak at flowering, and the positive effect of higher nitrogen dose and [CO 2 ] enrichment on plant growth. At anthesis, C R * was significantly ( p < 0.001) and linearly correlated with stem basal area (R 2 : 0.51–0.68), aboveground biomass index (basal area × plant height; R 2 : 0.47–0.62) and leaf chlorophyll concentration (R 2 : 0.40–0.56). However, the best correlation (R 2 : 0.73 and 0.74) was found for plant leaf area, which is closely related to root water uptake, suggesting that the applied current signal penetrated the roots, and that the capacitance method directly measured root status in the field. In addition, C R * at flowering was a reasonable early predictor of maize grain yield (R 2 : 0.58 and 0.64) under our experimental conditions. Conclusions The electrical capacitance method proved to be a practical high-throughput tool for phenotyping not only the root but the whole plant in the field. Being noninvasive, it is particularly beneficial in FACE systems, where destructive sampling and soil disturbance should be minimized. It would also provide cost-effective support for breeding stress-tolerant and climate-resilient crops. Graphical Abstract

Why it matches plant phenotyping methods根の電気容量測定を非破壊・高スループットな植物フェノタイピング手法として評価し、圃場での相関および予測性能を検証しているため、方法が研究の中心である。

abstractThis study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment.

Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow. →

BACKGROUND: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction. RESULTS: > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development. CONCLUSIONS: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.

Why it matches plant phenotyping methods低コストの双眼カメラとSfM-MVSによるサフラン葉の器官レベル3D形質取得ワークフローを開発し、精度検証、再現性、パラメータ最適化まで扱っており、植物フェノタイピング手法が研究の中心である。

abstractHere, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.

Diversity of root system architecture and root-shoot biomass allocation in industrial hemp (Cannabis sativa L.). →

Roots are major contributors to nutrient acquisition, water absorption, and plant anchoring and stability. However, little is known about the root system of industrial hemp (Cannabis sativa L.), an increasingly important crop worth $16 billion annually. Hemp is commonly cultivated for grain as an oilseed, stalk biomass for fiber and industrial materials, but has also had growing interest for its carbon sequestration potential due to its reported deep rooting profile. The objectives of this research were to (1) phenotype a panel of 46 industrially-relevant hemp genotypes, (2) quantify the phenotypic differences of shoot and root traits through 2D image analysis, (3) and to investigate genotype grouping strategies and gene targets that could be useful for crop improvement. To phenotype the root system architecture of multiple hemp genotypes representative of production hemp, a large format raised-bed was developed in a greenhouse in which hemp was planted in rows. Root and shoot traits varied across genotypes, with a difference of 175% in total root length between the largest and smallest genotype, and heritability values ranging from 0.51 to 0.88 for key root traits. A strong positive correlation was found between root and shoot biomass (R = 0.93) suggests coordinated resource allocation strategies across genotypes. Of the 46 genotypes studied, two genotypes consistently showed the greatest differences across most of the traits analyzed in the panel. A root-to-shoot quadrant framework was applied to classify hemp ideotypes based on biomass allocation and architectural traits. In addition, comparative genomic analysis identified 74 candidate root architecture genes in hemp that are orthologous to known regulators in maize, rice, and Arabidopsis. These findings highlight substantial phenotypic diversity in hemp root systems and provide a foundation for developing genotype grouping strategies and selecting breeding targets for mapping populations.

Why it matches plant phenotyping methods複数遺伝子型の根系形態を2D画像解析で定量し、温室内の大規模 raised-bed フェノタイピング基盤も開発しているため、植物形質取得が研究の中心である。

abstractTo phenotype the root system architecture of multiple hemp genotypes representative of production hemp, a large format raised-bed was developed in a greenhouse

Feasibility assessment of aboveground biomass and carbon stock estimation in heterogeneous tropical campus green spaces using unmanned aerial vehicle RGB photogrammetry →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsUAV RGBフォトグラメトリを用いて植物群落の地上部バイオマスを推定する技術評価であり、植物形質の取得・推定手法が中心である。

titleFeasibility assessment of aboveground biomass and carbon stock estimation in heterogeneous tropical campus green spaces using unmanned aerial vehicle RGB photogrammetry

Automated Discovery of Multicellular Behavior for Optimized Plant Growth and Climate Resilience →

Developing resilience against climate change and establishing food security will require significant research into the responses of multicellular organisms to their environment. New approaches, such as lab automation, can substantially increase the rate of data collection for organism‐level behavior. This report describes an automated robotic system for studying multicellular organisms to accelerate scientific experimentation. The robot can simultaneously image and deliver chemicals to each organism during multiday experiments, and uses a deep learning model to automatically obtain phenotypic data. This system's abilities are demonstrated by creating a plant growth strategy for food security applications that increases biomass while decreasing nutrient utilization. Furthermore, plant growth is characterized under high salt concentrations to better understand the effects of climate change on freshwater ecosystems. This robotic approach improves lab automation for studying multicellular organisms by increasing experimental throughput, and will enable researchers to improve crop yields under uncertain climates and predict the response of organisms in changing environments.

Why it matches plant phenotyping methodsロボットによる自動撮像と深層学習による表現型データ取得が研究の中心であり、植物の成長・バイオマスを高スループットに評価する表現型解析プラットフォームである。

abstractThe robot can simultaneously image and deliver chemicals to each organism during multiday experiments, and uses a deep learning model to automatically obtain phenotypic data.

3D crop reconstruction: A review of hyperspectral and multispectral approaches →

Hyperspectral imaging (HSI) has emerged as a powerful tool for precision agriculture, enabling the non-destructive monitoring of crop biochemical and physiological traits. However, HSI alone lacks structural context, which limits its ability to accurately capture complex canopy architectures and organ-level traits. Integrating HSI with depth-sensing modalities such as Light Detection and Ranging (LiDAR), Red, Green, Blue, and Depth (RGB-D) cameras, and computational reconstruction technique such as photogrammetry enables the generation of three-dimensional hyperspectral point clouds, combining spectral richness with geometric fidelity. This multi-modal fusion enhances crop trait estimation, including biomass, leaf chlorophyll content, canopy height, leaf area, and stress indicators, while improving the robustness of phenotyping under occlusions, shadows, and varying illumination. Dimensionality reduction, feature selection, and machine learning approaches, including deep learning and explainable AI, are useful for handling high-dimensional hyperspectral data and extracting actionable agronomic insights. Moreover, the integration of thermal, radar, and Global Navigation Satellite System (GNSS) data further expands the capabilities of multi-modal sensing, enabling continuous, all-weather crop monitoring and accurate spatial referencing. Despite these advances, most studies to date focus on controlled environments, highlighting the need for field-based validation to ensure the reliability and scalability of HSI-depth fusion techniques. This review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges, and outlines future directions for implementing high-throughput, real-time phenotyping and precision agriculture solutions.

Why it matches plant phenotyping methods植物形質推定のためのハイパースペクトル・深度センシング融合と3D再構成を中心に扱うレビューであり、フェノタイピング手法の方法論的整理が主題。

abstractThis review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges

3D crop reconstruction: A review of hyperspectral and multispectral approaches →

Hyperspectral imaging (HSI) has emerged as a powerful tool for precision agriculture, enabling the non-destructive monitoring of crop biochemical and physiological traits. However, HSI alone lacks structural context, which limits its ability to accurately capture complex canopy architectures and organ-level traits. Integrating HSI with depth-sensing modalities such as Light Detection and Ranging (LiDAR), Red, Green, Blue, and Depth (RGB-D) cameras, and computational reconstruction technique such as photogrammetry enables the generation of three-dimensional hyperspectral point clouds, combining spectral richness with geometric fidelity. This multi-modal fusion enhances crop trait estimation, including biomass, leaf chlorophyll content, canopy height, leaf area, and stress indicators, while improving the robustness of phenotyping under occlusions, shadows, and varying illumination. Dimensionality reduction, feature selection, and machine learning approaches, including deep learning and explainable AI, are useful for handling high-dimensional hyperspectral data and extracting actionable agronomic insights. Moreover, the integration of thermal, radar, and Global Navigation Satellite System (GNSS) data further expands the capabilities of multi-modal sensing, enabling continuous, all-weather crop monitoring and accurate spatial referencing. Despite these advances, most studies to date focus on controlled environments, highlighting the need for field-based validation to ensure the reliability and scalability of HSI-depth fusion techniques. This review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges, and outlines future directions for implementing high-throughput, real-time phenotyping and precision agriculture solutions.

Why it matches plant phenotyping methods植物形質推定のためのハイパースペクトル・深度センシング・3D再構成手法を中心に扱うレビューであり、フェノタイピング手法レビューに該当する。

abstractThis review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges

Dynamic light intensity improves light use efficiency of lettuce in vertical farming: quantifying light interception through 3D phenotyping analysis →

Vertical farming offers a promising solution to global food security and urbanization challenges, yet its widespread adoption is hindered by high costs, particularly for lighting. Addressing this requires enhancing light use efficiency (LUE) through intelligent control strategies. While numerous studies have investigated the effects of light intensity on lettuce growth, relatively few have explored the potential benefits of stage-specific light regulation. In this study, we first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception. Utilizing this quantitative framework, we conducted a dynamic light experiment with lettuce in a commercial plant factory to evaluate four dynamic light-intensity strategies. The proposed 3D phenotyping pipeline demonstrated promising performance for canopy information extraction, with RMSEs for plant height, canopy diameter, and projected leaf area of 0.79 cm, 1.05 cm, and 44.3 cm², respectively. The “high-low-high” dynamic lighting strategy, applying higher light intensity during the early and late growth stages and lower intensity during the mid-growth stage, successfully optimized canopy morphology for better light capture. This treatment significantly increased shoot fresh and dry weights by 28 % and 65 %, respectively, compared to constant lighting. Furthermore, it enhanced LUE based on incident and intercepted light integrals by 67 % and 19 %, while reducing electricity consumption per unit of fresh weight by 24 %. Nutritional quality analysis showed the treatment increased soluble sugars and starch contents. By integrating advanced 3D phenotyping with dynamic light intensity control, this study demonstrates a prototype for intelligent decision-making to enhance yield and energy use efficiency in practical vertical farming.

Why it matches plant phenotyping methods自動3Dフェノタイピングパイプラインを開発し、マルチビュー再構成で植物体形態と光遮断を定量化、精度評価も実施しており、フェノタイピング手法が研究の中心である。

abstractwe first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception.

Scalable phenotyping and yield estimation via stability index and single-plant variability using a vision-based large model framework →

High-throughput field phenotyping bridges genotype, environment, and phenotypic performance. Conventional plot-level approaches relying on manual surveys are labor-intensive, and error-prone and fail to capture variability among individual plants, limiting seed cotton yield estimation and genotype screening under natural conditions. To address these limitations, a complex framework was developed, integrating single-plant instance segmentation, multi-trait inversion, plot-level stability characterization, and yield estimation. The enhanced vision-model framework, TopoRefineSAM, combines YOLOv12 detection with SAM2 segmentation and incorporates adaptive enhancement and topological refinement modules, enabling efficient, robust, and cost-effective single-plant identification under weak annotation. Based on this segmentation, multi-source UAV imagery (RGB, multispectral, thermal infrared, and DSM) was used to build ensemble learning models for inversion of physiological and biomass traits. A Stability Index Group (SIG) translates inter-plant variability into plot-level stability features, improving interpretability and consistency in yield estimation and cultivar screening. Results demonstrated that TopoRefineSAM achieved high segmentation accuracy for single-plant extraction under complex field conditions. In multi-trait inversion, Gradient Boosting Decision Trees (GBDT) achieved the highest performance. Our results demonstrated strong consistency between multimodal features and measured traits. In yield estimation, incorporating the SIG substantially improved predictive performance across growth stages. In cultivar screening, the method achieved high agreement with field measurements, showing robust identification of top-performing cultivars. Collectively, the findings establish a scalable, cost-effective, and high-accuracy framework for field-based phenotypic analysis and yield estimation, providing both methodological innovations and practical support for precision breeding and large-scale crop improvement.

Why it matches plant phenotyping methods単一植物のセグメンテーション、マルチモーダル画像による形質推定、安定性指標、収量推定を統合した圃場フェノタイピング手法の開発が中心である。

abstractTo address these limitations, a complex framework was developed, integrating single-plant instance segmentation, multi-trait inversion, plot-level stability characterization, and yield estimation.

Development and validation of a screening method for actinomycetes from Protaetia brevitarsis larval frass with biocontrol potential →

This study aimed to establish an efficient screening method for identifying biocontrol strains effective against soil-borne pathogens of plants. To achieve this objective, we developed a quantitatively-controlled potted plant testing (QC-PPT) system by optimizing cultivation devices, growth substrates, pathogen inoculation methods, and quantitative evaluation of root infection. This design effectively confines the roots and facilitates uniform pathogen infection. The optimal inoculation timing was determined to be between the 10th and 16th day of growth. Furthermore, a substrate composed of vermiculite with 4% organic compost was identified as ideal, supporting vigorous peanut growth while allowing sufficient pathogen infection required for reliable biocontrol evaluation. Using this system, seven strains with strong antagonistic effects against Sclerotium rolfsii were isolated from White-spotted Flower Chafer (WSFC, Protaetia brevitarsis) larval frass. In vitro assays showed that strain X13 inhibited Sclerotium rolfsii growth by 44.56%, while strain X15 performed more excellently in the QC-PPT system: it increased peanut root dry weight by 32.8% and reduced root lesion area by 51.2% compared to the control group. Genomic sequencing data revealed that the superior strain X15 possesses the most diverse set of secondary metabolite biosynthetic gene clusters. Collectively, strain X15 is a promising candidate for biopesticide development, and the QC-PPT system we established can be extended to other crop-soil-borne pathogen systems. This study not only provides an effective biocontrol resource for peanut southern blight but also facilitates the development of sustainable disease management strategies in agriculture.

Why it matches plant phenotyping methods植物根の感染状態を定量評価するQC-PPTシステムを開発・最適化し、根病斑面積などの植物病徴を測定する方法が研究の中心であるため。

abstractwe developed a quantitatively-controlled potted plant testing (QC-PPT) system by optimizing cultivation devices, growth substrates, pathogen inoculation methods, and quantitative evaluation of root infection.

Spatio-temporal prediction of total and legume dry matter yield using UAV-borne RGB and multispectral images in alfalfa-grass mixtures →

Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.

Why it matches plant phenotyping methodsUAV画像から樹冠高・スペクトル情報を抽出し、機械学習で乾物収量とマメ科収量を推定する手法の開発・検証が研究の中心であるため。

abstractThis study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites.

Phenotype-driven screening and machine learning integration for developing high-performance Miscanthus genotypes for saline-alkaline soils →

Cultivation of dedicated bioenergy crops on marginal lands is critical for sustainable development, yet breeding climate-resilient genotypes remains a major bottleneck. This study introduces a powerful, integrated framework for accelerating the selection of high-performance Miscanthus hybrids adapted to saline-alkaline soils. The framework uniquely combines phenotypic classification (scatter vs. cespitose plant types) with explainable machine learning (ML). An Extra Trees Regressor (ETR) model, optimized through hyperparameter tuning, achieved high predictive accuracy for biomass (R² = 0.916) in a diverse hybrid population. Crucially, SHapley Additive exPlanations (SHAP) analysis revealed two distinct, data-driven ideotypes: the optimal cespitose type maximizes tiller number, whereas the optimal scatter type prioritizes individual tiller biomass and stem diameter. SHAP also uncovered critical physiological trade-offs, identifying potential thresholds for key traits beyond which biomass gains diminish. Applying this framework, we screened 216 genotypes and identified five superior lines. Subsequent two-year field trials on both farmland and saline-alkaline soil validated our approach, with genotype S88 demonstrating superior yield and stability under stress. This study not only establishes a scalable strategy for industrial crop breeding but also offers novel insights into the architectural determinants of yield under marginal conditions.

Why it matches plant phenotyping methods植物形態形質と機械学習を統合した選抜フレームワークが研究の中心で、バイオマス予測、形態型分類、SHAPによる形質解釈、圃場検証まで実施しているため、単なる生物学的実験のルーチン測定ではない。

abstractThis study introduces a powerful, integrated framework for accelerating the selection of high-performance Miscanthus hybrids adapted to saline-alkaline soils.

Photogrammetric software performance for estimating maize above-ground biomass using UAVs →

Estimating maize biomass is a manual, destructive method subject to variability. The use of unmanned aerial vehicles (UAVs) allows for the acquisition of high-resolution images of crop canopies and, through software, facilitates the estimation of above-ground biomass. The objective was to evaluate the performance of the photogrammetric software Agisoft Metashape and Pix4Dmapper in estimating above-ground maize biomass under field conditions. The experimental design adopted was a randomized complete block design (RCBD) with three replications, and the treatments consisted of eight maize hybrids (2A510 PW, B2360 PWU, B2433 PWU, B2612 PWU, B2688 PWU, CD3410 PW, DKB255 PRO3, DKB363 PRO3). The Agisoft Metashape (R2 = 0.89) and Pix4Dmapper (R2 = 0.82) software demonstrated high experimental precision in estimating above-ground biomass. The maize hybrids DKB255 PRO3 and B2433 PWU showed the highest and lowest biomass productivity, respectively. Integrating UAVs with photogrammetric techniques proves effective in estimating maize biomass under field conditions.

Why it matches plant phenotyping methodsUAV画像とフォトグラメトリックソフトウェアによるトウモロコシ地上部バイオマス推定という植物形質取得法を、複数ソフトウェアで性能評価しており、方法が研究の中心です。

abstractThe objective was to evaluate the performance of the photogrammetric software Agisoft Metashape and Pix4Dmapper in estimating above-ground maize biomass under field conditions.

