Plant phenotyping methods.

UAV-Based Classification of Crop Phenological Stages Using Deep Learning →

This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

Why it matches plant phenotyping methodsUAV画像と深層学習により作物の生育（フェノロジー）段階を自動推定する手法が研究の中心であり、植物状態の取得・分類に直接関わる。

abstractThis study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery.

UAV-Based Classification of Crop Phenological Stages Using Deep Learning →

This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

Why it matches plant phenotyping methodsUAV画像と深層学習により作物の生育・フェノロジー段階を自動推定する方法が研究の中心であり、植物状態の抽出性能も評価している。

abstractThis study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery.

Development of web-based YCPM-UAV interface for early yield prediction of canola crop using UAV multi-sensor data →

• Development of yellow color index (YCI) for yield estimation at flowering stage • Development of web-based interface (YCPM-UAV) for canola yield prediction using UAVs • Global application capability for UAVs datasets to predict canola yield using YCPM-UAV • Multi-sensor and multi-spectrum data fusion to find most suited indices for canola • Multiple and stepwise regression analysis for selection of most influencing VIs Canola ( Brassica napus L.) is a globally significant oilseed crop, yet accurate yield estimation remains challenging due to the complex and unique nature of the crop, especially at the flowering stage. Traditional field-based yield estimation methods are labor-intensive, time-consuming, and destructive, necessitating innovative approaches for early and non-destructive yield prediction. The main objective of the study is to develop a novel web-based platform, YCPM-UAV (Yellow Color Prediction Model using Unmanned Aerial Vehicles), for early and accurate canola yield estimation using high-resolution multi-sensor datasets acquired through low-altitude UAVs (LA-UAVs). To achieve this objective, a comprehensive two-year field study (2022-2024) was conducted across ten farmers’ fields in different geographical locations. Multisensor data (RGB, multispectral, and thermal) were acquired using UAVs at seven growth stages. Several vegetation indices (VIs), yellow color-based indices, and a thermal index were calculated. Linear, multiple, and stepwise regression analyses were performed to evaluate relationships of remote sensing indices with ground-truth yield data collected from 1200 sampling points. Multiple and stepwise regression analyses indicated that the newly developed Yellow Color Index (YCI) exhibited the strongest correlation with actual canola yield at the flowering stage across both years (Year 1: R 2 = 0.84, RMSE = 39.30 g m⁻²; Year 2: R² = 0.88, RMSE = 31.57 g m⁻²). Based on proposed predictive modeling, the YCPM-UAV web interface was developed, featuring automated data processing and spatial analysis with a testing accuracy of 88%. The YCPM-UAV platform provides farmers, researchers, and policymakers with a timely, user-friendly, and actionable decision-support tool for canola yield estimation at the field scale, contributing to improved crop management and food security. Future studies should incorporate additional canola varieties, irrigated and non-irrigated fields, and deep learning algorithms to further improve model robustness.

Why it matches plant phenotyping methodsUAVマルチセンサー画像からカノーラ収量を推定する指標・回帰モデル・Webプラットフォームを開発し、複数年データで検証しており、植物形質取得・推定が中心である。

titleDevelopment of web-based YCPM-UAV interface for early yield prediction of canola crop using UAV multi-sensor data

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network. →

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (R p 2 =0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Why it matches plant phenotyping methods油糠菜葉の鉛含量を蛍光ハイパースペクトル画像とニューラルネットワークで非破壊推定する手法の開発・比較検証が研究の中心であり、植物の化学的ストレス状態を定量するため。

titleNon-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Development of auxin reporters in oilseed rape (Brassica napus) →

Auxin is a key phytohormone that regulates all aspects of plant growth, development, and environmental responses, making the precise analysis of its distribution and signaling essential for understanding plant adaptation and physiological processes. However, despite the agricultural importance of oilseed rape (Brassica napus), the lack of robust, species-specific molecular tools limits detailed studies of hormone signaling in this crop. Here, we developed and characterized reporter systems for the sensitive visualization and quantification of auxin distribution and signaling in B. napus. The DR5cc auxin signaling reporter and a novel synthetic auxin-responsive reporter, BIP3, assembled from promoter fragments of three oilseed rape IAA genes, were generated to drive GUS expression. In hairy roots, both reporters showed auxin-responsive expression in the root apical meristem that became broader after auxin treatment. In transgenic seedlings, flowers at anthesis, and 12-day-old embryos, DR5cc exhibited a more defined expression pattern than BIP3. To monitor real-time auxin dynamics under abiotic stress, DR5cc fluorescent reporters were employed in hairy roots. Mannitol and NaCl treatments induced a time-dependent increase in fluorescence, peaking at 6-12 h before returning to basal levels after 24 h. Furthermore, dual-reporter assays enabled simultaneous monitoring of auxin and cytokinin signaling, revealing distinct hormone-specific spatial responses in hairy roots. Finally, we established a quantitative DII (qDII) reporter system using degron domains from B. napus Aux/IAA proteins, providing a high-resolution quantitative readout of auxin depletion. Together, these reporter systems enable spatial, temporal, and quantitative analyses of auxin dynamics during development and stress adaptation in oilseed rape.

Why it matches plant phenotyping methodsナタネにおけるオーキシン分布・シグナルを可視化および定量するレポーター系を開発・評価しており、植物の生理状態を取得する方法が研究の中心である。

abstractHere, we developed and characterized reporter systems for the sensitive visualization and quantification of auxin distribution and signaling in B. napus.