Use of Drones for Estimating Biodiversity, Structural Parameters, and Woody Carbon Storage in Sahelian Ecosystems of Senegal →

Sahelian ecosystems play a crucial role in climate regulation, particularly through their capacity to store atmospheric carbon. However, their carbon sequestration potential remains poorly quantified due to the high cost and logistical constraints of conventional field-based methods. This study investigates the use of low-cost unmanned aerial vehicles (UAVs) to assess dendrometric parameters and estimate the carbon storage capacity of woody vegetation within a 10-hectare Sahelian plot in Senegal. High-resolution aerial imagery was acquired using a DJI Phantom 4 Pro+ drone and processed with photogrammetric software. Tree height and crown diameter were extracted and validated using field measurements, showing a strong correlation for tree height (R² = 0.94) and a moderate correlation for crown diameter (R² = 0.74). Spectral index analysis enabled the identification of dominant woody species, including Balanites aegyptiaca, Sclerocarya birrea, Acacia raddiana, and Acacia senegal.The results indicate that Balanites aegyptiaca contributes the highest amount of carbon storage per hectare (1520.76 ± 185.5 kg ha⁻¹), mainly due to its high stem density (23.3 individuals ha⁻¹), whereas Sclerocarya birrea exhibits the highest average carbon stock per individual tree (206.8 ± 107.8 kg). These findings highlight the significant role of these species in climate change mitigation strategies. By demonstrating the effectiveness of low-cost UAVs for rapid and non-destructive assessment of Sahelian ecosystems, this study provides a promising approach for large-scale ecological monitoring and sustainable natural resource management. It also identifies methodological improvements required to enhance crown dimension estimates. Overall, this UAVbased approach represents a reliable alternative to destructive methods and is well suited to the ecological and operational challenges of Sahelian environments, with strong potential for application in similar regions.

Why it matches plant phenotyping methodsUAV画像から樹高・樹冠径という植物形態形質を抽出し、地上測定で検証する手法が研究の中心であるため、植物フェノタイピング手法として含める。

abstractTree height and crown diameter were extracted and validated using field measurements, showing a strong correlation for tree height (R² = 0.94) and a moderate correlation for crown diameter (R² = 0.74).

Sustainable Estimation of Tree Biomass and Volume Using UAV Imagery: A Comprehensive Review →

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks.

Why it matches plant phenotyping methodsUAV画像・3Dデータによる樹木のバイオマス、体積、樹高、樹冠形状などの植物形質推定手法を181研究から体系的にレビューしており、フェノタイピング手法が中心です。

abstractThis review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review.

A review of forest biomass assessments based on remote sensing reveals progress in methodological quality—but major challenges remain →

Abstract Reliable forest biomass assessments are becoming increasingly important, as Parties to the Climate Convention are required to report changes in multiple carbon pools, including both above- and belowground biomass. In some regions, use of remote sensing is the only viable option for obtaining such estimates, whereas in other regions it bears potential to improve the accuracy of ground inventory-based biomass estimates. However, statistically rigorous estimation through remote sensing poses several challenges. This study systematically and comprehensively reviews the methodological quality of large-area biomass assessment studies from 1992 to 2022, based on core survey elements for successful biomass surveying assisted by remote sensing. For each element, we reviewed the studies in relation to “ideal standards” derived from the literature, which served as evaluation criteria. Our review revealed an increasing trend in use of remote sensing for biomass surveys, coupled with gradual improvements in methodological quality for all survey elements evaluated. For example, advances in remote sensing techniques, particularly the increased use of Light Detection and Ranging, Radio Detection and Ranging, and digital aerial photogrammetry, all technologies able to capture information on forest structure, have enhanced the reliability of biomass estimates. However, several problems remain, such as field data scarcity for model calibration, signal saturation in high-biomass regions, and misconceptions about the use of statistical methods. We identified five remaining key challenges for improving remote sensing assisted large-area biomass assessments. These include (i) obtaining sensor data that correlate stronger with biomass, (ii) acquiring larger sets of harmonized field data at the level of trees and plots for calibrating models, (iii) adequate use of statistical principles, (iv) developing methods for domain estimation, and (v) improved quality assurance and quality control. While upcoming new airborne technologies and satellite missions may mitigate some challenges, continued methodological innovation and further enhancement of the rigor of statistical and other procedures will remain essential for advancing remote sensing-based biomass assessments.

Why it matches plant phenotyping methods森林の植物バイオマスという明示的な形質を対象に、リモートセンシングによる推定手法の方法論的品質、校正、統計、精度向上を体系的に評価しており、単なるバイオマス測定の報告ではない。

abstractThis study systematically and comprehensively reviews the methodological quality of large-area biomass assessment studies from 1992 to 2022, based on core survey elements for successful biomass surveying assisted by remote sensing.

Estimation of soybean phenotypic parameters across growth stages using UAV-based multi-source feature fusion and XGBoost →

While essential for precision agriculture, the accurate and dynamic monitoring of crop phenotypic parameters faces challenges, including the constraints of single-data sources and insufficient model generalization across growth stages. This research introduced an integrated framework that leverages multi-source data fusion and the XGBoost algorithm to estimate key soybean parameters, including Leaf Area Index (LAI) and Above-Ground Biomass (AGB). Field experiments incorporated different irrigation methods (drip/micro-sprinkler) and planting densities (210,000/270,000 plants ha −1 ), multispectral images and corresponding ground truth data were acquired across five critical growth stages.We extracted 11 vegetation indices (V) and 8 texture features (T) and constructed inversion models using Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) based on single and multi-source (V+T) features. The results indicated that: the multi-source feature fusion model outperformed single-feature models. The XGBoost algorithm outperformed all other models, achieving average R 2 values of 0.673, and 0.671, and RMSE values of 0.117, and 79.751 kg ha −1 for LAI, and AGB inversion, respectively. The full pod stage (R4) was identified as the optimal remote sensing observation window, where the best models achieved R 2 values of 0.846 (LAI) and 0.731 (AGB), with RMSE values of 0.131 and 81.01 kg ha −1 , respectively. Drip irrigation combined with high planting density significantly ( P < 0.05) increased soybean LAI and AGB. This study provides a robust, high-throughput technical solution for dynamic crop phenotyping, it highlights the value of fusing multi-source UAV features with machine learning for advancing data-driven smart agriculture. • Achieved dynamic soybean phenotyping by fusing unmanned aerial vehicle (UAV) multi-source features with machine learning. • Multi-source feature fusion outperformed single-feature models in accuracy and robustness. • Full pod stage identified as the optimal UAV remote sensing observation window. • Drip irrigation with high planting density significantly enhanced soybean Leaf Area Index and Above-Ground Biomass.

Why it matches plant phenotyping methodsUAVマルチソース画像と機械学習により、LAIおよび地上部バイオマスを推定する方法を開発・比較検証しており、植物表現型取得が研究の中心である。

abstractThis research introduced an integrated framework that leverages multi-source data fusion and the XGBoost algorithm to estimate key soybean parameters, including Leaf Area Index (LAI) and Above-Ground Biomass (AGB).

Artificial neural network-based estimation of physiological, biochemical, and nutrient parameters in durum wheat under NaCl and biostimulant treatments. →

BACKGROUND: Durum wheat (Triticum durum L.) productivity is strongly limited by salinity stress, particularly during early growth stages, due to disruptions in growth, water relations, and nutrient uptake. Seaweed extracts (SWEs), especially those derived from Ascophyllum nodosum, are widely used as biostimulants to enhance stress tolerance; however, their effects on durum wheat under salinity remain insufficiently characterized. In parallel, artificial neural networks (ANNs) provide effective tools for modeling complex plant responses to environmental stress. RESULTS: Salinity significantly reduced growth and physiological parameters, including biomass, chlorophyll content, and relative water content. SWE applications (2 and 4 g L⁻¹) effectively mitigated these negative effects. Biochemical traits such as proline accumulation, total phenolic content, and total antioxidant capacity were markedly enhanced under salinity. SWE treatments also improved macro- and micronutrient uptake in roots and shoots. ANN models successfully predicted multiple plant traits with high accuracy (R² > 0.90 for several key parameters). These models were implemented in a web-based R Shiny application to enable real-time prediction of plant responses. CONCLUSIONS : SWE application alleviates salinity-induced stress in durum wheat by improving growth, antioxidant capacity, and nutrient acquisition. The integration of ANN modeling with experimental data provides a reliable and practical approach for predicting plant responses, supporting artificial intelligence-assisted strategies for sustainable wheat production under saline conditions.

Why it matches plant phenotyping methods塩ストレス・生物刺激剤実験を背景とするが、ANNによる複数の植物生理・生化学・栄養形質の予測とWebアプリ実装が題名および結果の中心であり、再利用可能な計算的形質推定ワークフローに該当する。

titleArtificial neural network-based estimation of physiological, biochemical, and nutrient parameters in durum wheat under NaCl and biostimulant treatments.

Enhanced estimation of crop biomass and height using Sentinel-1 polarization texture indices and integration with optical remote sensing →

Accurate estimation of above-ground biomass (AGB) and plant height is essential for precision crop management. However, traditional methods like synthetic aperture radar (SAR) data and optical vegetation indices (VIs) often face signal saturation at medium to high AGB levels. To address this, we proposed two polarization texture indices, i.e., Ratio SAR Texture Index (RSTI) and Normalized Difference SAR Texture Index (NDSTI), derived from Sentinel-1 (S-1) data to estimate crop AGB and height. We further investigated their integration with S-1 polarizations and Sentinel-2 (S-2) VIs using four machine learning algorithms to enhance retrieval performance. Results revealed that both RSTI and NDSTI outperformed individual polarizations, polarization texture features, and most of VIs in estimating crop AGB and height. Furthermore, the combination of these indices with S-2 VIs significantly improved the retrieval accuracy. The optimal models achieved R2 values up to 0.75 and 0.80 for maize and soybean AGB, 0.89 and 0.94 for maize and soybean height, respectively. Validation with an independent dataset confirmed the robustness and transferability of the proposed models for estimating maize AGB and height. Overall, RSTI and NDSTI, along with their integration with optical VIs, provide an effective approach for improving crop AGB and height estimation for agricultural monitoring.

Why it matches plant phenotyping methodsSentinel-1/2リモートセンシングから作物バイオマスと草丈を推定する新規テクスチャ指数を開発し、独立データで検証しており、植物形質取得手法が中心である。

abstractwe proposed two polarization texture indices, i.e., Ratio SAR Texture Index (RSTI) and Normalized Difference SAR Texture Index (NDSTI), derived from Sentinel-1 (S-1) data to estimate crop AGB and height.

Combining UAV-SfM, SAR, MSI and field surveys for estimation of above ground biomass in mangrove forest of Chonburi, Thailand →

Mangrove biomass is a key indicator for quantifying carbon cycling in blue-carbon ecosystems, yet conventional approaches face significant challenges. To improve large-scale mangrove biomass assessment and provide a baseline for targeted conservation, present study proposes a Single-tree-Plot-Community-Region (AGB T/F~U~S ) upscaling method that integrates UAV-SfM, SAR, MSI, and field surveys, and applies it to Chonburi, Thailand. In 2023, total mangrove aboveground biomass in Chonburi Province was 145.24 kt, with a mean AGB density of 101.61 Mg/ha, slightly below the global mangrove average. Long-term records reveal an initial decline followed by post-2015 recovery to about 85% of the 1996 level. Relative to the conventional plot-satellite model, the AGB T/F~U~S framework substantially improves estimation performance and reduces prediction error (ΔR²≈0.47; ΔRMSE ≈ 66.03 Mg/ha), and remains robust under limited training data, with accuracy gains saturating once plot numbers exceed a moderate threshold. These results demonstrate that multi-scale upscaling provides a transferable pathway for mangrove biomass mapping in data-scarce regions and offers a practical baseline for blue-carbon accounting and targeted restoration planning.

Why it matches plant phenotyping methodsUAV-SfM、SAR、MSIと現地調査を統合し、マングローブの地上部バイオマスを推定するマルチスケール手法を開発・比較評価しており、植物形質の取得・推定が研究の中心です。

abstractpresent study proposes a Single-tree-Plot-Community-Region (AGB T/F~U~S ) upscaling method that integrates UAV-SfM, SAR, MSI, and field surveys

Point Cloud Data-driven Methods for Estimating Maize Leaf Biomass →

[Objective]Maize leaf dry biomass is a key trait that reflects plant morphology, growth vigor, and physiological processes including photosynthetic production. Its dynamic changes can effectively characterize the growth status of maize. Accurate estimation of maize leaf dry biomass is crucial for accurately predicting maize yield and informing production management decisions. Extensive research on crop dry biomass estimation indicates that 3D point cloud data characterizing crop morphological structure, along with features derived therefrom, exhibit an extremely high correlation with crop dry biomass. However, traditional dry biomass prediction studies focus primarily on the population canopy scale, and lack effective prediction methods for dry biomass at the plant and organ scales. Research on non-destructive measurement methods for maize leaf dry biomass, based on 3D point clouds and machine learning, the demand is conducted to address for rapid acquisition of organ-level dry biomass information in maize cultivation and management research.[Methods]Maize leaf point cloud data were acquired using three techniques: Multi-view stereo (MVS), LiDAR scanning, and 3D digitalization (DT). The leaf point clouds underwent preprocessing steps that included plant segmentation, denoising, mesh refinement, and uniform subsampling. Subsequently, morphological traits were extracted from the processed data, including leaf length, leaf area, bounding box dimensions, and the number of points contained within the leaf point clouds. Three machine learning methods: random forest (RF), gradient boosting regression tree (GBRT), and support vector regression (SVR), as well as two deep learning methods: convolutional neural network (CNN) and fully connected neural network (FCNN), were employed for predicting maize leaf dry weight. A point cloud-based maize leaf dry biomass prediction model was subsequently developed. This study utilized the mean squared error reduction method inherent to RF and the cumulative improvement method based on decision tree splits in GBRT to rank and visualize feature importance for optimal models. The resulting rankings were then visualized. Simultaneously, Pearson correlation analysis was used to analyze the correlations of the features from the fused dataset (integrating data from the three devices) as well as those from the DT data with maize leaf dry biomass.[Results and Discussions]The results demonstrated that, among the dry biomass prediction models developed in this study, the model based on Laser point cloud data and the FCNN method achieved the highest accuracy, with a mean absolute error (MAE) of 0.08 g, a mean absolute percentage error (MAPE) of 4.60%, a root mean square error (RMSE) of 0.10 g, and a coefficient of determination (R2) of 0.98. In the correlation analysis, the leaf area exhibited the strongest correlation with dry biomass (r = 0.92), followed by the number of points (r = 0.88), leaf width (r = 0.86), and leaf length (r = 0.77). In the feature importance ranking, the leaf area trait consistently ranked within the top two positions, whereas the number of points ranked among the top three in most cases. However, features such as the height of the leaf base above the ground, the horizontal distances from the leaf tip and apex to the stem, and the azimuth angle demonstrated low correlations with dry biomass and low feature importance.[Conclusions]Among all the maize leaf features investigated in this study, size-related traits (such as leaf area, point count, leaf length, and leaf width) had the greatest impact on the accuracy of dry biomass estimation. The utilization of high-resolution 3D point clouds of maize leaves, combined with machine learning methods, enabled a high-accuracy estimation of leaf dry weight and provided a novel approach for the non-destructive measurement of dry biomass in crop organs.

Why it matches plant phenotyping methods3D点群取得・前処理・形態形質抽出と機械学習を組み合わせ、トウモロコシ葉の器官レベル乾物重を非破壊推定する手法を開発・評価しており、表現型取得と推定が研究の中心である。

abstractMaize leaf point cloud data were acquired using three techniques: Multi-view stereo (MVS), LiDAR scanning, and 3D digitalization (DT).

Computer vision-based non-invasive biomass estimation and water stress monitoring in tomato plants →

Precision horticulture demands intelligent monitoring systems for automated crop management. While traditional biomass estimation relies on destructive sampling, modern ICT-driven approaches offer transformative solutions. This study presents an AI-powered methodology integrating YOLOv11 for autonomous biomass estimation and stress monitoring in tomato crops. High-resolution RGB imagery was captured across multiple phenological stages under two irrigation regimes. YOLOv11’s computer vision capabilities enabled automated canopy detection, segmentation, and digital biomass quantification, eliminating destructive sampling. Novel AI-driven metrics were introduced: convex hull area for stress-induced canopy alterations and compactness (digital biomass to convex hull ratio) for automated canopy assessment. This integrated approach achieved robust accuracy in stress detection and biomass estimation (R² =0.821), enabling real-time monitoring for precision horticulture. The model demonstrated exceptional performance with segmentation precision of 0.950, recall of 0.979, and mean average precision of 0.975 at IoU 0.5, with mAP50-95 of 0.826. The rapid inference time of 2.3ms per image enables high-throughput phenotyping and decision support for site-specific management. YOLOv11-derived digital biomass correlated strongly with fresh biomass (R² = 0.821). Image-derived features effectively differentiated control and stress conditions. Genotype analysis revealed variation in biomass accumulation: Arka Abhed and Arka Rakshak performed better under optimal irrigation, while Arka Vikas showed greater stress resilience. These results validate YOLOv11 as a scalable solution for intelligent crop monitoring and precision input application in next-generation digital horticulture.

Why it matches plant phenotyping methods植物の画像からバイオマスと水ストレスを推定するコンピュータビジョン手法を開発・検証しており、表現型取得が研究の中心である。

abstractThis study presents an AI-powered methodology integrating YOLOv11 for autonomous biomass estimation and stress monitoring in tomato crops.