Estimating Crop Nitrogen Uptake from UAV-Based Imagery Using Machine Learning Techniques →

Unmanned Aerial Vehicle (UAV)-based remote sensing using high-throughput spectral imaging has emerged as an effective non-destructive alternative for large-scale agricultural monitoring. This study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola. Field trials were conducted at irrigated and non-irrigated sites in southern and central Alberta, Canada, respectively, over three growing seasons (2023–2025). Coincident with ground-truth tissue sampling, aerial imagery was collected and processed to train and validate six machine learning models, using ~520 matchups per crop. All models successfully estimated nitrogen uptake across years and locations, although performance varied by sensor and data types. For canola, ANN produced the highest MSI-based accuracy (R2 = 0.83, RMSE = 0.5%), whereas HSI data improved prediction performance, with SVR achieving the best results (R2 = 0.90, RMSE = 0.40%). In wheat, ANN yielded the highest accuracy for both MSI and HSI data (R2 = 0.77, RMSE = 0.54% for MSI; R2 = 0.8, RMSE = 0.48% for HSI). These findings demonstrate that UAV-based spectral imaging combined with machine learning provides a reliable and scalable approach for non-destructive nitrogen uptake estimation. Although MSI sensors produced strong predictive performance, the enhanced spectral resolution of HSI data consistently improved estimation accuracy for both crops across varied growing conditions.

Why it matches plant phenotyping methodsUAVマルチスペクトル・ハイパースペクトル画像と機械学習により、作物の窒素吸収量という植物形質を推定し、複数モデル・センサーの性能を評価しているため、フェノタイピング手法が中心である。

abstractThis study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola.

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

Towards Accurate Crop Yield Prediction: Integrating Sentinel-2 Remote Sensing with AI-Based Modelling →

Abstract. Accurate monitoring of vegetation health and canopy structure is essential for optimizing agricultural productivity and managing natural resources. Remote sensing technologies, combined with artificial intelligence (AI) and advanced satellite data, have revolutionized the capacity to assess crop conditions at large scales with high temporal and spatial resolution. This study leverages Sentinel-2 multispectral imagery and a novel AI-driven model approach to estimate Leaf Area Index (LAI) across multiple fields for canola. By integrating spectral reflectance data with view and solar geometry parameters, the model effectively captures the complex interactions between canopy structure and environmental factors. The methodology employs a two-layer neural network calibrated with physically based normalization to translate Sentinel-2 spectral and angular inputs into accurate LAI estimates. Validation against observed field measurements demonstrates strong agreement, underscoring the model’s robustness and reliability. Spatial analysis reveals distinct LAI patterns among the crop types, highlighting differences in canopy density and growth dynamics. Temporal profiling further illustrates crop-specific development trends, with canola showing extended canopy expansion. The results confirm that the fusion of remote sensing data with AI modelling provides a powerful tool for precision agriculture, enabling detailed monitoring of crop growth and facilitating informed decision-making. This approach offers significant potential for enhancing yield prediction, resource management, and sustainable farming practices, ultimately supporting global food security efforts.

Why it matches plant phenotyping methodsSentinel-2画像とニューラルネットワークにより、作物のLAIという明示的な植物形質を推定し、実測値で検証する手法が研究の中心である。

abstractThis study leverages Sentinel-2 multispectral imagery and a novel AI-driven model approach to estimate Leaf Area Index (LAI) across multiple fields for canola.

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.

Larval antibiosis to cabbage stem flea beetle (Psylliodes chrysocephala) is absent within oilseed rape (Brassica napus). →

Background Insect pests present a global threat to crops, with plant resistance representing a key breeding goal. The cabbage stem flea beetle (Psylliodes chrysocephala; CSFB) is a key pest of oilseed rape (Brassica napus; OSR) in Europe; however, CSFB resistance is yet to be found in B. napus. To address this, we examine CSFB larval development over time, explore antibiosis across a genetically diverse B. napus panel, and test whether larvae can develop in model Brassicaceae species (Brassica rapa and Arabidopsis thaliana). Results CSFB larvae completed development from 4 weeks post-infestation, undergoing a 20-fold size increase, with larval recovery after 2 weeks allowing semi-high-throughput resistance phenotyping. Applying this method to 98 Brassicaceae genotypes (97 B. napus and 1 Sinapis alba), we found weak evidence for genotype effects on larval survival. However, phenotype validation with 'resistant' and 'susceptible' B. napus genotypes showed no differences in larval survival or adult emergence. Larval antibiosis was consistently observed in S. alba. Finally, we showed that model B. rapa and A. thaliana genotypes represent suitable hosts for CSFB, with larvae increasing eight to ten times in size after 2 weeks. Conclusion CSFB larval antibiosis appears absent in B. napus, possibly because of bottlenecks experienced during domestication. However, larval antibiosis is present in S. alba, and future work should study the basis of this resistance. Further, CSFB larval screening in Brassicaceae model species presents an opportunity to explore CSFB resistance genetics, informing breeding progress for insect resistance in B. napus. © 2026 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Why it matches plant phenotyping methods幼虫回収・発育評価による半ハイスループットな抵抗性表現型解析法を開発し、遺伝子型パネルへの適用と抵抗性・感受性系統による検証を行っており、表現型取得法が中心です。

abstractlarval recovery after 2 weeks allowing semi-high-throughput resistance phenotyping

From plot to field: A practical and robust model for rapeseed LAI inversion using a consumer-grade UAV RGB imaging platform →

PURPOSE: The leaf area index (LAI) is a crucial parameter for crop growth management. While UAV remote sensing has been utilized to estimate LAI at the plot scale, its application to complex farmland environments—characterized by heterogeneous backgrounds (e.g., soil, residue, and weeds)— has been less explored. METHOD: This study employed UAV-mounted hyperspectral and RGB sensors to gather data from both experimental plots and farmland environments. Data from diverse rapeseed cultivars and growth stages were used as the calibration dataset, while farmland-level data validated the models. The study compared three models: the PROSAIL model, an empirical model incorporating canopy spectral and morphological parameters without differentiating canopy cover types, and the proposed canopy morphological parameters (CMP) model. The CMP model estimated LAI using fractional vegetation cover (FVC) for sparse canopies and canopy height for closed canopies. RESULT: Despite challenges such as UAV image resolution and the limited availability of spatial data, the CMP model showed strong performance, with an R² of 0.779 and RMSE of 0.732. Although its R² was similar to that of the empirical spectral–morphological (ESM) model (R² = 0.780), the CMP approach achieved a notably lower RMSE (0.732 vs. 0.814). This improvement stems from its canopy-aware design, which adaptively uses fractional vegetation cover for sparse canopies and canopy height for closed canopies. Such differentiation enhances model stability and generalization in heterogeneous farmland scenes—conditions in which background interference and structural variability often degrade empirical models. In comparison, the PROSAIL model performed less accurately (R² = 0.618, RMSE = 1.094). CONCLUSION: These results highlight that the CMP model provides a robust and cost-effective solution for LAI estimation, supporting crop growth assessment and management in real farmland.

Why it matches plant phenotyping methodsUAV画像・センサーから rapeseed のLAIを推定するモデルを開発・比較し、異なる圃場条件で検証しているため、植物形質取得手法が研究の中心です。

abstractThe CMP model estimated LAI using fractional vegetation cover (FVC) for sparse canopies and canopy height for closed canopies.

Identification of acidic tolerance in rapeseed varieties based on hyperspectral imaging →

With increasingly severe soil acidification, it is essential to screen and identify acid-tolerant crop varieties to safeguard agricultural production. Integration of hyperspectral imaging with machine learning models has been extensively used in high-throughput crop phenotyping. Here, we established a multi-indicator evaluation system for quantification of acidic tolerance in rapeseed based on hyperspectral data collected from 65 rapeseed varieties under pH = 5.2 (acidic) and pH = 6.4 (normal) soil conditions. After denoising and smoothing, 34 existing vegetation indices and band combination indices were derived from which eight growth-sensitive indices were selected based on their correlations with the actual growth scores. Six key spectral bands exhibiting significant changes under acidic stress were identified, with the feature importance outputs from three machine learning classification models. Comprehensive sensitivity coefficients (SC) were derived by integrating growth-sensitive indices and key band data using principal component analysis weighted-sum (PCA-WS). Hierarchical clustering classified the 65 tested varieties into strongly-tolerant (four varieties), moderately-tolerant (26 varieties), and weakly-tolerant (35 varieties). Physiological validation based on yield and branch number showed that the strongly-tolerant varieties had 10.03 % and 17.15 % higher relative yields and 13.24 % and 11.14 % higher relative branch numbers than moderately and weakly-tolerant varieties, respectively. These results have established a hyperspectral evaluation system that can accurately evaluate the acidic tolerance of rapeseed, providing a reliable basis for screening acid-tolerant rapeseed varieties.

Why it matches plant phenotyping methodsラペシードの酸性耐性をハイパースペクトル画像から定量評価する評価システムを構築し、特徴量選択・機械学習・検証まで行っており、植物表現型取得・抽出手法が中心である。

abstractIntegration of hyperspectral imaging with machine learning models has been extensively used in high-throughput crop phenotyping.

TrSC2Y: A transfer-learning-based model from UAV hyper-spectra imagery for field-scale canola yield prediction by integrating DSSAT with PROSAIL →

Rapid and accurate acquisition of field crop yield is of great significance for agriculture management optimization, food security and crop productivity. By the non-destructive and high-throughput data acquisition, the unmanned aerial vehicle (UAV) remote sensing has become a key tool for crop growth monitoring. However, the scarcity of in-situ samples poses technical barriers and efficiency challenges to yield model training. This study has developed a new yield estimation framework that integrates process models, optical remote sensing, and transfer learning to improve the stability and accuracy of crop yield estimation under small sample conditions. The DSSAT was calibrated with hyperspectral UAV derived crop growth variables, to describe the spatial-temporal variation of small-scale field winter canola leaf nitrogen content during growing season. Firstly, a process-interpretative crop yield estimation framework, TrSC2Y, was pre-trained using the PROSAIL radiative transfer model and the DSSAT crop growth model. Secondly, TrSC2Y was fine-tuned using field observations and UAV hyper-spectra images from three-years canola experiment. Finally, the actual performance and application potential of fine-tuned TrSC2Y in canola yield estimation were evaluated with machine learning as a benchmark test. The results show that: (1) Pre-trained by the crop spectra dataset (from PROSAIL) and yield dataset (from DSSAT), TrSC2Y can accurately extract crop phenotype parameters from theoretical canopy spectra. The joint use of phenotype parameters from multiple growth stages can achieve the best yield estimation (R²= 0.98;RMSE= 33.07 kg/ha;MAE= 1.26 %);(2) Fine-tuned TrSC2Y can be transferred to the field winter canola yield estimation task and shows stable performance (R²= 0.86;RMSE=224.42 kg/ha;MAE=6.5 %). Compared with the machine learning benchmark test, the demand of modeling samples for TrSC2Y is reduced by 50 %; (3) TrSC2Y supports the visualization of field-scale winter canola yield and captures the spatial variability of winter canola yield caused by irrigation-fertilizer treatments.The above results provide a lightweight, cost-effective, and innovative method for field crop yield estimation, promoting the development of precision agriculture management and intelligent applications.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像と作物モデル・転移学習を統合し、収量などの植物表現型を抽出・推定する枠組みを開発・評価しており、取得・推定手法が研究の中心である。

abstractThis study has developed a new yield estimation framework that integrates process models, optical remote sensing, and transfer learning to improve the stability and accuracy of crop yield estimation under small sample conditions.

In-season variable rate nitrogen topdressing recommendation and validation for winter oilseed rape cultivation supported by UAV multispectral imagery →

Efficient nitrogen (N) management is critical for maximising yield while minimising environmental impacts in oilseed rape production. While Unmanned Aerial Vehicle (UAV)-based monitoring of N status has advanced rapidly, translating estimated N status into actionable fertilization strategies remains limited. This study proposes a multistage N topdressing recommendation framework for winter oilseed rape that integrates UAV multispectral data with prior agronomic knowledge using machine learning algorithms. The framework accurately estimated the nitrogen nutrition index (NNI), with the random forest model performing best (r² =0.73 and RMSE = 0.11) for the validation dataset. By integrating estimated NNI, critical NNI thresholds, and optimal N uptake levels, dynamic, stage-specific N fertilizer topdressing rates were computed. A field experiment with varying basal N fertilizer rates was conducted to validate the framework, with UAV-guided topdressing performed after each monitoring event. Compared with the local conventional fertilization practice, the UAV-guided treatment with 90 kg N/ha basal fertilizer rates significantly improved yield by 20.2 % and N use efficiency by 80.1 %. This study bridges the gap between remote sensing-based diagnostics and in-field N fertilization, offering a feasible data-driven approach for real-time N management to enhance productivity and sustainability in oilseed rape cultivation.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物の窒素栄養指数（NNI）を推定する手法を機械学習で構築・検証し、その推定に基づく施肥フレームワークを評価しており、植物状態の取得・抽出が中心的です。

abstractThis study proposes a multistage N topdressing recommendation framework for winter oilseed rape that integrates UAV multispectral data with prior agronomic knowledge using machine learning algorithms.