High throughput phenotyping of energy cane using uncrewed aircraft system (UAS) and machine learning →

Accurate estimation of biomass in energy cane is essential for cultivar selection in breeding programs and biomass supply forecasting in bioenergy production. This study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP). Seven cultivars were monitored between December 2023 and July 2024 at an experimental field in Weslaco, Texas. Structural metrics such as percentile-based heights, canopy volume, and interaction variables were extracted and used to train four machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and a one-dimensional Convolutional Neural Network (1D-CNN). Ensemble tree algorithms consistently outperformed CNN, with XGBoost and LightGBM providing the most stable and interpretable predictions. For single sensor inputs, lidar models (R² = 0.70–0.78; RMSE = 1.36–1.60 kg/m²) generally outperformed RGB-SfM (R² = 0.69–0.73; RMSE = 1.52–1.63 kg/m²), though RGB-SfM performed competitively with XGBoost. Fused models combining lidar and RGB-SfM features achieved the highest accuracies (XGBoost: R² = 0.80, RMSE = 1.31 kg/m²; LightGBM: R² = 0.79, RMSE = 1.36 kg/m²), mitigating the underestimation of high-biomass plots in RGB-SfM and the slight overestimation of lidar at the upper tail. Cultivar specific analysis confirmed TH16–22 as the top performer, followed closely by Ho02–113 and TCP10–4928, demonstrating the capacity of UAS HTP to support breeding decisions. These findings confirm the biological relevance of percentile-based height metrics (particularly the 75th percentile), canopy volume, and their interactions for biomass accumulation and underscore the value of sensor fusion in reducing systematic bias. This study provides systematic demonstration of lidar and RGB-SfM fusion for biomass estimation in energy cane, establishing a scalable and non-destructive approach that advances high-throughput phenotyping and supports the development of sustainable bioenergy cropping systems.

Why it matches plant phenotyping methodsUAS搭載LiDAR・RGB-SfMによる植物構造特徴の取得と機械学習によるバイオマス推定を開発・比較評価しており、表現型取得・抽出手法が研究の中心である。

abstractThis study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP).

High-throughput UAV phenotyping for plot-level harvest index estimation in wheat fields →

Accurate estimation of the harvest index (HI), the ratio of grain yield to total aboveground biomass (AGB), is crucial for evaluating crop productivity and resource-use efficiency in wheat breeding programs. While traditional HI measurement methods use destructive field sampling, which is labour-intensive and impractical for large-scale breeding trials, recent advances in UAV-based remote sensing now offer non-destructive alternatives capable of delivering high-throughput, plot-level HI estimation. In this study, we present a high-throughput phenotyping framework that combines UAV-based multispectral imaging and ensemble machine learning to estimate HI under field environments. Multispectral data were collected at two key growth stages, anthesis and maturity, using a DJI M300 RTK drone equipped with a RedEdge-P sensor. Vegetation indices (VIs), including the normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green NDVI (G-NDVI), were extracted using data from sensors and ground truth monitoring and used as predictors to estimate grain yield and AGB for calculating HI. An ensemble learning model, based on a stacking architecture comprising five regressors and a ridge regression meta-learner, was employed to enhance prediction accuracy. Results showed strong correlations between UAV-derived and ground-truth VIs ( R 2 > 0 . 94 , RMSE < 0 . 023). The ensemble model demonstrated high accuracy and strong generalization for HI estimation across both experimental sites and growing seasons. At the anthesis stage, the NDVI-based ensemble model achieved the best performance. For the Indian Head site, it yielded a testing R 2 of 0.87, RMSE of 4.18 g/p, and NRMSE of 2.73%, based on a training R 2 of 0.83. At the Swift Current site, the model produced a testing R 2 of 0.84, RMSE of 8.67 g/p, and NRMSE of 5.67%. Similarly, at the maturity stage, the NDRE-based ensemble model was the top performer. It recorded a testing R 2 of 0.86, RMSE of 7.10 g/p, and NRMSE of 4.64% at Indian Head, and a testing R 2 of 0.83 with an RMSE of 8.06 g/p, and NRMSE of 5.27% at Swift Current. Across all indices and stages, the ensemble model consistently outperformed individual models, achieving high testing R 2 values and low RMSE, which confirms its robustness and predictive power on unseen data. The proposed UAV machine learning framework demonstrates a reliable and non-destructive approach for field-level HI estimation, thereby improving germplasm selection efficiency for yield improvement. It offers a valuable tool for accelerating trait-based wheat breeding and precision agriculture applications.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習により、小麦の収穫指数を非破壊・高スループット推定する手法が研究の中心であり、検証結果も提示している。

abstractIn this study, we present a high-throughput phenotyping framework that combines UAV-based multispectral imaging and ensemble machine learning to estimate HI under field environments.

Estimating spinach fresh weight from UAV multispectral imagery: A multitask attention U-Net and regression approach →

Monitoring plant weight during its development cycle is crucial for effective growth monitoring; it provides valuable information on plant’s health and development. Weight data are essential to determine the optimal harvest time and ensure that plants are harvested when they are at their best. This work aimed to design and implement a workflow that allows the study of growth and development variables in a spinach crop cycle using high spatial resolution multispectral images acquired with an Unmanned Aerial Vehicle (UAV). We based this workflow on applying a multitask attention U-Net model for plant segmentation and advanced statistical analysis, including regression methods and hierarchical analysis, to build and evaluate a Random Forest (RF) model and a Generalized Linear Model (GLM) for spinach fresh weight estimation. The segmentation model achieved a mean Intersection over Union (mIoU) of 0.90 and an F-score of 0.93 against manually drawn labels. Experimental validation of the estimation of the fresh weight of spinach plants from geometric and spectral characteristics with an R2 of 0.90 and RMSE = 23.48 g for the RF model constructed from explanatory variables found with hierarchical analysis. Results demonstrate the utility of a novel hybrid approach for analysis of multispectral imagery from UAVs in crop monitoring.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物セグメンテーションと幾何・スペクトル特徴を抽出し、ホウレンソウの生体重を推定するワークフローを開発・検証しており、表現型取得・推定手法が中心である。

abstractThis work aimed to design and implement a workflow that allows the study of growth and development variables in a spinach crop cycle using high spatial resolution multispectral images acquired with an Unmanned Aerial Vehicle (UAV).

Advanced workflows for UAV-based crop height estimation using structure from motion (SfM) point clouds →

Crop height is a key biophysical parameter closely linked to plant growth, biomass, and yield. With the advancement of remote sensing technologies, unmanned aerial vehicles (UAV) have emerged as a promising tool for estimating crop height using structure from motion (SfM) point clouds. However, accurately mapping the digital terrain model (DTM) beneath dense canopies remains a major challenge, as existing methods struggle to capture the soil surface effectively under full vegetation cover. This study aimed to develop and evaluate efficient workflows for generating DTMs from UAV-derived point clouds for in-season crop height estimation, with a focus on eliminating the need for pre-season UAV flights. The experiment was conducted during the 2023 maize growing season in Temple, TX, and compared three workflows: (i) UAV-B CH , which used UAV-derived bare soil surfaces as the DTM; (ii) Sent CH , which used Sentinel-1A-derived surfaces from the dormant season; and (iii) UAV-P CH , a novel approach that applied a low-pass filter to select the lowest 1 % of elevation points within a moving window, followed by fitting a 2.5D regression surface to generate the DTM. Results showed that UAV-P CH consistently outperformed the other methods across two fields with varying elevation patterns, achieving higher accuracy (R 2 ≈ 0.89) compared to UAV-B CH (R 2 ≈ 0.66) and Sent CH (R 2 ≈ 0.69). UAV-P DTM effectively minimized temporal inconsistencies and vertical misalignments commonly associated with multi-date UAV acquisitions. This approach offers a scalable and efficient solution for crop height estimation using a single UAV flight. Future research should explore its applicability across diverse crop types and canopy structures to enhance its utility in precision agriculture.

Why it matches plant phenotyping methodsUAV-SfM点群から作物高を推定するDTM生成ワークフローを開発・比較評価しており、植物形質の取得方法が研究の中心である。

abstractThis study aimed to develop and evaluate efficient workflows for generating DTMs from UAV-derived point clouds for in-season crop height estimation

Introducing partial pixel integration of UAV imagery to estimate maize (Zea mays L.) aboveground biomass →

Accurate estimation of aboveground biomass (AGB) helps to monitor maize growth and yield prediction, and unmanned aerial vehicles (UAVs) have become one of the most significant technological tools in precision agriculture. However, previous studies have mainly focused on utilizing spectral indices, texture metrics and structural features derived from UAV multispectral imagery. These methods often involve significant uncertainties and ignore overall maize morphological characteristics. In this study, an innovative partial pixel integration (PPI) parameter is introduced to characterize both horizontal and vertical structural features of maize (Zea mays L.) at the plot scale. Field experiments were conducted in Dafeng District, Yancheng City, Jiangsu Province, China. Multispectral UAV imagery was captured at five flight altitudes (10 m, 20 m, 30 m, 50 m, and 80 m). Five structural features—fractional vegetation cover (FVC), plant height (PH), FVC × PH, pixel integration (PI), and PPI—were extracted to develop Fresh and Dry AGB estimation models based on linear, exponential, and power functions. The models were verified with the method of five-fold cross-validation to evaluate the predictive performance of different parameters. The results revealed that: (1) The models with PPI parameter outperformed that with all other metrics (FVC, PH, FVC×PH, PI), achieving the highest R² values of 0.968 for Fresh AGB (at 20 m flight altitude) and 0.948 for Dry AGB (at 10 m flight altitude); (2) Fresh AGB estimation models were generally more accurate than Dry AGB estimation models; (3) Contrary to expectations, increasing UAV flight altitude did not necessarily reduce AGB prediction accuracy. These findings demonstrate that the PPI parameter delivers high accuracy and robustness, presenting a novel and reliable approach for in-field maize AGB estimation.

Why it matches plant phenotyping methodsUAV画像からトウモロコシの形態特徴を抽出し、PPIという新規パラメータで地上部バイオマスを推定・検証することが研究の中心であり、植物表現型取得手法として適格。

abstractan innovative partial pixel integration (PPI) parameter is introduced to characterize both horizontal and vertical structural features of maize

Use of an enhanced cultivar calibration framework for DSSAT to examine effects of ecotype and time-series data →

Abstract Process-based crop modelling platforms such as DSSAT are potentially valuable tools for crop breeding programmes, with the capacity to predict genotype-by-environment-by-management interactions. However, their application for breeding is challenged by the need to calibrate large numbers of genotypes within populations. In wheat (Triticum aestivum L.), using pre-existing DSSAT-CERES wheat ecotypes can introduce unrealistic parameter compensation during cultivar calibration. To address this, we developed a two-phase sequential calibration framework. This workflow uses phenotypic clustering to first define representative ecotypes using experiment-specific data before proceeding with cultivar-level parameter estimation. We demonstrate the utility of this framework to integrate direct measurements from proximal and remote sensing data collected on 14 genotypes grown under well-watered, drought, and heat stress field conditions. Incorporating experiment-derived ecotypes reduced compensatory adjustments in cultivar coefficients and improved simulation accuracy compared with default or non-representative ecotypes. Time-series data enhanced calibration, although the effect of different data combinations varied with environmental scenario and trait. Model simulations under stress conditions generally captured drought effects on biomass but underestimated heat stress impacts. This framework provides a systematic and scalable approach for integrating high-throughput phenotyping and process-based crop modelling.

Why it matches plant phenotyping methods作物モデルの遺伝型較正において、表現型クラスタリングと近接・リモートセンシングの時系列データ統合を中核とするスケーラブルな手法を開発・検証しているため。

abstractwe developed a two-phase sequential calibration framework

Comparative evaluation of 2D RGB and 3D RGB-D imaging for non-contact biomass estimation in lettuce →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsレタスのバイオマスを非接触で推定する2D RGBと3D RGB-D画像法を比較評価しており、植物形質取得手法の技術評価が中心です。

titleComparative evaluation of 2D RGB and 3D RGB-D imaging for non-contact biomass estimation in lettuce

3D Phenotyping Pipeline for Non-Destructive Biomass Estimation of Nickel Hyperaccumulator Plants →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methods3D画像等を用いた非破壊的な植物バイオマス推定パイプラインの開発が題名上の中心であり、植物形質の取得・推定手法に該当する。

title3D Phenotyping Pipeline for Non-Destructive Biomass Estimation of Nickel Hyperaccumulator Plants

Estimation of Carbon Storage Based on AAV Multispectral Photogrammetry: An Offshore Islands Study in Pearl River Delta →

Aboveground biomass (AGB) of forests is a crucial metric for assessing ecosystem carbon storage and cycling. The geographical complexity of offshore islands, especially their perennial cloud cover, poses a challenge to traditional AGB remote sensing, so unmanned aerial vehicle (UAV) based remote sensing technology is particularly important. However, UAV approaches face limitations from ground interference, structural parameter errors, and allometric equation discrepancies. This study develops a framework integrating UAV photogrammetry and the Mask R-CNN to identify tree species, quantify forest structure, and estimate AGB in a representative offshore island to infer the impact of tree species differences, dominated by allometric equations and structural parameter errors, dominated by canopy occlusion on the estimated AGB of island forests. Compared to the AGB results calculated by species identification of individual trees, multispectral sensors effectively identified four dominant tree species and land cover, as general allometric equations resulted in significant errors ranging from − 68 % to + 36 %. The optimized canopy height model (CHM) approach revealed a 5.6 % underestimation of tree height and 10.0 % error in diameter at breast height (DBH) due to canopy occlusion. This study provides new insights into AGB estimation and forest species identification methodologies, which are important for studying carbon management activities in similar ecosystem types.

Why it matches plant phenotyping methodsUAVマルチスペクトル写真測量とMask R-CNNを用いて樹種、樹木構造、樹高・DBH・地上部バイオマスを推定する手法を開発し、誤差も評価しており、植物表現型取得が中心である。

abstractThis study develops a framework integrating UAV photogrammetry and the Mask R-CNN to identify tree species, quantify forest structure, and estimate AGB

Broad-scale monitoring of canopy structure with multi-source remote sensing and alternative reference data →

In a time of shifting disturbance regimes and anthropogenic pressures, effective management of forest and woodland ecosystems depends on continuous, landscape-level monitoring of canopy structure. While remote sensing can help meet this need, its broad operational implementation is often impeded by a myriad of data constraints, necessitating careful tailoring of monitoring programs to specific management objectives across heterogeneous environments. For example, certain regions, such as African savannas, are heavily data-limited and present unique challenges for detecting subtle structural variations within sparse, low-stature vegetation. Conversely, data-rich regions introduce complexities in reconciling disparate reference datasets created with varied methodologies. Furthermore, widely available 3D remote sensing resources like digital aerial photogrammetry (DAP) remain vastly underutilized, despite directly measuring fundamental attributes like canopy height. A final, critical gap is that remote sensing maps are rarely evaluated for one of their primary intended applications, which is aiding in the estimation of population means or totals for vegetation attributes over distinct management areas. To address these gaps, this dissertation investigates the strategic integration of multi-sensor remote sensing and diverse reference datasets across forest and savanna ecosystems. Crucially, each investigation moves beyond map development to evaluate how these products improve population estimates of vegetation structure using post-stratified and small area estimators. This hybrid approach empowers land managers and forest inventory programs by pairing time-series maps that visualize continuous landscape dynamics with rigorous statistical estimates that deliver defensible, population-level metrics for adaptive decision-making. The first chapter evaluates combining remote sensing predictors from multiple sources for extending canopy measurements from spaceborne waveform lidar (GEDI) across data-limited African savannas. By combining optical time series, synthetic aperture radar, and environmental covariates, parsimonious models successfully quantified annual structural changes from herbivory and woody encroachment, though the estimated magnitude of change was muted. The second chapter examines how the selection of canopy cover reference data for building Landsat-based canopy cover maps ultimately influences the precision of population estimates of forest area and aboveground biomass in the Rocky Mountains. Comparing diverse field, aerial, and lidar-based canopy cover sources revealed that the efficiency of post-stratified estimators for aboveground biomass is driven primarily by the plot-level correlation between the reference data and the target variable, rather than marginal improvements in mapping accuracy. Finally, the third chapter demonstrates the operational viability of multi-temporal DAP for statewide forest structure estimation and post-fire canopy change assessment. While DAP struggled to capture residual standing dead stems and sparse woodlands at high resolutions, spatial aggregated canopy height models closely matched airborne lidar benchmarks and achieved nearly identical precision in statewide aboveground biomass estimation. These investigations together advance broad-scale operational use of remote sensing in diverse and complex environments to support forest inventories and effective land management.

Why it matches plant phenotyping methods森林・サバンナの個体群／プロットレベルで樹冠高・樹冠被覆・構造をリモートセンシングにより推定し、異なるセンサーや参照データとの比較、精度評価、航空レーザーとのベンチマークを行っている。植物構造形質の取得・検証が中心であり、単なる生態学的ルーチン測定ではない。

abstractthis dissertation investigates the strategic integration of multi-sensor remote sensing and diverse reference datasets across forest and savanna ecosystems

Explainable AI-Driven Spectral–Frequency–Spatial Multi-Domain Fusion for Poplar Above-Ground Biomass Evaluation →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsポプラの地上部バイオマスという植物形質を、スペクトル・周波数・空間情報の融合と説明可能AIで評価する手法が題名上の中心であり、植物フェノタイピング手法として採用する。

titleExplainable AI-Driven Spectral–Frequency–Spatial Multi-Domain Fusion for Poplar Above-Ground Biomass Evaluation

Noninvasive Sensing of Foliar Moisture in Hydroponic Crops Using Leaf-Based Electric Field Energy Harvesters →

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of R2=0.697 and R2=0.794 for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops.

Why it matches plant phenotyping methods葉の電気応答を用いて葉面水分量と生体重量を推定する非侵襲センシング手法を開発・評価しており、植物表現型の取得が研究の中心である。

abstractthis work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response.

Spatio-temporal prediction of total and legume dry matter yield using UAV-borne RGB and multispectral images in alfalfa-grass mixtures →

Abstract Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.

Why it matches plant phenotyping methodsUAV画像から草冠高・スペクトル情報を抽出し、乾物収量とマメ科収量を予測する手法の開発・検証が研究の中心であり、植物の収量形質を直接推定している。

abstractThis study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites.

Spatio-temporal prediction of total and legume dry matter yield using UAV-borne RGB and multispectral images in alfalfa-grass mixtures →

Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.

Why it matches plant phenotyping methodsUAV画像から草冠高・スペクトル情報を抽出し、乾物収量という植物形質を予測する手法の開発・検証が中心です。

abstractThis study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites.

The experience of using NDVI and color vegetation indexes of UAVs to identify phytocenotic parameters of steppe vegetation →

The spatial and temporal variability in the development of steppe fires is largely due to the peculiarities of seasonal, interannual and long-term vegetation conditions. At the same time, there is no objective understanding of which phytocenotic parameters of steppe vegetation are recorded by spectral vegetation indices (SVI) based on Earth remote sensing materials. In this regard, the purpose of the study was to identify these correspondences, as well as to test aerial photography materials as an additional source of detailed information about the spatial and temporal structure of vegetation cover. In the area located in the foothill steppes of the Southern Urals, field studies were conducted in 2023–2024, during which vegetation descriptions and mowing were carried out monthly during the growing season (April–October), aerial photography using a UAV with a standard RGB-sensor. Vegetation is represented by the Stipa zalesskii–Festuca valesiaca–Stipa capillata community and its pasture-degraded variant Ceratocarpus arenarius–Potentilla bifurca. The objective advantage of aerial photographs and color vegetation index (CVI) NDI, VARI, ExG, GLI, ExGR and ExR distribution schemes based on them is their high level of detail, which makes it possible to assess the features of spatial differentiation of vegetation cover, the direction of seasonal and long-term changes. It was revealed that the values of NDVI Sentinel-1–2 and CVI were most often highly correlated with the projective cover of green vegetation. A close relationship was observed with other phytocenotic indicators (total projected coverage, total phytomass reserves and green vegetation) in the hydrothermal conditions favorable for vegetation in 2024 due to the abundance of green vegetation. The results obtained suggest that the main factors of discrepancies between spectral and color indexes and the actual parameters of steppe communities are: a) the overlap of green vegetation with dead phytomass; b) the small share of diverse grasses in the structure of steppe communities; c) seasonal differences in the spectral response of dominant plants and in the aspects they create; d) morphological features of plant species, the volumetric structure of vegetation cover created by them. The revealed phenological reactions of communities to the features of hydrothermal conditions in 2023 and 2024 indicate a high degree of interannual and seasonal variability in their fire status.

Why it matches plant phenotyping methodsUAV RGB画像、色植生指数、衛星NDVIを用いて植生被覆・植物量などの植物群落特性を推定し、現地測定との対応関係を検証しており、植物形質取得法が研究の中心です。

abstractthe purpose of the study was to identify these correspondences, as well as to test aerial photography materials as an additional source of detailed information about the spatial and temporal structure of vegetation cover.

Soybean Yield Prediction with High-Throughput Phenotyping Data and Machine Learning →

The non-destructive estimation of grain yield could increase the efficiency of soybean breeding through early genotype testing, allowing for more precise selection of superior varieties. High-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was utilized to collect data on plant density (PD), plant height (PH), canopy cover (CC), biomass (BM), and various vegetation indices (VIs) from different stages of soybean development. These traits were used within random forest (RF) and partial least squares regression (PLSR) algorithms to develop models for soybean yield estimation. The initial RF model produced more accurate results, as it had a smaller error between actual and predicted yield compared with the PLSR model. To increase the efficiency of the RF model and optimize the data collection process, the number of predictors was gradually decreased by eliminating highly correlated VIs and selecting the most important variables. The final prediction was based only on several VIs calculated from a few mid-soybean stages. Although the reduction in the number of predictors increased the yield estimation error to some extent, the R2 in the final model remained high (R2 = 0.79). Therefore, the proposed ML model based on specific HTPP variables represents an optimal balance between efficiency and prediction accuracy for in-season soybean yield estimation.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物形質を取得し、機械学習でダイズ収量を推定するワークフローが研究の中心であり、形質抽出・予測モデルの開発と効率化を扱っている。

abstractHigh-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models.

Application of NDVI-Based Crop Sensor in Alfalfa Selection for Improving Breeding Process →

Alfalfa (Medicago sativa) is a globally important forage crop; however, improvements in its biomass yield have stagnated due to its complex genetic architecture and the costly, labor-intensive phenotyping. This study evaluated the potential of the normalized difference vegetation index (NDVI) to predict biomass yield and enhance selection efficiency in alfalfa breeding programs. Specifically, nineteen alfalfa experimental populations (AEXP 1–19) and one control cultivar (OS 66) were evaluated over two growing seasons in Croatia. NDVI was measured at four development stages using a GreenSeeker sensor and compared with forage yield, dry matter yield, and plant height. NDVI values varied significantly among genotypes, years, and growth stages, ranging from 0.23 to 0.87, and increased consistently from early to late vegetative phases. Strong positive correlations were observed between NDVI and forage yield (r = 0.543–0.843) and plant height (r = 0.537–0.738) at early vegetative, late vegetative, and early bud stages. Conversely, NDVI at the mid-vegetative stage correlated negatively with yield and height (r = –0.622 to –0.794). High-performing populations (AEXP 2, AEXP 15, AEXP 18) also exhibited the highest NDVI values. NDVI is a reliable, non-destructive indicator for early selection of high-yielding alfalfa genotypes, although multi-location validation is advised to confirm its broader applicability.

Why it matches plant phenotyping methodsNDVIセンサーによる非破壊的な生育・収量形質推定を育種選抜へ適用し、収量や草丈との相関で妥当性を評価しており、フェノタイピング手法が中心である。

abstractThis study evaluated the potential of the normalized difference vegetation index (NDVI) to predict biomass yield and enhance selection efficiency in alfalfa breeding programs.

Assessing multiple remotely sensed data and model-assisted inference for biomass estimation of an Atlantic Forest fragment →

Forest biomass quantification (Mg ha⁻¹) is essential for ecosystem monitoring, especially in areas under anthropogenic pressure, such as Atlantic Forest fragments. This study aimed to compare remote sensors in biomass mapping and population stock estimation of an Atlantic Forest fragment. Ten 0.1 ha plots were randomly distributed within a 17 ha fragment. Data from Sentinel-1 (S1), Sentinel-2 (S2), digital aerial photogrammetry (DAP), and their fusion were evaluated for the construction of predictive models. Linear models with two predictors were fitted: one for each sensor and another using data fusion, selecting the best predictors among all. The models were applied to estimate stand biomass using a regression estimator. The data fusion model showed the best predictive performance (RMSE = 41%), while the DAP-based model had the highest error (RMSE = 64%). However, the most accurate population estimate was obtained with the S2-based model (SE = 21 Mg ha-1), with a relative efficiency 7% higher compared to the traditional inventory (SE = 22 Mg ha-1). Estimates based on DAP, S1, and fusion were less accurate than those from the field inventory. The selected metrics, such as vegetation indices (S2) and textural metrics (S1), reflected the sensors' sensitivity to canopy structure and foliage abundance. DAP showed limitations, possibly due to its low canopy penetration. It is concluded that although the data fusion between DAP and S2 produced the best model for biomass mapping, S2 alone proved more advantageous for population estimates in forest fragments with limited sampling.

Why it matches plant phenotyping methods複数のリモートセンシング手法とデータ融合を比較し、森林キャノピー由来のバイオマス推定モデルを構築・性能評価している。植物群落の明示的な形質推定が中心であり、単なる生物学的実験の routine 測定ではない。

abstractThis study aimed to compare remote sensors in biomass mapping and population stock estimation of an Atlantic Forest fragment.

Biophysical dynamics and wave attenuation in a constructed saltmarsh: Insights from laboratory and remote sensing data. →

Successful establishment and growth of constructed saltmarshes can be evaluated through consistent monitoring of plant biophysical parameters, such as aboveground biomass and leaf area index. Monitoring during the early establishment stage is vital for ensuring the long-term effectiveness of constructed saltmarshes in delivering anticipated ecosystem services, including wave energy dissipation, which strongly depends on vegetation biophysical characteristics. Efficient, low-disturbance methods are needed for the successful adoption of such monitoring plans. This study combines laboratory measurements and remote sensing observations to evaluate the performance of vegetation indices in capturing changes in aboveground biomass, leaf area index, and wave energy dissipation in a constructed saltmarsh. Allometric equations were also investigated to predict aboveground biomass from non-destructive plant traits. Results showed acceptable correlations between vegetation indices, measured biophysical parameters and wave energy dissipation characteristics. All species performed better with NIR-R-based indices for leaf area index, while aboveground biomass predictions varied, with both NIR-R- and G-R-based indices performing best depending on species. Wave energy dissipation also correlated with vegetation indices, aligning closely with the best predictors of aboveground biomass, particularly when vegetation was submerged. These findings indicate that remote sensing combined with allometric equations offers a promising method for monitoring newly established marshes and estimating their biophysical parameters, which serve as key indicators of successful establishment and initial wave energy dissipation.

Why it matches plant phenotyping methodsリモートセンシング指標とアロメトリック式によって植物のバイオマスや葉面積指数を推定し、その性能を評価することが研究の中心であるため、植物フェノタイピング手法として適格。

abstractThis study combines laboratory measurements and remote sensing observations to evaluate the performance of vegetation indices in capturing changes in aboveground biomass, leaf area index, and wave energy dissipation in a constructed saltmarsh.

Improved Estimation of Cotton Aboveground Biomass Using a New Developed Multispectral Vegetation Index and Particle Swarm Optimization →

Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index termed CGSIVI. Moreover, the applicability of various machine learning algorithms—including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)—was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSIVI) and AGB was enhanced by 13.60% overall, with the CGSICIre exhibiting the highest correlation with cotton AGB (R2 = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the flowering stage. Furthermore, when the CGSIVI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenotyping analysis method enables rapid and accurate estimation. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field.

Why it matches plant phenotyping methods綿花の地上部バイオマスをUAVマルチスペクトルデータと新規植生指数・機械学習で推定する手法を開発・評価しており、植物形質取得が中心である。

abstractThis study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB.

Allometric equations for orchard and vineyard trees: enhancing AFOLU-based climate change mitigation. →

Introduction Perennial orchard systems are emerging as important yet underrepresented carbon sinks within the AFOLU sector, which contributes 20-24% of global GHG emissions. Many countries still rely on Tier 1 default values that fail to capture the structural and management characteristics of orchard species. Accurate biomass and carbon estimation, particularly through species-specific allometric equations, is essential for improving Tier 2-3 GHG reporting and recognizing orchards as meaningful contributors to climate-smart land management. Methods A systematic literature review was conducted using five major databases (2008-2024), following PRISMA guidelines. From 240 initial records, 53 studies met the inclusion criteria. These were categorized into three domains: (i) biometric modeling of fruit-tree biomass, (ii) species-specific allometric equation development, and (iii) carbon-sequestration assessments. Methodological trends, model performance, and research gaps were synthesized to inform an IPCC-aligned framework for orchard-specific emission and removal factors. Results Most studies were concentrated in Asia and the Mediterranean and focused on citrus, mango, apple, grape, and olive systems. Power-law allometric models dominated and generally showed high predictive performance (R² > 0.90) with variables such as diameter, height, and crown dimensions. However, major gaps remained: limited data for belowground biomass, juvenile trees, grafted architectures, vineyards, and uncertainty quantification-all of which restrict Tier 2-3 applicability. Discussion Based on these findings, this review proposes a standardized methodological framework linking biometric measurements, species-specific allometric modeling, remote-sensing integration, and uncertainty analysis to derive orchard-specific emission and removal factors consistent with IPCC guidance. Broader adoption of such protocols would improve transparency and accuracy in national AFOLU inventories and strengthen recognition of perennial orchards as viable nature-based climate solutions that support national net-zero targets.

Why it matches plant phenotyping methods果樹・ブドウ樹のバイオマスという植物形質を推定するアロメトリック手法を体系的にレビューし、計測・モデル化・リモートセンシング・不確実性分析を統合した標準化フレームワークを提案しており、手法が中心である。

abstractA systematic literature review was conducted using five major databases (2008-2024), following PRISMA guidelines.

Metabolomic signatures predict heterotic performance in opium poppy (Papaver somniferum). →

Metabolomics-based prediction models are reshaping hybrid breeding by enabling early and accurate identification of superior genotypes. Papaver somniferum (opium poppy), a pharmaceutically important crop producing benzylisoquinoline alkaloids, is particularly suited to benefit from this approach. Although metabolomics and machine learning have been widely applied in food and oilseed crops, their potential remains largely unexplored in medicinal plants, especially opium poppy. Here, we developed a metabolomics-guided, machine learning-assisted framework to predict hybrid performance and accelerate breeding progress. A cross between two genetically divergent parental lines, Sujata (low alkaloid) and Thial (high alkaloid), yielded 233 F 1 hybrids evaluated at physiological maturity for five key alkaloids and three yield-related traits. Principal component analysis separated phenotypic variation into chemical and agronomic axes, revealing distinct hybrid clusters. Six machine learning algorithms (Multiple Linear Regression, Ridge Regression, LASSO, Random Forest, XGBoost, and Support Vector Regression) were applied under three predictive scenarios: metabolomics-only (MP), morphology-only (MMP), and combined metabolomics-morphology (MM_MP). Random Forest, XGBoost, and LASSO consistently achieved the highest prediction accuracy, particularly under the MP and MM_MP frameworks. Noscapine (R 2 = 0.654) and morphine (R 2 = 0.611) emerged as the most reliably predicted traits. Clustering analyses identified elite alkaloid-rich chemotypes (e.g., H83, H181) and dual-purpose ideotypes (e.g., H221, H78) that combined high alkaloid content with strong biomass yield. Feature importance analysis highlighted biosynthetic relationships among alkaloids, offering mechanistic insights into trait regulation. Overall, this study demonstrates a scalable metabolomics-assisted prediction framework that enables first-cycle ranking of F 1 hybrids from mature-stage data, thereby supporting earlier decisions without assuming cross-stage predictability.

Why it matches plant phenotyping methods代謝物・形態情報を用いてアルカロイドおよび収量関連形質を予測する機械学習フレームワークを開発・比較評価しており、植物形質の推定手法が研究の中心である。

abstractHere, we developed a metabolomics-guided, machine learning-assisted framework to predict hybrid performance and accelerate breeding progress.

An investigation of three machine learning models using UAV derived photogrammetry for detecting and measuring tree stumps →

Accurately quantifying stump volume on post-harvested sites is required to assess potential volume gains for biomass utilisation. The relatively uniform distribution and shape of stumps across such sites makes them well-suited for detection using machine learning (ML) algorithms. Recent developments in the analysis of Digital Aerial Photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAVs) have enabled the reliable identification of stumps via advanced ML methods. Furthermore, the processed outputs from these algorithms provide estimates of stump diameter and height, facilitating calculations of biomass volume. This integration of UAV-based photogrammetry and ML techniques presents a promising approach for enhancing forest management and biomass assessment. In this study, we trained three different ML model types: Faster Region-based Convolutional Neural Network (R-CNN), Single Shot Multibox Detector (SSD) and You-Only-Look-Once (YOLO). The data for the virtual stump detections came from two Norwegian sites, with stumps of Picea abies (L.) H.Karst., and three South African sites, with stumps of Pinus patula Schiede ex Schltdl. & Cham. We assessed the detection rates of each model and compared metrics by using similarly annotated images. The resultant encapsulating bounding boxes of detected stumps were used to calculate diameters and compared to field measurements. Each bounding box is rectangular in shape, and the average of the height and width was calculated to get an estimated diameter value. Virtual stump heights were determined from the Digital Surface Model (DSM) by subtracting the mean height of the surrounding area from the mean height of the stump. The calculated heights of the stumps can be used to assess potential loss of wood volume due to inefficient harvesting techniques. Similarly, the calculated wood volume can be used to estimate residual biomass, and therefore assist Foresters in deciding how best to utilise these stumps. Visible stumps on post-harvested sites could be detected with high rates of accuracy, with almost perfect precision from some object detection models, albeit at low levels of recall. Overall, all three model types had an F1-score of above 73% with the best model attaining an F1-score of 89%. Stump diameters were generally overestimated and this was not found to be related to stump size. Stump heights were underestimated in most cases.

Why it matches plant phenotyping methodsUAV画像と機械学習を用いて切り株を検出するだけでなく、直径・高さ・体積を推定し、野外測定と比較検証しているため、植物器官形質の取得手法が中心である。

abstractThe resultant encapsulating bounding boxes of detected stumps were used to calculate diameters and compared to field measurements.