Deep Learning Segmentation Models for UAV-Based Detection of Crop Damage in Rapeseed Using RGB Imagery →

The objective of this study was to evaluate the accuracy of detecting crop damage caused by wild boar in rapeseed fields using UAV (unmanned aerial vehicle)-derived RGB (red, green and blue) imagery and deep learning segmentation models. The experiments were conducted on rapeseed crops at full maturity shortly before harvest in central-western Poland in 2021. Four convolutional neural network architectures—U-Net (U-shaped network), U-Net++, DeepLabV3+ (deep learning + labelling), and PSPNet (Pyramid Scene Parsing Network)—were benchmarked using two input configurations: RGB imagery alone and RGB combined with the topographic position index (TPI) derived from a digital surface model (DSM). Model performance was assessed using overall accuracy, F1-score (harmonic mean of precision and recall), and Intersection over Union (IoU), with class-specific metrics reported to provide a realistic evaluation of damaged-area detection. For RGB-only data, overall accuracy ranged from 0.957 to 0.972, while damaged-class F1 and IoU reached 0.752 and 0.603, respectively, for the best-performing model (U-Net). When RGB data were supplemented with TPI, overall accuracy and damaged-class metrics changed only slightly, indicating limited benefit from the topographic feature under these field conditions. Non-damaged crop areas were consistently well-classified (F1 > 0.977, IoU > 0.955). These results confirm that UAV-based RGB imagery enables reliable late-season assessment of wildlife-induced crop damage, and that reporting class-specific metrics in spatially independent test sets is essential for realistic performance evaluation.

Why it matches plant phenotyping methodsUAV画像と深層学習セグメンテーションを用いて、ナタネの野生動物被害という植物状態を推定し、複数モデルをベンチマーク・性能評価しているため、フェノタイピング手法が中心である。

titleDeep Learning Segmentation Models for UAV-Based Detection of Crop Damage in Rapeseed Using RGB Imagery

Dissecting the genetic architecture of seed-related traits in Brassica napus by integrating multi-omics analysis and VIS-NIR hyperspectral imaging. →

Background Brassica napus (B. napus) is globally important oilseed crop, yet traditional approaches for phenotyping of seed traits are labor-intensive and destructive. Results Here, we establish a non-destructive analytical framework integrating hyperspectral imaging (HSI) with machine learning for characterizing seed-related traits. We collect HSI data from seeds of 393 B. napus accessions over two consecutive years, generating 1,944 spectral indices per sample. We identify significant correlations between 1,293 hyperspectral indices and 956 seed metabolites. Flavonoid metabolites exhibit the most consistent interannual correlations with hyperspectral indices. Systematic benchmarking of 19 machine learning algorithms identifies nine optimal models for metabolite prediction, with 73.44% of metabolites achieving significant associations. Hyperspectral indices effectively predict nine key seed-related traits, including oil content, seed coat content, glucosinolate content and six fatty acid components. Genome-wide association studies (GWAS) of hyperspectral indices uncover three stable quantitative trait loci (QTL) hotspots, qHSI.hotA09, qHSI.hotA05 and qHSI.hotC05, that co-localize with QTLs for seed oil and seed coat content. Integration of GWAS with POCKET prioritization identifies BnaA09.MYB52 and BnaC05.PMT6 as candidate genes for the hotspots, qHSI.hotA09 and qHSI.hotC05, respectively. Functional validation using mutants demonstrates that both genes significantly influence seed flavonoid metabolites and hyperspectral profiles. BnaPMT6 is characterized as a novel positive regulator of seed coat content, similar to BnaMYB52. Conclusions This study establishes a novel, non-destructive approach for seed traits and metabolite assessment in B. napus seeds. It also provides a theoretical foundation and genetic basis for breeding of B. napus varieties with high oil content and improved nutritional quality.

Why it matches plant phenotyping methods種子形質を非破壊的に推定するハイパースペクトル画像と機械学習の分析フレームワークが研究の中心であり、多数の品種・複数年で検証されている。

abstractwe establish a non-destructive analytical framework integrating hyperspectral imaging (HSI) with machine learning for characterizing seed-related traits.

Harnessing hyperspectral imaging and machine learning to enhance salinity stress detection in canola →

This study investigates the application of hyperspectral imaging and machine learning techniques for detecting and classifying salinity stress in canola (Brassica napus L.). After analysis of various methods, we employed a ridge classifier model to categorize six classes of salinity, utilizing various spectral bands and vegetation indices across multiple model iterations. Spectral signature analysis revealed significant changes in reflectance patterns for wavelengths exceeding 740 nm, corresponding to the near-infrared (NIR) region. We developed two novel vegetation indices tailored for salinity stress detection, which, when combined with established indices and selected spectral bands, significantly improved classification accuracy. Our sequential model refinement process demonstrated incremental improvements in accuracy, with the final model achieving 82.61 % accuracy on the test set using only 15 features. This represents a substantial reduction from the initial 331 features while maintaining high accuracy. The most effective features primarily spanned wavelengths corresponding to Sentinel-2A bands, with notable exceptions at 405.04 nm and 983.96 nm. Comparison with Sentinel-2 spectral bands revealed that while some important wavelengths align with the satellite sensor’s capabilities, several fall outside its capture range. Notably, our findings suggest that Sentinel-2 bands B1, B5, B6, B7, and B9 may have limited efficacy in identifying salinity stress in canola, highlighting the potential for crop-specific optimization of spectral bands in remote sensing applications. This comprehensive analysis provides insights into the most effective spectral regions and vegetation indices for salinity classification in canola, offering the potential for improved precision agriculture practices. Our findings contribute to the growing body of knowledge on non-invasive crop stress detection and pave the way for future research in hyperspectral imaging applications for sustainable agriculture.

Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習を用いて、カノーラの塩ストレスを検出・分類する手法を開発・評価しており、植物状態の取得・推定が研究の中心である。

abstractThis study investigates the application of hyperspectral imaging and machine learning techniques for detecting and classifying salinity stress in canola (Brassica napus L.).

Larval antibiosis to cabbage stem flea beetle ( Psylliodes chrysocephala ) is absent within oilseed rape ( Brassica napus ) →

BACKGROUND Insect pests present a global threat to crops, with plant resistance representing a key breeding goal. The cabbage stem flea beetle ( Psylliodes chrysocephala ; CSFB) is a key pest of oilseed rape ( Brassica napus ; OSR) within Europe; however, CSFB resistance is yet to be found within B. napus . To address this, we examine CSFB larval development over time, explore antibiosis across a genetically diverse B. napus panel, and test whether larvae can develop within model Brassicaceae species ( Brassica rapa and Arabidopsis thaliana ). RESULTS CSFB larvae completed development from four-weeks post-infestation, undergoing a 20-fold size increase, with larval recovery after two weeks allowing semi-high throughput resistance phenotyping. Applying this method to 98 Brassicaceae genotypes (97 B. napus and a single Sinapis alba ), we found weak evidence for genotype effects on larval survival. However, phenotype validation with ‘resistant’ and ‘susceptible’ B. napus genotypes showed no differences in larval survival or adult emergence. Larval antibiosis was consistently observed in S. alba . Finally, we showed that model B. rapa and A. thaliana genotypes represent suitable hosts for CSFB, with larvae increasing 8-10× in size after two weeks. CONCLUSION CSFB larval antibiosis appears absent within B. napus , possibly due to bottlenecks experienced during domestication. However, larval antibiosis is present within S. alba , and future work should study the basis of this resistance. Further, CSFB larval screening in Brassicaceae model species presents an opportunity to explore CSFB resistance genetics, informing breeding progress for insect resistance in B. napus .

Why it matches plant phenotyping methodsCSFB幼虫の回収・サイズ評価による抵抗性表現型測定法を開発し、半高スループット化、遺伝子型パネルへの適用、表現型検証を行っており、フェノタイピング手法が中心的です。

abstractlarval recovery after two weeks allowing semi-high throughput resistance phenotyping

Plant-to-camera enabled 3D morphological reconstruction: A high-fidelity approach for plant phenotyping →

Three-dimensional (3D) plant modeling is fundamental for precise phenotyping analysis. In this study, a high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales. The pipeline comprises five core components: data acquisition, semantic segmentation, sparse reconstruction, dense reconstruction, and phenotypic trait extraction. To enhance the accuracy of plant structure identification, the SegFormer semantic segmentation model is employed for pixel-level segmentation, thereby guiding the subsequent reconstruction stages to focus specifically on plant regions. In feature-based sparse reconstruction, the scarcity of texture information often results in an insufficient number of matching point pairs, leading to failures in camera parameter estimation. Furthermore, the reconstructed point clouds frequently lack consistency with real-world scale. To address these challenges, a calibration-constrained sparse reconstruction method, Sparse Reconstruction from Calibrated Images (SRCI) was proposed. By integrating precise calibration results computed in a custom world coordinate system, SRCI circumvents the limitations of traditional feature matching in scenarios with scarce features, thereby resolving camera pose estimation failures caused by insufficient matching pairs and generating sparse point clouds with true physical scale. Subsequently, CL-MVSNet was employed to generate dense point clouds. The validation experiments are conducted from three perspectives: visual comparison, phenotypic accuracy assessment, and reconstruction accuracy evaluation. First, five groups of rapeseed plants are selected to perform visual comparisons between the proposed reconstruction pipeline and other advanced reconstruction software. The results demonstrate that the proposed reconstruction pipeline achieves superior performance in terms of visual quality. Additionally, three phenotypic parameters of rapeseed plants—plant height, leaf width, and chord length are manually measured and compared with the corresponding phenotypic parameters extracted from the reconstructed point clouds. The analysis revealed mean absolute errors of 4.93 mm, 3.16 mm, and 6.02 mm; root mean square errors of 6.38 mm, 4.56 mm, and 8.35 mm; and coefficients of determination of 0.98, 0.94, and 0.93, respectively. To further validate the generalization performance and accuracy of the proposed method, four additional plant categories with progressively increasing complexity were selected for accuracy evaluation. For the first three plant categories, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 2.4 mm. In the most complex rice reconstruction experiments, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 9 mm, while other methods failed to achieve effective reconstruction. The proposed high-throughput, automated, and high-quality 3D reconstruction framework provides reliable technical support and data resources for genetic research applications, including gene localization, quantitative trait locus analysis, and genome-wide association studies. • We propose a Plant-to-Camera system for high-quality 3D plant reconstruction within 6 minutes, showing strong generalizability. • Our Sparse Reconstruction from Calibrated Images (SRCI) method prevents failures in feature-scarce scenes. • We develop SFNet, a feature descriptor module that fuses multi-frequency to enhance plant feature representation.