High throughput phenotyping of energy cane using uncrewed aircraft system (UAS) and machine learning →

Accurate estimation of biomass in energy cane is essential for cultivar selection in breeding programs and biomass supply forecasting in bioenergy production. This study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP). Seven cultivars were monitored between December 2023 and July 2024 at an experimental field in Weslaco, Texas. Structural metrics such as percentile-based heights, canopy volume, and interaction variables were extracted and used to train four machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and a one-dimensional Convolutional Neural Network (1D-CNN). Ensemble tree algorithms consistently outperformed CNN, with XGBoost and LightGBM providing the most stable and interpretable predictions. For single sensor inputs, lidar models (R² = 0.70–0.78; RMSE = 1.36–1.60 kg/m²) generally outperformed RGB-SfM (R² = 0.69–0.73; RMSE = 1.52–1.63 kg/m²), though RGB-SfM performed competitively with XGBoost. Fused models combining lidar and RGB-SfM features achieved the highest accuracies (XGBoost: R² = 0.80, RMSE = 1.31 kg/m²; LightGBM: R² = 0.79, RMSE = 1.36 kg/m²), mitigating the underestimation of high-biomass plots in RGB-SfM and the slight overestimation of lidar at the upper tail. Cultivar specific analysis confirmed TH16–22 as the top performer, followed closely by Ho02–113 and TCP10–4928, demonstrating the capacity of UAS HTP to support breeding decisions. These findings confirm the biological relevance of percentile-based height metrics (particularly the 75th percentile), canopy volume, and their interactions for biomass accumulation and underscore the value of sensor fusion in reducing systematic bias. This study provides systematic demonstration of lidar and RGB-SfM fusion for biomass estimation in energy cane, establishing a scalable and non-destructive approach that advances high-throughput phenotyping and supports the development of sustainable bioenergy cropping systems. • Ensemble models (RF, LightGBM) outperform CNN in UAS phenotyping at plot scale. • Canopy volume and percentile heights identified as key biomass predictors. • Fusion of UAS lidar and RGB-SfM improves energy cane biomass estimation. • Sensor fusion mitigates RGB underestimation and lidar overestimation biases. • UAS-HTP supports energy cane cultivar screening for bioenergy applications.

Why it matches plant phenotyping methodsUAS lidar・RGB-SfMによるバイオマス推定と、特徴抽出・機械学習・センサ融合の検証が研究の中心であり、再利用可能な高スループット表現型計測手法を評価している。

abstractThis study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP).

An Efficient Biomass Estimation Model for Large-Scale Olea europaea L. by Integrating UAV-RGB and U2-Net with Allometric Equations →

Olea europaea L. is an economically and ecologically significant species, for which accurate biomass estimation provides critical insights for artificial propagation, yield forecasting, and carbon sequestration assessments. Currently, research on biomass estimation for Olea europaea L. remains scarce, and there is a lack of efficient, accurate, and scalable technical solutions. To address this gap, this study achieved, for the first time, non-destructive estimation of Olea europaea L. biomass across individual tree to plot scales by integrating UAV-RGB (Unmanned Aerial Vehicle-Red-Green-Blue) imagery with the U2-Net model. This study initially developed allometric models for W-D-H, CA-D, and CA-H in Olea europaea L. (where W = biomass, D = ground diameter, H = tree height, and CA = canopy area). A single-parameter CA-based whole-plant biomass model was subsequently developed utilizing the optimal models. An innovative whole-plant biomass estimation model (UAV-RGB, U2-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U2-Net at the sample-plot level was developed and assessed. The results revealed the following: (1) The model for Olea europaea L. aboveground biomass (AGB) was WA = 0.0025D1.943H0.690 (R2 = 0.912), the model for belowground biomass (BGB) was WB = 0.012D1.231H0.525 (R2 = 0.693), the model for CA-D was D = 4.31427C0.513 (R2 = 0.751), CA-H model was H = 226.51939C0.268 (R2 = 0.500). (2) The optimal AGB model for CA single-parameter was WA = 1.80901C1.181 (R2 = 0.845), and the model for BGB was WB = 1.25043C0.772 (R2 = 0.741). (3) The R2 of Olea europaea L. biomass, as estimated by CA derived from the U2-Net and UUTB models, was 0.855. This study presents the first integration of UAV-RGB imagery and the U2-Net model for biomass estimation in Olea europaea L., which not only addresses the research gap in species-specific allometric modeling but also overcomes the limitations of traditional manual measurement methods. The proposed approach provides a reliable technical foundation for accurate assessment of both economic yield and ecological carbon sequestration capacity.

Why it matches plant phenotyping methodsUAV-RGB画像とU2-Netを統合し、個体からプロット規模でオリーブのバイオマスという植物形質を非破壊推定する手法を開発・評価しており、表現型取得・推定が研究の中心である。

abstractAn innovative whole-plant biomass estimation model (UAV-RGB, U2-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U2-Net at the sample-plot level was developed and assessed.

Integrating UAV Multi-Temporal Imagery and Machine Learning to Assess Biophysical Parameters of Douro Grapevines →

The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R2 = 0.83) and yield (R2 = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R2 = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習により、ブドウのLAI、バイオマス、収量を推定する手法を開発・比較しており、表現型取得と推定が研究の中心である。

abstractThis study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield

EcoBOT: an AI/ML enabled automated phenotyping capability for model plants. →

Introduction Advances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools. Methods The EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated. Results The results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement. Discussion The findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.

Why it matches plant phenotyping methodsEcoBOTという自動化プラットフォームを開発し、画像による植物成長・健康状態のモニタリングと、画像に基づくバイオマス推定を中核としているため、植物フェノタイピング手法として採用する。

abstractThis study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools.

Multi-modal feature integration from UAV-RGB imagery for high-precision cotton phenotyping: A paradigm shift toward cost-effective agricultural remote sensing →

Cost-effective remote sensing solutions are critically needed to democratize precision agriculture technologies. While hyperspectral and LiDAR systems deliver high accuracy, their prohibitive costs limit widespread adoption. This study demonstrates that systematic multi-modal feature integration transforms standard UAV-based RGB imagery into a powerful phenotyping instrument, achieving crop trait prediction accuracy comparable to systems costing 10–50 times more. We developed a comprehensive framework integrating spectral indices, geometric parameters, and texture metrics from commodity RGB sensors to predict five critical cotton traits: leaf area index (LAI), intercepted photosynthetically active radiation (IPAR), above-ground biomass, lint yield, and seed cotton yield. The progressive integration approach employed Random Forest regression with four feature configurations: baseline color indices (CIbₐₛₑ), refined color indices (CIᵣₑf), geometric parameters (CIᵣₑf + GP), and texture metrics (CIᵣₑf + GP + T). Field experiments across three trials over two growing seasons (2022–2023) with varying genotypes, planting densities, and sowing dates provided 2,126 ground truth measurements for model development and validation. The optimal multi-modal model achieved R² = 0.97 for IPAR (rRMSE = 6 %), R² = 0.91 for LAI (rRMSE = 15 %), and R² = 0.85 for biomass (rRMSE = 32 %), with lint yield and seed cotton yield demonstrating R² values of 0.92 and 0.77, respectively. Variance partitioning analysis revealed texture features as the dominant contributor (16.2 % ± 7.1 %), followed by spectral indices (9.1 % ± 4.2 %) and geometric parameters (8.0 % ± 2.8 %), with substantial shared variance (45–65 %) indicating strong feature complementarity. Phenological analysis demonstrated that flowering-stage imagery outperformed boll opening stage measurements, while stage-general models showed superior robustness. Cross-temporal validation confirmed model generalizability, with trial-general models achieving R² values of 0.91–0.97 for IPAR across diverse environmental conditions. The framework enables sub-meter spatial resolution trait mapping while maintaining operational simplicity and cost-effectiveness, demonstrating that systematic feature engineering can democratize high-precision phenotyping technologies for broader agricultural applications.

Why it matches plant phenotyping methodsUAV-RGB画像から複数の綿形質を推定する特徴統合フレームワークを開発・検証しており、形質取得・抽出手法が研究の中心である。

abstractThis study demonstrates that systematic multi-modal feature integration transforms standard UAV-based RGB imagery into a powerful phenotyping instrument

Field-scale single-plant sunflower head detection and geometric parameters measurement integrating multi-modal UAV data, deep learning and point cloud analysis →

Accurate monitoring of sunflower heads is critical for yield prediction, yet traditional methods are labor-intensive. This study proposed a novel framework integrating UAV remote sensing, deep learning, and point cloud analysis to address this challenge. The proposed method used a Dual-Branch YOLOv10n model, leveraging multi-modal data for precise detection of sunflower heads at various growth stages. Feature indices were designed and a two-step clustering technique was applied to extract sunflower head point clouds, from which geometric parameters such as diameter and volume are computed. The detection model achieved high accuracy (precision: 0.9, recall: 0.894, mAP@50: 0.932) across growth stages. A strong correlation (R² = 0.80) was found between diameter measurements from point cloud and ground-truth data, while volume showed good alignment with biomass (R² = 0.61). This method offers an innovative, efficient solution for field-scale crop monitoring and yield estimation, advancing agricultural practices.

Why it matches plant phenotyping methodsUAV画像・深層学習・点群解析を統合し、ヒマワリ頭部の検出から直径・体積という植物器官形質を抽出・検証する方法が研究の中心であるため。

abstractThis study proposed a novel framework integrating UAV remote sensing, deep learning, and point cloud analysis to address this challenge.

An enhanced stacking algorithm for inversing cotton growth parameters using multi-source remote sensing data →

Cotton is a significant broadacre crop globally, and monitoring its growth is crucial for improving agricultural productivity. With the development of unmanned aerial vehicle (UAV) remote sensing technology, the inversion of cotton growth parameters (including plant height (PH), and leaf chlorophyll content (LCC), leaf area index (LAI), above-ground biomass (AGB)) from remote sensing data has emerged as a prominent research area. To address the issue of limited accuracy in traditional stacking algorithm for remote sensing inversion, this study proposes an enhanced stacking algorithm (ESA). First, multi-source remote sensing data is acquired using UAV equipment equipped with an integrated payload of a LiDAR sensor and a visual RGB camera and raw features are extracted from the data. Then, principal component analysis (PCA) is used to reduce the dimensionality of these features. The model construction is optimized through the following steps: first, explore all feature combinations and train each using multi-class learners to construct the entire set of base models; second, remove over- or under-fitting models to build a candidate pool; third, introduce iterative screening to the pool—each round incorporates the algorithm with the greatest performance gain and removes those with negative contributions, iterating to construct an efficient subset of base models; finally, RidgeCV is used to fuse base-model outputs. The experimental results show that ESA outperforms other traditional methods in terms of prediction performance for the four growth parameters. Specifically, on the test set, the R2 values for PH, LCC, LAI, and AGB are 0.9320, 0.8015, 0.8638, and 0.8272 , respectively. Compared with the second-best model, the relative improvement is approximately 4.6% (PH), 3.6% (LCC), 7.5% (LAI), and 11.2% (AGB) . ESA offers an effective approach for high-precision inversion of cotton growth parameters, providing new insights for the precision management of other crops.

Why it matches plant phenotyping methodsUAVのLiDAR・RGBデータから綿花の複数生育形質を推定するスタッキングアルゴリズムを開発し、他手法と性能比較しており、表現型取得・推定法が中心です。

abstractthis study proposes an enhanced stacking algorithm (ESA)

Decoding canola and oat crop health and productivity under drought and heat stress using bioelectrical signals and machine learning →

Abiotic stresses, such as heat and drought, often reduce crop yields by harming plant health. Plants have evolved complex signaling networks to mitigate environmental impacts, making monitoring in-situ biosignals a promising tool for assessing plant health in real time. In this study, needle-like sensors were used to measure electrical potential changes in oat and canola plants under heat and drought stress conditions. Signals were recorded over a 30-min period and segmented into time intervals of 1-, 5-, 10-, 20-, and 30-min. Machine learning algorithms, including Random Forest, K-Nearest Neighbors, and Support Vector Machines, were applied to classify stress conditions and estimate biomass based on 14 extracted bioelectrical features, such as signal amplitude and entropy. Results showed that heat stress primarily altered signal patterns, whereas drought stress affected the signal intensity, possibly due to a reduction in the flow rate of charged ions. Random Forest classifier successfully identified over 85 % of stressed crops within 30 min of signal recording. These signals also explained 58–95 % of the variation in plant aboveground and root biomass, depending on stress intensity and crop genotype. This study demonstrates the potential of using bioelectrical sensing as a rapid and efficient tool for stress detection and biomass estimation. Future research should explore the ability to use biosensors to capture genetic variability to mitigate abiotic stresses and combine this with remote sensing and other emerging precision agriculture technologies.

Why it matches plant phenotyping methods植物の生体電気信号を測定し、機械学習でストレス状態を分類するとともにバイオマスを推定するセンシング手法が研究の中心であり、植物表現型の取得・推定方法を実質的に評価している。

abstractneedle-like sensors were used to measure electrical potential changes in oat and canola plants under heat and drought stress conditions.

Formulation of Image-based Mass Model of Indian Potato (Solanum tuberosum) With Selected Dimensional Attributes Using Artificial Neural Network and Response Surface Method →

Accurate mass estimation of agricultural products plays a vital role in enhancing post-harvest handling, storage and processing efficiency. This study presents a constructive approach to modelling the mass of potato (Solanum tuberosum) using targeted dimensional attributes, employing both artificial neural networks (ANN) and response surface methodology (RSM). By leveraging a dataset that encompasses essential physical parameters, such as length, width, projected area and perimeter, we have successfully constructed predictive models that can significantly improve the accuracy of mass estimations. The ANN model was carefully trained and optimised to effectively capture the nonlinear relationships inherent in the data, while the RSM approach offers a robust analytical framework for examining variable interactions. Our performance evaluation, utilising metrics like the coefficient of determination (R²), root mean square error (RMSE) and mean square error (MSE), reveals that the ANN model excels in predicting potato mass, surpassing traditional regression techniques. Notably, the RSM exhibited an impressive R² of 0.9981, and the ANN model reached an R² of 0.9943, both demonstrating a strong correlation with actual mass measurements. These findings underscore the potential of our proposed models for image-based weight estimation, facilitating improved sorting and grading of potatoes. By implementing these innovative mass estimation techniques, we can enable automated sorting processes, enhance yield estimations, optimise supply chains, conduct non-destructive quality assessments, incorporate smart farming practices and minimise post-harvest losses—all while ensuring standardised food processing. In conclusion, this research highlights the promising avenues for intelligent modelling techniques in agricultural engineering, offering scalable solutions that can significantly contribute to the efficiency and effectiveness of mass estimation in the agricultural sector.

Why it matches plant phenotyping methods画像からジャガイモ塊茎の寸法を抽出し、ANNおよびRSMで質量を推定する手法の構築・評価が研究の中心であり、植物器官形質の測定法に該当する。

abstractThis study presents a constructive approach to modelling the mass of potato (Solanum tuberosum) using targeted dimensional attributes, employing both artificial neural networks (ANN) and response surface methodology (RSM).

Evaluating the potential of very high-resolution satellite data for the enhanced estimation of rice aboveground biomass by combining spectral and spatial information →

Monitoring aboveground biomass (AGB) using high spatial and temporal resolution remote sensing data is important for smart agriculture. Significant technological advances have been made in developing satellites with very high spatial resolution, delivering a promising avenue for vegetation observations. However, the high costs and limited revisit periods of high-resolution satellites hinder their widespread use, leaving the feasibility of combining vegetation indices (VIs) and textures derived from satellite images for AGB estimation uncertain and the quantitative improvements achieved by incorporating textures into estimation unclear. Airborne hyperspectral imaging with high spectral and spatial resolution offers a fresh opportunity to simulate the satellite imaging process objectively and realistically across both spectral and spatial dimensions. The study first evaluated the potential benefits of combining textures and VIs derived from different high-resolution satellites to enhance AGB retrieval. Rice samples and UAV hyperspectral data were collected throughout the rice growth cycle over three consecutive years. Each hyperspectral image was resampled in spectral and spatial dimensions to simulate nine multispectral satellites with sub-meter spatial resolution (WorldView-3, WorldView-2, GeoEye-1, SuperView-1C, GaoFen-2, Beijing-2, Jilin-1, GeoSat-2, KomPast-2). VIs, textures, and their combinations were employed to establish AGB models for the pre-heading, post-heading, and the entire growth stage, respectively. The results showed that combining VIs and textures always achieved the greatest rice AGB estimations, with the integration of multiple satellite data always yielding the best outcomes (overall validation rRMSE ≤ 0.35). For the texture-based monitoring, the impact of satellite spatial resolution was more pronounced on influencing the estimation effectiveness than spectral bands. The monitoring accuracy of rice AGB demonstrated a nonlinear decreasing trend as the spatial resolution dropped, and combining VIs and textures mitigated the negative impact of reduced spatial resolution on the monitoring accuracy of rice AGB. The combination of VIs and textures showed a compensatory effect and combining VIs and textures derived from red-edge band could offset the impact of the reduced spatial resolution on AGB estimation. The involvement of textures in modelling exerted an overall bigger impact on rice AGB estimation than the inclusion of red-edge variables. Satellites with higher spatial resolution and a red-edge band always performed the best in AGB estimation. This study facilitates the optimization of sensor design and farmland management.

Why it matches plant phenotyping methods高解像度リモートセンシング画像からイネの地上部バイオマスを推定する手法を、スペクトル情報・テクスチャ・空間解像度の組合せとして評価・検証しており、植物形質取得が研究の中心である。

abstractThe study first evaluated the potential benefits of combining textures and VIs derived from different high-resolution satellites to enhance AGB retrieval.

Integration of satellite, UAV, soil, and topographic data for assessing corn nitrogen uptake at early vegetative growth stages →

PURPOSE: Spatial variability within fields and unpredictable rainfall patterns make nitrogen (N) management challenging, with up to 65% of applied N being lost to the environment. Post-emergence sidedress applications of N fertilizer can improve plant uptake and reduce N losses, making it critical to efficiently identify corn (Zea mays L.) N status at early growth stages. We hypothesized that indicators of plant structure (plant height and canopy cover fraction), canopy greenness (vegetation indices), and their integration with soil and topography-related properties would improve the prediction of early-season corn N status. The objectives of this study were to: (1) evaluate plant height, canopy cover fraction (CCF), and vegetation indices (VI) as indicators of biomass, N concentration, and N uptake at early growth stages (~ V4); (2) assess whether linear models integrating UAV-derived CCF with VI improve N uptake prediction; and (3) determine whether incorporating soil and topographic parameters from publicly available datasets into machine learning (ML) models improves performance over linear regressions. METHODS: Two large-scale field trials were conducted in Indiana during the 2019 growing season. Multispectral UAV (MicaSense Altum, 0.03 m resolution) and satellite imagery (Planet, 3 m resolution) were acquired and processed to extract CCF and calculate VI. Biomass samples were collected to determine N uptake. Linear regressions and three ML models were evaluated. RESULTS: Plant structural metrics, CCF and plant height, were the most reliable predictors of biomass and N uptake (R² up to 0.95). Integrating CCF with NIR-based VI improved or maintained model performance. Adding soil and topographic metrics provided limited improvement. CONCLUSION: Linear regression models performed comparably to ML approaches, emphasizing the utility of simpler models for supporting more efficient in-season fertilizer applications. Performance differences across sites reflected variability in crop development and underscore challenges in model generalization.

Why it matches plant phenotyping methodsUAV・衛星画像から植物構造、キャノピー被覆率、植生指数を抽出し、トウモロコシのバイオマスおよび窒素吸収量を予測する手法を比較・評価しており、フェノタイピング手法が中心である。

abstractThe objectives of this study were to: (1) evaluate plant height, canopy cover fraction (CCF), and vegetation indices (VI) as indicators of biomass, N concentration, and N uptake at early growth stages (~ V4); (2) assess whether linear models integrating UAV-derived CCF with VI improve N uptake prediction; and (3) determine whether incorporating soil and topographic parameters from publicly available datasets into machine learning (ML) models improves performance over linear regressions.

Research on the Possibilities of Determining the Structural Characteristics of Plant Communities Dominated by Reeds Using High-Resolution Satellite Imagery, Ground Measurements, and Unmanned Aerial Vehicles →

Abstract— Plant communities dominated by reeds (Phragmites altissimus (Benth.) Mabille, Phragmites australis (Cav.) Trin. ex Steud.) are widely distributed in floodplain and delta landscapes. Despite their significant biospheric role and potential for industrial use, insufficient attention has been paid to the mapping and assessment of these communities in Russia. The objective of this study is to explore the possibilities of mapping biomass and vegetation height in reed-dominated communities in the Volga Delta using Sentinel-1/2 satellite data supported by ground measurements and aerial surveys conducted with a drone. Allometric relationships between the heights, stem diameters of reeds, and biomass were established for 92 sample plots within the Astrakhan Nature Reserve in the Volga Delta enabling the use of aerial imagery to obtain reference data through photogrammetric methods. The application of vegetation height calculated photogrammetrically based on aerial imagery across 27 test polygons combined with temporally distinct satellite data and the Random Forest nonparametric regression method yielded a high accuracy in mapping heights (coefficient of determination R2 = 0.80, root mean square error (RMSE) 0.46 m) and biomass (R2 = 0.65, RMSE = 12.6 t/ha) of reed-dominated communities in the Volga Delta. Thus, the approach employed proves to be effective for mapping the biomass of reed communities in the Volga Delta and similar landscapes.

Why it matches plant phenotyping methodsヨシ群落の高さ・バイオマスという植物形質を、衛星画像、ドローン空撮、写真測量、回帰モデルで推定・検証する方法が研究の中心である。

abstractThe objective of this study is to explore the possibilities of mapping biomass and vegetation height in reed-dominated communities in the Volga Delta using Sentinel-1/2 satellite data supported by ground measurements and aerial surveys conducted with a drone.

Estimating plant height, nitrogen uptake and above-ground biomass using UAV multispectral imaging coupled with machine learning in industrial hemp (Cannabis sativa L.) →

Industrial hemp (Cannabis sativa L.) is known for its high fiber production with lower ecological footprint. Nitrogen (N) status and stem biomass (SB) and total above-ground biomass (AGB) of the crop highly influence fiber quantity and quality. Conventional monitoring practices are labour intensive and time consuming. Unmanned Aerial Vehicles (UAVs) with imaging sensors can be a promising tool for mitigating these challenges. This study evaluated the performance of multispectral camera-equipped UAV in predicting key agronomic parameters, i.e., plant height (PH), Leaf Nitrogen Uptake (LNU) and SB and AGB. Field trials were conducted at UF/IFAS West Florida Research and Education Centre, Jay, FL during the years 2021 and 2022 consisting of two cultivars and six N treatments. The PH was estimated through Crop Height Model, yielding an R² of 0.87 at full crop maturity (90 days after planting). Twenty-seven Vegetation Indices (VIs) were extracted and features, including PH and VIs, were selected through Recursive Feature Elimination with adjusted Variance Inflation Factor (VIF<10) to develop machine learning models for the estimation of yield components. The LNU prediction was best with Support Vector Machine model with R², RMSE and nRMSE % value of 0.364, 34.55 kg N ha⁻¹ and 68.48 respectively. Random Forest Regressor predicted the SB and total AGB most accurately with R², RMSE and nRMSE % value of 0.752 and 0.707, 890.70 and 1492.73 kg ha⁻¹, 48.86 and 43.05 respectively. The results demonstrate the potential of UAVs to generate more reliable estimates of PH, SB and total AGB whereas it remained unreliable for LNU.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて植物高、窒素吸収、茎・地上部バイオマスを推定し、モデル性能を評価しているため、表現型取得・推定法が中心である。

abstractThis study evaluated the performance of multispectral camera-equipped UAV in predicting key agronomic parameters, i.e., plant height (PH), Leaf Nitrogen Uptake (LNU) and SB and AGB.

Multi-sensor proximal remote sensing for cover crop biomass estimation at high and moderate spatial resolutions →

Cover crops play a critical role in providing agroecological services such as improving soil health, reducing erosion and nitrogen loss, and suppressing weeds, which are closely tied to their performance such as accumulated biomass. This study evaluated the Active Canopy Sensor (ACS) -214, an active proximal sensing device equipped with its own light-emitting red and near-infrared spectral reflectance sensors, a time-of-flight laser, and an ultrasonic sensor, for estimating winter cover crop biomass across 13 U.S. states from 2020 to 2024. We assessed 11 species from three functional groups – grasses ( n = 797), legumes ( n = 264), and brassicas ( n = 181) – using Random Forest (RF) models and four cross-validation strategies. The ACS-214 showed moderate to strong prediction accuracy for grasses ( R 2 = 0.51 – 0.64) and legumes ( R 2 = 0.44 – 0.76), though performance declined in leave-one-region-out analyses ( R 2 = 0.06 – 0.46), indicating limited spatial generalizability. Brassica models had low prediction accuracy for all models ( R 2 < 0.30), likely due to flowering and patchy growth. Biomass prediction breakpoints were observed at ∼3000 kg ha −1 for legumes and ∼4000 kg ha −1 for grasses. We also evaluated the effectiveness of using ACS-214 data to train Sentinel-2 satellite imagery for estimating grass cover crop biomass using withheld, out of bag data from 2023 to 2024. Sentinel-2 RF models trained with ACS-214 data showed good agreement with field-sampled ( R 2 = 0.58 – 0.61) and ACS-214-estimated biomass ( R 2 = 0.70). While Sentinel-2 offers scalability, the ACS-214 enables finer-resolution biomass mapping and better accounts for within-field variability, making it an effective tool for localized management and monitoring. These findings support the integration of proximal and satellite sensing approaches to enhance cover crop biomass estimation and agroecological assessment.

Why it matches plant phenotyping methods近接センサーと衛星画像を用いた作物バイオマス推定を開発・評価し、交差検証で精度と空間汎化性を検証しているため、植物形質取得法が研究の中心である。

abstractThis study evaluated the Active Canopy Sensor (ACS) -214, an active proximal sensing device equipped with its own light-emitting red and near-infrared spectral reflectance sensors, a time-of-flight laser, and an ultrasonic sensor, for estimating winter cover crop biomass across 13 U.S. states from 2020 to 2024.

Reciprocal BLUP: A Predictability-Guided Multi-Omics Framework for Plant Phenotype Prediction →

Abstract Sustainable improvement of crop performance requires integrative approaches that link genomic variation to phenotypic expression through intermediate molecular layers. Here, we present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction. Using a panel of 198 soybean accessions grown under well-watered and drought conditions, we first evaluated four direction-specific prediction models (genome→microbiome, genome→metabolome, metabolome→microbiome, and microbiome→metabolome) to estimate the predictability of individual omics features. Then, we evaluated whether subsets of features with high cross-omics predictability improve phenotype prediction. These cross-layer models identify features that play physiologically meaningful roles within multi-omics systems, enabling us to prioritize variables that capture coherent biological signals enriched for phenotype-relevant information. As a result, metabolome features were highly predictable from microbiome data, whereas microbiome predictability from metabolomic data was weaker and more environment-dependent, revealing an asymmetric relationship between these layers. In the subsequent phenotype prediction analysis, the model incorporating predictability-based feature selection substantially outperformed models using randomly selected features and also achieved prediction accuracies comparable to those of the full-feature model. Under drought, phenotype prediction models based on metabolomic or microbiomic kernels (MetBLUP or MicroBLUP) outperformed the genomic baseline (GBLUP) for several biomass-related traits, indicating that environment-responsive omics layers captured phenotypic variation not explained by additive genetic effects. Our results highlight hierarchical interactions among genomic, metabolic, and microbial systems, with the metabolome functioning as an integrative mediator linking genotype, environment, and microbiome composition. The Reciprocal BLUP framework provides a biologically interpretable and practical approach for integrating multi-omics data, improving phenotype prediction, and guiding omics-based feature selection in plant breeding.

Why it matches plant phenotyping methods植物表現型予測のためのマルチオミクス計算フレームワークを提案・評価しており、表現型推定手法が研究の中心である。

abstractwe present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction.

Data-driven localization of the TOMGRO model: Cultivar-specific parameter optimization for Shanghai greenhouse tomato production →

Crop models are an integral component in greenhouse control systems, enabling the simulation of plant responses to environmental conditions and facilitating optimal operational decisions for high productivity with low energy use. However, existing crop models often lack transferability beyond their original development conditions. Additionally, cultivar-specific parameterization remains challenging, as some parameters can be empirically determined while others require complex calibration. This study adapted the reduced TOMGRO model to simulate growth and yield for four local tomato cultivars under Shanghai greenhouse conditions. Through Sobol’s global sensitivity analysis and Bayesian optimization, four highly influential parameters were identified and optimized, including growth efficiency (E), maintenance respiration coefficient (rₘ), extinction light coefficient (K), and leaf quantum efficiency (Qₑ). This combined approach provides an effective framework for model calibration, with the calibrated model achieving an average R² > 0.94 for node number, plant dry weight, fruit dry weight, and leaf area index predictions in all cultivars. Model validation using 2023–2024 greenhouse data confirmed model effectiveness for the target variables (average R² > 0.92 for cultivar QX and > 0.88 for LZ), whereas the model showed limitations in simulating mature fruit growth. This calibrated model offers reliable predictions of key growth variables, informing both plant breeding and greenhouse management.

Why it matches plant phenotyping methods作物モデルの感度分析・ベイズ最適化によるパラメータ校正と、植物成長形質予測の検証が研究の中心であり、再利用可能な計算フェノタイピング手法に該当する。

abstractThrough Sobol’s global sensitivity analysis and Bayesian optimization, four highly influential parameters were identified and optimized

Multimodal GiaC-phenomic approach for microbiome-tree system profiling →

We constructed a computational methodology to assess health of plant-microbiome system through microbiome structure modelling combined with plant remote sensing. As a test dataset, we selected soil mycobiome and morphometry of Tilia cordata in nursery and forest sites. Our method is also applicable on forest or regional scale. Microbiome part called GiaC ( G u i lds a nd o C currences) combines taxonomic and trophic composition as well as species co-occurrence modelled with advanced graph methods. We complemented state-of-the-art approaches with novel ones for visualisations, species filtering (Flexible99) and graph transformation modelling species clusters (ClusterCollapse). Flexible99 is a method that adjusts the species abundance cut-off to each sample set and removes rare species. ClusterCollapse generalises co-occurrence networks to species clusters by edge contraction and serves as an implicit homogeneity test. To assess biomass of the seedlings we used low-cost and field-adopted morphometric and manual measurements. Top and side tree images, acquired with handheld RGB camera, were analysed using colour segmentation and pixel count based methods. Parameters, such as crown size, shape, area and pigment content, number of leaves, branch length and foliage density, allowed the seedlings to be classified into three different vitality groups. Presented multimodal approach was capable to differentiate and characterize distinct best, suboptimal or critical states of microbiome-host system, both on microbial and plant side. Our results show that more stable fungal co-occurrence patterns should be attributed to the plant set of the best growth. In contrast, more chaotic patterns can be considered non-optimal for plant-mycobiome cooperation.

Why it matches plant phenotyping methods植物の健康・活力状態を推定するマルチモーダル手法の一部として、RGB画像の色分割・画素計数から樹冠形状、葉数、枝長、葉密度などの形質を抽出しており、フェノタイピング手法の適用が実質的に含まれる。

abstractWe constructed a computational methodology to assess health of plant-microbiome system through microbiome structure modelling combined with plant remote sensing.

Enhancing crop growth forecasting by incorporating estimated uncertainties for time-series hyperspectral data and crop model GECROS simulations into Ensemble Kalman Filter →

Crop status forecasting by crop model simulations can benefit from assimilating remote sensing observations. When conducting data assimilation (DA) using a common procedure – the Ensemble Kalman Filter (EnKF), arbitrary inflation factors are normally adopted to account for unspecified uncertainties, so as to alleviate filter divergence. Here, we developed a more effective Bayesian methodology, in which the uncertainties were systematically quantified by combining multiple methods in one framework. Its applicability and performance in the EnKF were tested using the crop model GECROS (Genotype-by-Environment interaction on CROp growth Simulator) and the data collected from two years of field experiments for rice. Aboveground biomass (Wₐbₒᵥₑ), grain weight (Wgᵣₐᵢₙₛ), aboveground nitrogen (N) content (Nₐbₒᵥₑ), grain N content (Ngᵣₐᵢₙₛ) and leaf traits like leaf dry weight, leaf N content and leaf area index were measured in the experiments. Using only the observations from the first year, the uncertain parameters in GECROS were calibrated by a Markov Chain Monte Carlo approach, while the parameters in the uncertainty model that describes the errors of crop model simulations were estimated simultaneously. The calibrated model parameters performed well in the validation year, except for the simulated leaf traits (Normalized Root Mean Squared Error (NRMSE) > 0.38). Remotely sensed leaf traits predicted by a Gaussian Process Regression (GPR) model were more accurate (NRMSE < 0.32), with uncertainties of the remote sensing observations estimated from the GPR model itself. Assimilating simulated and predicted leaf traits with their estimated uncertainties into EnKF prevented filter divergence, and the forecast accuracy of crop model improved in the validation year. Compared with simulation without assimilating in-season remote sensing observations, the assimilation procedure led the NRMSE to decrease from 0.37 to 0.20 for whole-season Wₐbₒᵥₑ and Nₐbₒᵥₑ and from 0.39 to 0.20 for the end-season Wgᵣₐᵢₙₛ and Ngᵣₐᵢₙₛ. The updated crop traits of our method also agreed better with the measurements than those of common EnKF with arbitrarily assumed uncertainties and with adjusted inflation factors. The developed method contributes to systematic uncertainty analysis in DA and accurate forecasting of crop growth and yield for smart farming.

Why it matches plant phenotyping methodsリモートセンシングから葉形質を推定し、その不確実性を定量化してデータ同化する計算手法が研究の中心であり、植物形質推定・予測ワークフローとして評価されている。

abstractRemotely sensed leaf traits predicted by a Gaussian Process Regression (GPR) model were more accurate (NRMSE < 0.32), with uncertainties of the remote sensing observations estimated from the GPR model itself.

Optimized substrate selection for enhanced orchid growth based on high-throughput lysimetric arrays. →

Orchids are highly valued ornamental plants whose growth conditions directly impact the economic returns of the horticultural industry. The substrate, acting both as a physical support and a nutrient reservoir, is critical for orchid development. Therefore, the careful selection of an appropriate growth substrate is of paramount importance. However, existing research on the relationship between orchid growth and substrate properties relies mainly on manual measurements of physiological indicators, with limited application of high-throughput phenotyping (HTP) platforms. In this study, we evaluated three distinct substrate types, peat soil mixed with perlite, pine bark, and river sand, which were applied to two orchid species, Cymbidium goeringii and Cymbidium faberi . Using the high-throughput Plantarray lysimetric system, we continuously recorded environmental parameters (photosynthetically active radiation, humidity, and temperature) as well as key growth metrics (biomass accumulation, canopy conductance, and transpiration rate). This platform enabled precise and rapid quantification of orchid growth indicators. The results show that the type of substrate significantly affects orchid growth. Under controlled conditions, mixed substrates that provide balanced nutrition and excellent drainage enhanced orchid growth compared to other substrates. Additionally, when the data obtained from the HTP platform were compared with those from traditional manual measurements, the automated system showed higher reliability and accuracy. This study not only provides practical guidance for selecting cultivation substrates for orchids, but also establishes a robust scientific framework for integrating advanced phenotyping technologies into orchid cultivation practices.