Why it matches plant phenotyping methods植物の3D再構成と形質抽出パイプラインを開発し、植物形質および再構成精度を検証しており、フェノタイピング手法が中心である。

abstracta high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales

Rapeseed seedling counting and geospatial localization system integrating visual tracking and real-time kinematic positioning →

Accurate estimation of rapeseed emergence requires reliable counting and spatially precise localization under field conditions. The video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing. Seedling centers are extracted frame by frame, clustered along the row direction with an adaptively estimated neighborhood radius and centroid-based inter-frame matching, then tracked and counted within a drifting spatiotemporal window. A calibrated projection chain links image coordinates to world coordinates by fusing visual trajectories with RTK reference points, thereby generating centimeter-level geospatial identities. Field experiments conducted on 12 videos covering 120 rapeseed varieties at the East Anhui Experimental Station of Anhui Agricultural University demonstrate strong performance: the detector achieves an AP of 93.6 % with a processing speed of 110 FPS; HAD-DBSCAN reaches 0.967 clustering accuracy while preserving row integrity under uneven density and delayed emergence; the tracking module attains a tracking accuracy (Pₜᵣ) of 92.5 %, a tracking precision (Pₘₜ) of 93.1 %, an ID switch rate (WID) of 7.4 %, and a counting precision (Pc) of 92.8 %; Geolocation yields a mean error of 2.84 cm with quasi-normal residuals centered near zero. These results establish a unified framework for efficient seedling counting and multi-temporal plant-level monitoring, enabling growth analysis to support high-throughput phenotyping.

Why it matches plant phenotyping methods圃場画像から菜種幼苗の検出・追跡・計数・高精度位置推定を行う方法を開発し、性能検証しており、植物表現型取得が研究の中心である。

abstractThe video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing.

Inversion of total photosynthetic area index of oilseed rape based on a multilayer microwave scattering semiempirical model adapted to each phenological stage →

To accurately invert the canopy parameters (Total photosynthetic area index, TPAI) of oilseed rape, a microwave characteristic layered measurement experiment was designed, and a multilayer microwave scattering semiempirical model (MLMSSM) was constructed based on the radar response changes induced by the differences in the vertical structure and canopy components at different phenological stages. This model was then applied to the main oilseed rape production areas to conduct regional TPAI inversion. Microwave characteristic layered measurement experiments were performed at six-leaf, flowering, beginning ripening and fully ripening stages of oilseed rape in the laboratory of target microwave properties (LAMP). The MLMSSM was constructed based on the LAMP-measured data, corresponding to different plant structures containing two- and three-layer submodels, and the TPAI inversion model was derived based on the correlation between the MLMSSM parameters and the crop biophysical variables. Finally, regional TPAI inversion and validation were carried out in the main oilseed rape production area (Hengyang), using Sentinel-1 SAR data. Visualization results, MLMSSM parameters and TPAI inversion results based on LAMP data all revealed the occurrence of multiple scattering interactions among distinct oilseed rape structural layers and verified the effectiveness of the measurement scheme and the model. The regional validation results showed high TPAI inversion accuracy throughout entire oilseed rape growth stages, with R² = 0.78, RMSE = 1.03, and MAE = 0.74 under VV polarization, and R² = 0.84, RMSE = 0.75, and MAE = 0.52 under VH polarization. The MLMSSM was found to significantly outperform the modified water cloud model (MWCM), increasing R² by 0.05 and 0.18 under VV and VH polarization respectively, while reducing the RMSE and MAE by 0.49–0.72. These results prove the accuracy and applicability of the MLMSSM for regional TPAI inversion of oilseed rape.

Why it matches plant phenotyping methods油糧ナタネの群落光合成面積指数を推定するマイクロ波計測・散乱モデルを開発し、Sentinel-1 SARで地域検証しており、植物形質取得法が中心である。

abstracta multilayer microwave scattering semiempirical model (MLMSSM) was constructed

High-throughput extraction of individual plant height in rapeseed based on LiDAR-Camera data fusion →

Efficient and accurate extraction of plant height (PH) plays an important role in analyzing its deeper phenotypic traits and improving breeding efficiency. Traditional methods make it difficult to measure PH at the individual plant level in the plot-level on a large scale, with high accuracy and low delay. To address this issue, we adopted a truss-type phenotyping platform to acquire LiDAR and RGB canopy data from 120 rapeseed genotypes from the two-leaf stage to the flowering stage, covering a total of 6 growth stages. (1) Object detection was performed to identify per plant of rapeseed images by the K-Means improved Faster R-CNN algorithm. (2) The image data before and after object detection and the point cloud data were fused to recognize per plant on the point cloud. Besides, the rapeseed plant point cloud and the ground point cloud were distinguished by color. (3) The Cloth Simulation Filter (CSF) algorithm is used to fit the ground points obscured by the canopy, which contributes to accurately extracting the PH of individual rapeseed. The field tests indicated that the mAP (IoU = 0.5) of the improved object detection method was 0.902, which achieved a high detection accuracy for rapeseed with different sizes in all periods. Specifically, in the PH accuracy verification of the No. 077 cultivar, R² was 0.997, RMSE was 1.156 cm, rRMSE was 5.48 %, and the maximum difference between automatic recognition and manual measurement in the late stage of growth was less than 6 cm. Especially, the proposed method can extract of the height of individual plant with the values of R² was 0.980, RMSE was 0.651 cm, rRMSE was 6.543 % in the seedling stage and the PH was lower than 20 cm. The differences of pH of 40 rapeseed genotypesunder cold stress were compared, which provide a reference for high-throughput PH extraction and exploring genotypic differences in plant breeding.

Why it matches plant phenotyping methodsLiDAR・RGBデータ融合と画像／点群処理により、個体ごとの草丈を高精度・ハイスループットに抽出する手法の開発と検証が中心であるため。

titleHigh-throughput extraction of individual plant height in rapeseed based on LiDAR-Camera data fusion

Rapeseed seedling counting and geospatial localization system integrating visual tracking and real-time kinematic positioning →

Accurate estimation of rapeseed emergence requires reliable counting and spatially precise localization under field conditions. The video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing. Seedling centers are extracted frame by frame, clustered along the row direction with an adaptively estimated neighborhood radius and centroid-based inter-frame matching, then tracked and counted within a drifting spatiotemporal window. A calibrated projection chain links image coordinates to world coordinates by fusing visual trajectories with RTK reference points, thereby generating centimeter-level geospatial identities. Field experiments conducted on 12 videos covering 120 rapeseed varieties at the East Anhui Experimental Station of Anhui Agricultural University demonstrate strong performance: the detector achieves an AP of 93.6 % with a processing speed of 110 FPS ; HAD-DBSCAN reaches 0.967 clustering accuracy while preserving row integrity under uneven density and delayed emergence; the tracking module attains a tracking accuracy ( P tr ) of 92.5 %, a tracking precision ( P mt ) of 93.1 %, an ID switch rate ( W ID ) of 7.4 %, and a counting precision ( P c ) of 92.8 %; Geolocation yields a mean error of 2.84 cm with quasi-normal residuals centered near zero. These results establish a unified framework for efficient seedling counting and multi-temporal plant-level monitoring, enabling growth analysis to support high-throughput phenotyping.