Why it matches plant phenotyping methodsPlantarray高スループット表現型計測システムによる生長・生理形質の連続測定と、手動測定との信頼性・精度比較が研究の中心であり、基質効果の単なる生物実験にとどまらない。

abstractUsing the high-throughput Plantarray lysimetric system, we continuously recorded environmental parameters (photosynthetically active radiation, humidity, and temperature) as well as key growth metrics (biomass accumulation, canopy conductance, and transpiration rate).

Accelerating Cassava Genetic Improvement through NDVI-Based High-Throughput Phenotyping →

Abstract Cassava ( Manihot esculenta Crantz) is an important food security crop in sub-Saharan Africa and other tropical regions, but its genetic improvement is hindered by long breeding cycles and labour-intensive phenotyping procedures. This study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with affordable handheld sensor (Trimble GreenSeeker). A diverse panel of 453 cassava accessions was evaluated across two contrasting agroecological zones in Nigeria; Mokwa (Southern Guinea Savannah) and Onne (Humid Forest) during the 2021/2022 planting season. NDVI data collected at 3, 6, and 9 months after planting (MAP) were integrated with ground truth phenotypic measurements of 26 agronomic traits.Genetic parameters including broad-sense heritability and genotype-by-environment interactions were estimated. Results showed moderate to high heritability for important traits such as fresh root yield (FYLD), dry matter content (DM), and harvest index (HI). NDVI data, especially at 6 months after planting, demonstrated strong predictive power (R² up to 0.9) for yield components, with prediction accuracy varying across locations. Significant negative correlations between lodging (LODG) and yield traits highlighted the influence of plant architecture on productivity in cassava. These findings affirm the applicability of handheld NDVI sensors as cost-effective tools for enhanced phenotyping and selection in cassava breeding programs for rapid genetic gains and varietal development under diverse field conditions.

Why it matches plant phenotyping methodsキャッサバの収量・草型形質を推定するNDVIベースの迅速な表現型取得プロトコルを開発し、ハンドヘルドセンサーの予測性能を評価しているため、方法が中心的である。

abstractThis study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with affordable handheld sensor (Trimble GreenSeeker).

Shifting seasons: Long-term crop dynamics across agroclimatic regions of Czechia →

This study analyzes the evolution of phenological (start-of-season, end-of-season, length-of-season, day of maximum-of-season) and productivity (small and large seasonal integrals) parameters for six major crop types in Czechia (winter cereals, spring cereals, winter rapeseed, fodder crops, sugar beetroot, and corn), using a 35-year Landsat time series (1986–2020). The leaf area index (LAI) was retrieved using an artificial neural network regression model trained on PROSAIL radiative transfer simulations and validated with extensive in situ measurements collected in 2017 and 2018 in the lowlands of Central Bohemia. The supervised classification of Landsat quarterly composites enabled the identification of crop spatial patterns for each growing season. Phenological and productivity indicators were then derived from LAI time series aggregated at the level of ten agro-climatic regions using the threshold approach. Changes in phenological and productivity parameters over the examined period were assessed through the linear least squares regression analysis and the significance of trends was tested. Results revealed significant negative trends in the end-ofseason and day of maximum-of-season for winter and spring cereals, winter rapeseed (up to –0.7 days/year), and fodder crops (up to –1.6 days/year), indicating an earlier maturation and harvest. Significant differences in trends in phenological and productivity parameters were observed between agro-climatic regions in more than 40% of cases, and the response was observed to be highly crop-specific. While the shift in harvest dates and the shortening of the season for corn and fodder crops were more pronounced in warmer regions, the shift in winter rapeseed phenology occurred more rapidly in colder regions. The findings underscore the relevance of crop type and regional climate in shaping phenological responses, offering a basis for future research and planning of agricultural adaptation strategies.

Why it matches plant phenotyping methodsLandsatからLAIを推定し、作物のフェノロジー・生産性形質を抽出するリモートセンシング手法を、PROSAIL/ANNモデルと現地測定で検証しており、形質取得ワークフローが主要な役割を担う。

abstractThe leaf area index (LAI) was retrieved using an artificial neural network regression model trained on PROSAIL radiative transfer simulations and validated with extensive in situ measurements collected in 2017 and 2018 in the lowlands of Central Bohemia.

A multiscale remote sensing method to measure aboveground woody biomass in savanna ecosystems →

Background Woody aboveground biomass (AGB) stores and releases carbon in savannas, with fire as a key driver. Aims Savanna fire management (SFM) programs reduce emissions from AGB burning but do not incorporate live tree carbon sequestration. Assessing the impact of SFM on woody AGB carbon sequestration requires precise measurement and modelling. Methods We developed a multiscale remote sensing method for woody AGB estimation and applied it across ~105,000 ha of tropical savanna. A novel metric (shade volume) bridged the gap between terrestrial lidar-derived woody AGB and a convolutional neural network (CNN) model trained on airborne lidar and satellite imagery. Using the method, we estimated savanna woody AGB and quantified AGB prediction error. Key results CNN-predicted shade volume had 5.5% mean absolute error and −2.1% bias. Validation against independent 1 ha woody AGB measurements (n = 7) showed 7.9% mean error. In 40.1% of the study region, woody AGB predictions exceeded maximum potential biomass estimated by Australia’s national carbon accounting model. Conclusions This methodology improves carbon estimation accuracy over large areas, enabling fine-scale monitoring of woody AGB under varied SFM strategies. Implications Enhancing SFM carbon credit integrity requires direct measurement and transparency in woody AGB quantification, both achievable with this method.

Why it matches plant phenotyping methodsリモートセンシング、LiDAR、衛星画像、CNNを統合し、樹木の地上部バイオマスという明示的な植物形質を推定・検証する方法が研究の中心である。

abstractWe developed a multiscale remote sensing method for woody AGB estimation and applied it across ~105,000 ha of tropical savanna.

Advancing Sustainable Forest Management in Darkhan-Uul province, Mongolia using the Spectral Forest Index (SFI) →

Abstract. Forest ecosystems in semi-arid boreal regions, such as those in Darkhan-Uul province, Mongolia, serve as critical reservoirs of biodiversity and carbon while facing escalating anthropogenic and climatic pressures. Despite covering 22.4% (~733 km2) of the province, these birch- and larch-dominated forests exhibit declining resilience due to unsustainable land-use practices, illegal logging, and climate-induced disturbances, necessitating advanced monitoring frameworks for sustainable forest management (SFM). This study introduces the Spectral Forest Index (SFI), a novel composite metric derived from Sentinel-2 multispectral data within a Google Earth Engine (GEE) platform, to quantify spatiotemporal variations in forest health, productivity, and species composition. By integrating normalized difference and ratio-based indices (e.g., NDVI, RVI), the SFI synthesizes canopy structural attributes, photosynthetic activity, and biomass dynamics across monthly intervals (May–October 2020–2024), enable monitoring of forest cover, health, and species composition, with quality control measures ensuring data reliability. Results reveal pronounced spatial heterogeneity in forest degradation, with SFI depressions strongly correlated with overgrazing and anthropogenic land conversion, while regenerative trajectories align with targeted reforestation initiatives. The SFI’s sensitivity to ecological stressors (e.g., drought, pest infestations) underscores its utility as a scalable, policy-relevant tool for monitoring carbon sequestration potential and guiding adaptive management. This research advances remote sensing applications in SFM, offering a transferable framework for reconciling ecological preservation with socio-economic demands in vulnerable boreal ecosystems.

Why it matches plant phenotyping methodsSentinel-2データとGoogle Earth Engineを用いて、森林の健康状態、生産性、種組成、バイオマス動態を定量化する新規SFI指標を開発しており、植物キャノピー状態の取得・抽出手法が研究の中心である。

abstractThis study introduces the Spectral Forest Index (SFI), a novel composite metric derived from Sentinel-2 multispectral data within a Google Earth Engine (GEE) platform, to quantify spatiotemporal variations in forest health, productivity, and species composition.

Optimizing nitrogen topdressing for winter wheat by coupling remote sensing data with the DSSAT model. →

Introduction Excessive fertilization not only causes environmental pollution and degrades water and soil quality but also increases production costs and reduces agricultural sustainability. Methods Based on two consecutive years of field experiments, this study developed a two-step data assimilation strategy for nitrogen (N) topdressing recommendations for winter wheat. First, a data assimilation system was established by minimising the discrepancy between aboveground dry biomass (AGB) estimated from remote sensing and that simulated by the crop growth model using a particle swarm optimization approach. Second, target yields under varying growth conditions were constructed using the DSSAT model and N economic return curves to enable optimised N fertilization recommendations. Results AGB monitoring model was developed, achieving satisfactory results in both the calibration and validation datasets, with determination coefficient (R²) (normalised root mean square error (nRMSE)) values of 0.94 (13.62%) and 0.82 (15.42%), respectively. Based on the data assimilation system, the data assimilation stability for AGB and yield are relatively high. The nRMSE values for AGB are 11.20% and 19.44% for the training and validation datasets, respectively. The nRMSE values for yield are 6.35% and 11.22% for the training and validation datasets, respectively. The data assimilation-based recommended fertilization shows a negative power-law relationship with AGB at the jointing stage (R² = 0.65). Under different yield levels, fertilization was reduced by 6.69%-34.08% compared with that under high yield levels. Conclusion This study balances yield and production costs by developing a data assimilation strategy for N fertilization recommendations, which can maintain high productivity and sustainability.

Why it matches plant phenotyping methodsリモートセンシングから小麦の地上部乾物量を推定するモデルを開発・検証し、データ同化に組み込んでいるため、施肥推薦が主目的でも植物形質取得法が実質的に中心的役割を持つ。

abstractFirst, a data assimilation system was established by minimising the discrepancy between aboveground dry biomass (AGB) estimated from remote sensing and that simulated by the crop growth model using a particle swarm optimization approach.

Optimising grassland Above-Ground biomass Estimation for managed grasslands: A Gaussian process regression approach for Sentinel-2 and Planet Scope in Northern Italy →

Abstract Context Accurate and regular estimation of above-ground biomass (AGB) in grassland ecosystems is essential for sustainable grazing management, feed planning, and carbon accounting. However, AGB mapping in heterogeneous grasslands remains challenging due to the spatial and temporal variability of vegetation and management practices. Aims This study explores the potential of Gaussian Process Regression (GPR) models combined with multispectral imagery from Sentinel-2 and PlanetScope to predict AGB across different grassland systems in Northern Italy. Methods Extensive field measurements (n = 954) were collected over 18 months across meadows, lowland pastures, and alpine grasslands, covering a range of altitudes, management regimes, and canopy structures. Spectral predictors from Sentinel-2 and PlanetScope were used to train independent GPR models and evaluate their predictive performance at both pixel and field scales. Key Results At the pixel level, GPR models achieved R 2 = 0.520 (Sentinel-2) and R 2 = 0.514 (PlanetScope) with mean absolute errors (MAE) of ~400 kg DM ha −1 , consistent with the high heterogeneity of grassland canopies. Aggregating predictions at the field scale markedly improved accuracy (R 2 = 0.972 and 0.968; MAE = 60–120 kg DM ha −1 , ≤10% relative error). These results are comparable to those of commercial pasture monitoring platforms. Conclusion The integration of high-resolution multispectral imagery and non-parametric GPR modeling allows robust AGB estimation in heterogeneous grasslands, reducing uncertainty through field-scale aggregation. Implications and Impacts This research provides a scalable and transferable framework for operational biomass monitoring, offering a practical tool for digital decision support systems (DSS) and a scientific basis for integration into carbon Measurement, Reporting, and Verification (MRV) protocols. The novelty of the study lies in demonstrating the combined use of Sentinel-2 and PlanetScope data within a unified GPR framework for multi-site grassland systems, validated through extensive field observations.

Why it matches plant phenotyping methods衛星マルチスペクトル画像とGPRを統合して草地の地上部バイオマスという植物群落形質を推定し、広範な実測データで画素・圃場スケールの性能を検証しているため、方法開発・検証が中心である。

abstractThis study explores the potential of Gaussian Process Regression (GPR) models combined with multispectral imagery from Sentinel-2 and PlanetScope to predict AGB across different grassland systems in Northern Italy.

Estimating rice yield-related traits using machine learning models integrating hyperspectral and texture features. →

Background Rapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics. Increasing the accuracy of estimation models for rice yield-related trait indicators (leaf nitrogen concentration, LNC; leaf area index, LAI; aboveground biomass, AGB; and grain yield, GY) through a strategy of "spectral data + texture data + dimensionality reduction + machine learning" is highly important. Methods Between 2022 and 2023, hyperspectral canopy images, the LNC, LAI, AGB, and GY were collected synchronously. Then, dimensionality reduction was performed on the preprocessed spectral data using the Pearson correlation coefficient method, the successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) to select sensitive wavelengths. Estimation models were constructed using artificial neural networks (ANNs), support vector machine regression, one-dimensional convolutional neural networks, and long short-term memory networks. By extracting the texture features corresponding to sensitive wavelengths, high-precision estimation models were constructed using a "spectral data + texture data + dimensionality reduction + machine learning" method. Results SPA-ANN provided the best prediction for LNC (R 2 = 0.82, RMSE = 3.68 g/kg) and LAI (R 2 = 0.75, RMSE = 0.47), while CARS-ANN was optimal for AGB (R 2 = 0.90, RMSE = 79.05 g/m2) and GY (R 2 = 0.63, RMSE = 0.59 t/ha). Adding texture features increased R 2 by up to 9.9% and reduced RMSE by up to 27.2%. Conclusion The optimized method can significantly increase the accuracy of estimation models. The results provide a scientific basis and technical data for the precise diagnosis of rice yield-related traits.

Why it matches plant phenotyping methods水稲の収量関連形質を、ハイパースペクトル画像・テクスチャ特徴・次元削減・機械学習で非破壊推定する手法が研究の中心であり、植物フェノタイピング手法として明確に該当する。

abstractRapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics.

Non-destructive Biomass and SPAD Estimation Based on 3D Photogrammetry for Faba Bean →

Faba bean (Vicia faba L.) is a valuable legume crop with high protein content and the ability to fix atmospheric nitrogen through symbiotic bacteria in its root nodules, contributing significantly to both human nutrition and agricultural sustainability. Chlorophyll concentration in leaves serves as a reliable indicator of nitrogen status and photosynthetic capacity, while biomass production reflects overall plant growth and resource use efficiency. This study aims to develop a nondestructive and accurate method for simultaneously estimating chlorophyll content meter (SPAD) and above-ground biomass in faba bean using three-dimensional (3D) photogrammetric imaging combined with deep learning techniques. Point cloud data were obtained from hand-held camera scans of five faba bean genotypes and processed using the PointNet neural network architecture. Results showed that SPAD estimation achieved high accuracy (7.52% relative error) based solely on 3D structural features, while biomass prediction benefited from the integration of real and synthetic datasets, reducing relative error significantly from 24.15 % to 18.04 %. The study highlights the potential of 3D imaging and point cloud-based photogrammetry as effective tools for plant phenotyping, offering a scalable and non-invasive approach for monitoring physiological traits and genotype performance in faba bean.

Why it matches plant phenotyping methods3DフォトグラメトリとPointNetを用いて、ソラマメのSPADと地上部バイオマスを非破壊推定する手法を開発・評価しており、表現型取得・推定が研究の中心である。

abstractThis study aims to develop a nondestructive and accurate method for simultaneously estimating chlorophyll content meter (SPAD) and above-ground biomass in faba bean using three-dimensional (3D) photogrammetric imaging combined with deep learning techniques.

Leveraging Multispectral and 3D Phenotyping to Determine Morpho-Physiological Changes in Peppers Under Increasing Drought Stress Levels →

The expected population rise will require a maximum exploitation of agricultural lands with a consequent increase in the demand for freshwater for irrigation uses. Future trends predict increasing periods of drought stress, which may impact on crop performance and limit the future production. Pepper is one of the most economically important crops and globally consumed vegetables. This crop is highly demanding in terms of water supply, and so far, developing tolerant cultivars is one of the main targets for breeding. The aim of this study is to accurately determine how pepper plants react to water stress at the vegetative stage in order to select genotypes that better cope with drought. We implemented the PhenoHort Plant Eye phenotyping platform to precisely assess changes in plant architecture and morpho-physiological parameters on 25 cultivated pepper genotypes (Capsicum annuum) under drought stress conditions. Three different irrigation supply levels were considered, including the control, intense, and severe water stress, by irrigating every 24, 72, and 96 h, respectively. Daily monitoring of 20 traits allowed ~190,000 multispectral and tridimensional data points through scans over 6 weeks of cultivation, thus shedding light on changes in plant architecture and vegetation indices’ values during stress. The dissection of genotype (G) and treatment (T) interactions revealed that digital biomass and plant height traits were strongly affected by the T factor (more than 50% of total variance), whereas color and multispectral parameters were under greater genotypic control, accounting for 58.27% and 64.97% of the total variance for HUE and NPCI, respectively. The comparison of each accession with respect to the control and the application of multivariate models allowed us to select four drought-tolerant lines (G1, G2, G22, and G25) able to reduce the effects of drought on the morphological parameters and architecture of the plant with positive effects on vegetative indices. This work represents the first attempt to dissect the response of pepper under drought stress at the vegetative stage using a high-throughput and non-invasive phenotyping system, offering new insights for selecting resilient genotypes.

Why it matches plant phenotyping methods植物の乾燥応答を評価するため、マルチスペクトル・3D高スループット表現型解析プラットフォームを中心的に実装し、植物形態・生理形質を長期的かつ非侵襲的に取得しているため。

abstractWe implemented the PhenoHort Plant Eye phenotyping platform to precisely assess changes in plant architecture and morpho-physiological parameters on 25 cultivated pepper genotypes (Capsicum annuum) under drought stress conditions.

Integrating Vegetation Indices and Texture Features from UAV multispectral image for Non-destructive Peanut Aboveground Biomass Estimation →

Abstract. High-throughput phenotyping monitoring has become increasingly important in modern agriculture, as it can collect plant images to extract and analyse phenotype data related to growth and yield, thereby reducing crop monitoring costs. Aboveground biomass (AGB) is a key indicator for evaluating plant health, growth, and productivity, and reflects the impact of environmental factors (such as water, soil nutrients, and temperature) on plants. However, traditional methods for measuring AGB are often labor-intensive, costly, and limited in spatial coverage. Unmanned aerial vehicles (UAVs)-based remote sensing offer new solutions, enabling large-scale, high-resolution data collection in agricultural fields. Therefore, this study evaluates the use of Vegetation indices (VIs) and Texture features (TFs), as well as their combinations, derived from UAV multispectral imagery to estimate peanut AGB across different growth stages. Specifically, nine VIs and eight TFs with different parameter settings were first derived from RGB and four single-band UAV images. Based on random forest (RF) regression, the study explored the impact of different parameter combinations on the performance of AGB models and analysed the potential of combining VIs and TFs to improve AGB estimation. The results show that TFs effectively complement VIs, significantly enhancing peanut AGB estimation performance. The optimal window size was 7×7, with a direction of 90° and a grey level of 16. The combined VIs and TFs yield a regression with R² and RMSE of 0.929 and 0.032, respectively. These findings suggest that the strategy of extracting image textures and combining features significantly improves the accuracy of AGB estimation, providing a more precise method for monitoring AGB.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数・テクスチャ特徴を抽出し、RFでピーナッツ地上部バイオマスを推定する手法の評価が研究の中心であり、植物形質推定への技術的貢献が明確。

abstractthis study evaluates the use of Vegetation indices (VIs) and Texture features (TFs), as well as their combinations, derived from UAV multispectral imagery to estimate peanut AGB across different growth stages.

Research on the estimation method of crop net primary productivity based on improved CASA model. →

Net Primary Productivity (NPP) is a vital indicator for evaluating the carbon source and sink capacities of ecosystems, significantly influencing assessments of agricultural productivity and carbon cycle studies. Accurately estimating NPP in the agricultural sector, however, remains challenging. This research addresses the challenge by refining the estimation of the Fraction of Photosynthetically Active Radiation (FPAR) within the CASA model, introducing a novel methodology that significantly improves the accuracy of NPP estimation and, when applied to remote sensing imagery covering a broad region, demonstrates strong potential for large-scale crop NPP monitoring. We employed high-resolution Sentinel-2 satellite imagery and the Recursive Feature Elimination algorithm to extract FPAR-related features from 15 vegetation indices. The FPAR was subsequently estimated using a Convolutional Neural Network, leading to a dramatic decrease in the Root Mean Square Error (RMSE) from 0.2040 to 0.0020. The prediction errors for the improved model ranged from 0.0001 to 0.0092, with a mean absolute error (MAE) below 0.01. These values reflect the distribution of absolute residuals and indicate a substantial enhancement in accuracy over traditional methods. This improved FPAR estimation method was subsequently integrated into the CASA model. Compared to field-measured NPP data, the optimized model reduced the Mean Absolute Percentage Error (MAPE) from 28.92% to 20.31%. The MAPE values across the test samples ranged between 15% and 25%, indicating a significant improvement in model reliability. The optimized CASA model performs well in estimating net primary productivity (NPP) of crops, providing strong support for agricultural decision-making and future research on large-scale productivity and carbon cycling.

Why it matches plant phenotyping methods作物NPPおよびFPARを衛星画像とCNNで推定する手法の開発・精度検証が研究の中心であり、作物の生産性という植物状態を直接評価している。

abstractThis research addresses the challenge by refining the estimation of the Fraction of Photosynthetically Active Radiation (FPAR) within the CASA model, introducing a novel methodology that significantly improves the accuracy of NPP estimation

Near-Infrared Spectroscopy Prediction of Dry Matter and Starch Content in Cassava Using Optimized Calibration Models. →

Dry matter content (DMC) and starch content (StC) are key quality traits in cassava breeding, yet traditional phenotyping methods are time-consuming and limit scalability. This study aimed to develop and compare predictive models for DMC and StC using near-infrared (NIR) spectroscopy, evaluating two devices-a benchtop spectrometer (Büchi NIRFlex N-500; 1000-2500 nm) and a portable device (QualitySpec Trek; 350-2500 nm)-and assessing the influence of sample type (fresh vs. processed). A total of 3,391 cassava clones from the Embrapa breeding program were analyzed from 2018 to 2023. Reference values were obtained via gravimetric analysis (DMCg), oven drying (DMCo), and manual StC extraction. Spectral data were used to train and validate models using Partial Least Squares (PLS), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGB). PLS consistently delivered the highest predictive accuracy across traits and devices. KNN slightly outperformed PLS for DMCg using the benchtop device, while XGB was comparable to PLS in select scenarios (e.g., StC with the benchtop: 0.88 vs. 0.89; DMCo with the portable: 0.92 vs. 0.95). Processed samples yielded higher model accuracy than fresh ones. The portable NIR device showed better performance with processed samples and even surpassed the benchtop for DMCg and StC in external validation (0.74 and 0.76 vs. 0.71 and 0.72, respectively). Overall, processed sample preparation significantly improved model performance, and the portable spectrometer proved to be a practical, accurate, and scalable alternative for high-throughput phenotyping in cassava breeding.

Why it matches plant phenotyping methodsキャッサバ育種における乾物・デンプン含量の高スループット表現型取得を目的に、NIR機器と予測モデルを開発・比較・外部検証しており、測定法が研究の中心である。

abstractThis study aimed to develop and compare predictive models for DMC and StC using near-infrared (NIR) spectroscopy

Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field →

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environments to the field, and a lack of understanding of genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars in field experiments conducted across two contrasting environments. A diverse subset of 20 genotypes was subjected to ground-based root and shoot phenotyping at key growth stages, and this dataset was used in combination with unmanned aerial vehicle (UAV)-captured vegetation indices (VIs) to train machine learning models to predict root distribution and above-ground biomass for the untested panel comprising 544 genotypes across the two seasons. Unlike previous root studies that have focused on above-ground traits or indirect proxies, this approach predicts root traits in the field using machine learning. Haplotype-based mapping using predicted root and shoot traits in the broader panel revealed key genomic regions. These include novel regions, previously reported root quantitative trait loci, and EGT2-a recently cloned gene that regulates root gravitropism in barley. This scalable phenotyping approach offers opportunities to advance root research across crops and support the development of future varieties adapted to changing climates.

Why it matches plant phenotyping methodsUAV画像由来の植生指数と機械学習を組み合わせ、圃場で根系形質と地上部バイオマスを推定するスケーラブルな表現型解析手法が研究の中心である。

titlecombining UAV phenotyping and machine learning to predict barley root traits in the field

Adjusted CBA-Wheat model for predicting aboveground biomass in winter wheat from hyperspectral data →

Crop aboveground biomass (AGB) is a key indicator of photosynthesis and carbon cycle dynamics in agricultural ecosystems. The availability of accurate, real-time AGB data enables efficient resource management and precision farming. The crop biomass algorithm for wheat (CBA-Wheat) estimates winter wheat AGB using vegetation index (VI) and Zadoks stage (ZS), but acquiring ZS data through field surveys is challenging for large-scale applications. This study aimed to optimize the CBA-Wheat model by incorporating the concept of the relative day of the year (RDOY) as a replacement for ZS and combining it with VI to enhance the performance of the wheat growth model. We proposed the concept of RDOY to replace the traditional ZS, thereby optimizing the CBA-Wheat model. The study used data from Xiaotangshan, Beijing, from 2013 to 2020 for model development. The validation dataset included 2021 Xiaotangshan data, 2010 suburban Beijing data, and 2012 Yucheng, Shandong data for testing the model’s temporal and spatial transferability. Additionally, we compared the performance of the CBA-WheatRDOY model with machine learning models, including Partial Least Squares Regression (PLSR) and Random Forest (RF). We found that the modified CBA-WheatRDOY model, utilizing the modified simple ratio vegetation index (MSR) as an input parameter, achieved the highest AGB estimation accuracy, with a coefficient of determination (R²) of 0.82 and a root mean square error (RMSE) of 1.71 t/ha. This result surpassed the performance of partial least squares regression (R² = 0.78, RMSE = 1.48 t/ha) and random forest (R² = 0.73, RMSE = 2.03 t/ha) models when RDOY was introduced. Our findings highlight the effectiveness of introducing RDOY in improving the accuracy of winter wheat biomass estimation within the CBA-Wheat model. Moreover, RDOY is a superior alternative to traditional phenological observations and can potentially enhance the performance of conventional machine learning models. Compared with existing algorithms, the CBA-WheatRDOY model, grounded in RDOY, not only responds sensitively to various phenological stages but also exhibits improved inversion accuracy. This approach holds promising potential for enhancing the timeliness and spatial extrapolation of winter wheat AGB predictions, advancing precision agriculture and ecosystem management.

Why it matches plant phenotyping methods冬小麦の地上部バイオマスという植物形質を、ハイパースペクトル由来の植生指数と生育時期情報から推定するモデルを開発・最適化し、時空間移 transferability と他の機械学習モデルとの比較検証を行っているため、表現型取得・推定手法が中心である。

abstractThis study aimed to optimize the CBA-Wheat model by incorporating the concept of the relative day of the year (RDOY) as a replacement for ZS and combining it with VI to enhance the performance of the wheat growth model.

Comparison of digital aerial photogrammetry, lidar, and Sentinel-2 for evaluating forest fire effects →

Wildfires have the potential to profoundly alter forest structure, which can leave land managers with severe information gaps when making post-fire forest management decisions. We assess point clouds derived from aerial lidar (16.8 points/m²) and digital aerial photogrammetry (DAP, 13.9 points/m²) for the purposes of generating post-fire forest attribute maps using 82 permanent field plots. Low-cost aerial lidar and DAP data can enable predictive mapping after fires occur, and an assessment of the utility of DAP in this context is warranted given its lower cost. We evaluated the performance of models constructed from post-fire lidar and DAP data collected over a 158,000 ha area formerly dominated by Douglas-fir in Oregon, United States for four forest attributes: live aboveground biomass, standing dead aboveground biomass, live stem density, and relative basal area change. Both data sources were augmented with Sentinel-2 pre- and post-fire images. DAP models had relative RMSEs that were 10–47 % larger than lidar models. Augmenting the models with Sentinel-2 narrowed this error differential to 3–16 %. We found that the difference between lidar and DAP heights is explained by lidar cover and canopy intensity, suggesting that consumption of canopy fuels can adversely affect the quality of DAP heights. Our results indicate that DAP models augmented with Sentinel-2 present an attractive prospect for fire footprints lacking lidar data owing to DAP’s broader spatial and temporal availability; however, uncertainties may exist for some response variables in areas with high levels of canopy fuel consumption.

Why it matches plant phenotyping methods航空LiDAR・デジタル航空写真測量・Sentinel-2を比較し、森林プロットのバイオマス、幹密度、基底面積変化などの植物属性推定性能を検証しており、測定手法が中心である。

abstractWe assess point clouds derived from aerial lidar (16.8 points/m²) and digital aerial photogrammetry (DAP, 13.9 points/m²) for the purposes of generating post-fire forest attribute maps using 82 permanent field plots.

Effects of phenological uniformity on prediction accuracy of wheat yield and biomass →

Accurate prediction of wheat yield and biomass is essential for breeding new cultivars and optimizing field management. The accuracy of yield and biomass predictions can be affected by the phenological phase of data collection. However, phenological transitions are gradual, and wheat fields rarely consist of a single phenological stage. The influence of phenological uniformity (PU) on prediction accuracy has been largely overlooked, particularly in multi-cultivar study areas. This study aimed to quantitatively evaluate PU, to classify wheat phenological stages using hyperspectral data collected by an unmanned aerial vehicle (UAV), to identify the optimal growth stage for yield and biomass prediction, and to assess the impact of PU on prediction accuracy. A two-year field experiment was conducted using 210 wheat cultivars with diverse phenological stages, and time-series hyperspectral images were collected using an UAV. The study first defined and quantitatively evaluated PU. Subsequently, the classification accuracies of five models were compared to identify the most effective approach for phenological stage classification. Hyperspectral data collected at four key growth stages were then used to determine the optimal stage for yield and biomass prediction. Finally, datasets with varying PU were constructed to predict yield and biomass, and the influence of PU on prediction accuracy was assessed. PU of wheat exhibited a fluctuating trend throughout the growth stages, with most values ranging between 0.5 and 0.8. Hyperspectral data enabled effective discrimination of key phenological stages, among which the end-to-end mixhop superpixel-based graph convolutional networks (EMS-GCN) model achieved the highest classification accuracy, with an overall accuracy of 86.2 %. The PLSR model achieved the most accurate predictions of both yield (R² = 0.692, RMSE = 1.091 t/ha, CV = 0.152) and biomass (R² = 0.827, RMSE = 1.873 t/ha, CV = 0.113) at the flowering stage. The results of yield and biomass prediction based on datasets with varying PU values indicated a positive correlation between PU and prediction accuracy. Accurate classification of key wheat phenological stages can be achieved by combining deep learning with hyperspectral data. The flowering stage is the optimal period for yield and biomass prediction. PU positively correlates with the prediction accuracy of yield and biomass. This study emphasizes the important role of PU in wheat yield and biomass prediction, and accurate monitoring of PU can provide theoretical guidance for data collection. This is of great significance to the development of precision agriculture and guiding field management.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像と深層学習によるコムギの生育ステージ分類、および収量・バイオマス推定を中心に、モデル比較と精度評価を行っており、表現型取得・推定手法が研究の中核である。

abstractto classify wheat phenological stages using hyperspectral data collected by an unmanned aerial vehicle (UAV)

Digital twin for lettuce growth in six climate zones (‘Climate 2050′) →

This study investigates lettuce growth under extreme environmental conditions by simulating the weather in six climate zones in a plant growth chamber, including Lleida, Adelaide, Paris, San Luis, Singapore, and Fairbanks. The experiment involved weekly exposure to a new city’s climate, simulating “non-terrestrial weather stress,” which is also motivated from the vantage point of space plant growth and its process-control limitations. These simulated conditions shed light on ‘Climate 2050’, when Earth will probably have harsher and more fluctuating conditions. For the period investigated, the real temperature changes could be reproduced well and in real-time in the growth chamber, the actual rain fall was mimicked, and the lighting period was adjusted to the real sunshine exposure in the respective city. The virtual move of the lettuce plant from between six climates with their own profile in temperature, lighting time, and water is assumed to create stress beyond the variability of a weather change within a single climate. Machine learning models, including linear regression, random forest regression, and boosted decision tree regression, were employed to predict weekly lettuce biomass and yield. This study successfully demonstrated the application of machine learning algorithms for predicting lettuce growth under the given range of six climate conditions. Among the tested models, random forest regression consistently delivered the most accurate and reliable biomass predictions, achieving an R² of nearly 99 % and MAPE of 6 % in all scenarios. By introducing tuned correction factors for conditions like drought stress, fertilisation, and mixed soil composition, the accuracy and flexibility of models are enhanced. This research highlights the value of integrating real-time data with machine learning through a digital twin framework, offering a promising direction for climate-resilient agriculture and space-based plant growth systems.

Why it matches plant phenotyping methodsデジタルツインと機械学習を中核に、レタスのバイオマスおよび収量を予測する再利用可能な計算ワークフローを構築・評価しており、植物形質推定法が中心である。

abstractMachine learning models, including linear regression, random forest regression, and boosted decision tree regression, were employed to predict weekly lettuce biomass and yield.

Deriving structural and biochemical crop traits from one UAV sensor: Investigating a multiband VNIR/SWIR imaging system for crop trait monitoring →

Abstract. Frame-based VNIR/SWIR multispectral sensors on UAVs offer promising capabilities for precision agriculture by enabling the easy simultaneous acquisition of spectral and structural crop information. This study provides an independent validation of a two-band VNIR/SWIR sensor system for monitoring winter wheat traits and compares the results with previous findings. The UAV flights were conducted on a single date (May 11, 2022), capturing image datasets at wavelengths of 910, 980, 1100, 1200, 1510, and 1650 nm. Structure from Motion (SfM) processing enabled crop height extraction from the same multispectral datasets. Ground-truth data included fresh and dry biomass, moisture, nitrogen concentration, and nitrogen uptake from 36 samples across six varieties and three fertilization levels. Bivariate regression analyses revealed moderate performance for spectral vegetation indices (NRI: R2=0.52–0.61; GnyLi: R2=0.50–0.62), which was lower than that previously reported. Crop height showed a superior predictive capability (R2=0.63–0.75), demonstrating consistency across studies. Multivariate models combining vegetation indices with crop height significantly improved trait estimation (R2=0.72–0.84, nRMSE=0.12–0.15), confirming that integrated spectral-structural approaches provide robust performance even when individual predictors show limitations. While this single-date analysis limits conclusions about temporal stability throughout the growing season, it provides valuable validation of the capabilities of the sensor system. The ability to derive both structural and biochemical data from single-sensor imagery is the key advantage of this camera system. Future research should expand to multi-temporal analyses across complete growing seasons and implement the recently developed 6-channel VNIR/SWIR system to address the current limitations. This study reinforces the fact that combining SWIR spectral features with structural parameters is essential for reliable estimation of crop traits.