Why it matches plant phenotyping methods画像検出・追跡・クラスタリング・RTK測位を統合し、圃場での rapeseed 苗の計数と個体位置推定を技術的に開発・検証しており、植物表現型取得が中心である。

abstractThe video-based system, YOLO_DBSCAN_Track_RTK, integrates lightweight YOLOv11n detection, DeepSORT object tracking, Horizontal Adaptive Distance DBSCAN (HAD-DBSCAN) for crop-row clustering, a dynamic boundary-drift counting mechanism, and RTK-GNSS-aided georeferencing.

A structure-aware deep learning combining fine-grained silique segmentation with occlusion compensation for rapeseed yield estimation from multi-view UAV imagery →

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

Why it matches plant phenotyping methodsアブラナの莢を対象に、マルチビューUAV画像からのセグメンテーションと遮蔽補償によって収量を推定する手法開発が中心であり、植物形質推定に該当する。

titleA structure-aware deep learning combining fine-grained silique segmentation with occlusion compensation for rapeseed yield estimation from multi-view UAV imagery

FARM: Crop Yield Prediction via Regression on Prithvi’s Encoder for Satellite Sensing →

Accurate and timely crop yield prediction is crucial for global food security and modern agricultural management. Traditional methods often lack the scalability and granularity required for precision farming. This paper introduces FARM (Fine-tuning Agricultural Regression Models), a deep learning framework designed for high-resolution, intra-field canola yield prediction. FARM leverages a pre-trained, large-scale geospatial foundation model (Prithvi-EO-2.0-600M) and adapts it for a continuous regression task, transforming multi-temporal satellite imagery into dense, pixel-level (30 m) yield maps. Evaluated on a comprehensive dataset from the Canadian Prairies, FARM achieves a Root Mean Squared Error (RMSE) of 0.44 and an R2 of 0.81. Using an independent high-resolution yield monitor dataset, we further show that fine-tuning FARM on limited ground-truth labels outperforms training the same architecture from scratch, confirming the benefit of pre-training on large, upsampled county-level data for data-scarce precision agriculture. These results represent improvement over baseline architectures like 3D-CNN and DeepYield, which highlight the effectiveness of fine-tuning foundation models for specialized agricultural applications. By providing a continuous, high-resolution output, FARM offers a more actionable tool for precision agriculture than conventional classification or county-level aggregation methods. This work validates a novel approach that bridges the gap between large-scale Earth observation and on-farm decision-making, offering a scalable solution for detailed agricultural monitoring.

Why it matches plant phenotyping methods衛星画像から圃場内の作物収量を推定する回帰手法を開発し、独立データで検証しており、植物の収量形質の取得・推定が中心である。

abstractThis paper introduces FARM (Fine-tuning Agricultural Regression Models), a deep learning framework designed for high-resolution, intra-field canola yield prediction.

ST-DETrack: Identity-Preserving Branch Tracking in Entangled Plant Canopies via Dual Spatiotemporal Evidence →

Automated extraction of individual plant branches from time-series imagery is essential for high-throughput phenotyping, yet it remains computationally challenging due to non-rigid growth dynamics and severe identity fragmentation within entangled canopies. To overcome these stage-dependent ambiguities, we propose ST-DETrack, a spatiotemporal-fusion dual-decoder network designed to preserve branch identity from budding to flowering. Our architecture integrates a spatial decoder, which leverages geometric priors such as position and angle for early-stage tracking, with a temporal decoder that exploits motion consistency to resolve late-stage occlusions. Crucially, an adaptive gating mechanism dynamically shifts reliance between these spatial and temporal cues, while a biological constraint based on negative gravitropism mitigates vertical growth ambiguities. Validated on a Brassica napus dataset, ST-DETrack achieves a Branch Matching Accuracy (BMA) of 93.6%, significantly outperforming spatial and temporal baselines by 28.9 and 3.3 percentage points, respectively. These results demonstrate the method's robustness in maintaining long-term identity consistency amidst complex, dynamic plant architectures.

Why it matches plant phenotyping methods植物画像から個体枝を追跡・抽出する手法を開発し、アブラナ dataset で性能検証しているため、植物表現型取得の中心的研究である。

abstractAutomated extraction of individual plant branches from time-series imagery is essential for high-throughput phenotyping

Impedance flow cytometry for rapid quality assessment of protoplast cultures. →

Background Protoplasts, which are plant cells devoid of cell walls, are valuable tools in plant biotechnology. However, they are highly sensitive to mechanical and osmotic stress during isolation and early culture, often leading to significant loss of viability. Reliable and efficient methods for monitoring protoplast quality are essential for downstream applications. Results We applied impedance flow cytometry to assess the viability, cell size, and early division of freshly isolated protoplasts from Arabidopsis thaliana, Brassica napus, and Beta vulgaris. This label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations. Importantly, IFC-derived viability metrics strongly correlated with microcallus formation, demonstrating their predictive value for culture competence. Conclusions Impedance flow cytometry provides a robust, efficient and reproducible method for characterizing protoplast cultures. It enables rapid assessment of viability and growth potential, supporting quality control and optimization in plant cell culture workflows.

Why it matches plant phenotyping methodsインピーダンスフローサイトメトリーを用いて、植物プロトプラストの生存性・細胞サイズ・初期分裂を高速かつ高スループットに測定し、培養能力との相関で妥当性を検証しているため、植物表現型取得法が中心です。

abstractThis label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations.

Combining Hyperspectral Imaging with Ensemble Learning for Estimating Rapeseed Chlorophyll Content Under Different Waterlogging Durations. →

Chlorophyll content is a key physiological indicator reflecting photosynthetic capacity, and the Soil–Plant Analysis Development (SPAD) meter is a commonly used tool for its rapid and non-destructive estimation. Hyperspectral imaging (HSI) is a non-destructive technique that captures fine spectral characteristics and thus holds great potential for high-throughput phenotyping and early stress detection. This study aimed to explore the potential of HSI combined with ensemble learning (EL) to estimate SPAD of rapeseed seedlings under different durations of waterlogging. Hyperspectral images and corresponding SPAD values were collected from six rapeseed cultivars at 0, 2, 4 and 6 days of waterlogging. The mutual information was employed to select the top 30 most relevant spectral and vegetation index features. The EL model was constructed using partial least squares, support vector machine, random forest, ridge regression and elastic net as the first-layer learners and a multiple linear regression as the second-layer learner. The results showed that the EL model showed superior stability and higher prediction accuracy compared to single models across various genotypes and waterlogging treatment datasets. As waterlogging duration increased, the overall model accuracy improved; notably, under 6 days of waterlogging, the EL model achieved an R2 of 0.79 and an RMSE of 3.27, indicating strong predictive capability. This study demonstrated that combining EL with HSI enables stable and accurate estimation of SPAD values, therefore providing an effective approach for early stress monitoring in crops.

Why it matches plant phenotyping methodsHSIとアンサンブル学習により、ラペシードのクロロフィル含量（SPAD）を非破壊推定する手法が研究の中心であり、植物表現型取得・ストレスモニタリングに直接結びつく。

abstractThis study aimed to explore the potential of HSI combined with ensemble learning (EL) to estimate SPAD of rapeseed seedlings under different durations of waterlogging.

Data-efficient and accurate rapeseed leaf area estimation by self-supervised vision transformer for germplasms early evaluation. →

Early-stage, accurate and high-throughput phenotyping‌ through leaf area estimation is ‌critical‌ for future rapeseed breeding, but faces ‌two key constraints‌: expensive data annotation and persistent challenge of leaf occlusion. To address these issues, we present a ‌data-efficient‌ deep learning framework using smartphone-captured top-down RGB images for rapeseed leaf area quantification. Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated dataset of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning method. This pre-trained model is then fine-tuned on a custom rapeseed dataset using a novel Canopy-Mix data augmentation technique to handle fragmented views analogous to occlusion, and a hybrid loss function combining Smooth L1 and Log-Cosh for robust convergence. Through rigorous 5-fold cross-validation, our proposed model achieved strong predictive performance (Coefficient of Determination, R[Formula: see text]=0.805). Moreover, the predicted leaf area demonstrated a remarkably strong correlation with both fresh weight (r=0.900) and dry weight (r=0.885). The model significantly outperformed a range of baselines, including models trained from scratch, those pre-trained on ImageNet, and a heuristic method based on manually annotated bounding boxes. Ablation studies confirmed the essential contribution of each component, while qualitative analysis of attention maps demonstrated the model's ability to precisely localize the leaf canopy and ignore background distractors. This study demonstrates that domain-specific self-supervised pre-training offers a powerful solution to overcome data limitations in agricultural vision, providing a robust and scalable tool for non-destructive phenotyping that can potentially accelerate the rapeseed breeding cycle.

Why it matches plant phenotyping methods葉面積という植物形質をRGB画像から推定する深層学習法を開発し、交差検証・ベースライン比較・アブレーションで技術検証しているため、方法が中心である。

abstractwe present a ‌data-efficient‌ deep learning framework using smartphone-captured top-down RGB images for rapeseed leaf area quantification.

Polli-markers: spectral and chemical biomarkers for detecting cryptic early plant pollination responses →

Summary Pollination is essential for plant reproduction, ecosystem resilience and human health. Yet, our capability to map pollination service delivery in real-time across large areas remains poor. Determining where and when flowers are pollinated is vital to mitigate widespread pollination deficits, increase plant health and yield, and support pollinator management. Hence, innovative approaches are urgently needed for establishing scalable predictive bioindicators of plant pollination status with the goal of achieving real-time landscape-scale monitoring. Here we present two parallel controlled pollination assays in which we characterise the post-pollination petal physiology of a world leading flowering crop, Brassica napus , using in-situ close-range hyperspectral reflectance and semi-untargeted metabolomics. This multiomics approach coupled with supervised machine learning and biomarker detection reveals cryptic changes in the UV petal reflectance spectrum which are predictive of pollination status, representing a novel set of candidate pollination bioindicators (‘ polli-markers’ ), and our high-resolution time series enables prediction of when this pollination event occurred. It also reveals an associated set of candidate metabolites, including flavonoids and senescence markers, shedding light on the functional pathways related to our polli-markers. This study provides key insights into floral development, enabling a transformative step towards predicting, mapping and quantifying pollination service delivery at the landscape scale.

Why it matches plant phenotyping methods近接ハイパースペクトル計測と機械学習により、植物花弁の受粉状態および受粉時点を推定する方法を開発しており、表現型取得・推定が研究の中心である。

abstractusing in-situ close-range hyperspectral reflectance and semi-untargeted metabolomics

MSNet: A multispectral-image driven rapeseed canopy instance segmentation network →

Precise detection of rapeseed and the growth of its canopy area are crucial phenotypic indicators of its growth status. Achieving accurate identification of the rapeseed target and its growth region provides significant data support for phenotypic analysis and breeding research. However, in natural field environments, rapeseed detection remains a substantial challenge due to the limited feature representation capabilities of RGB-only modalities. To address this challenge, this study proposes a dual-modal instance segmentation network, MSNet, based on YOLOv11n-seg, integrating both RGB and Near-Infrared (NIR) modalities. The main improvements of this network include three different fusion location strategies (frontend fusion, mid-stage fusion, and backend fusion) and the newly introduced Hierarchical Attention Fusion Block (HAFB) for multimodal feature fusion. Comparative experiments on fusion locations indicate that the mid-stage fusion strategy achieves the best balance between detection accuracy and parameter efficiency. Compared to the baseline network, the mAP50:95 improvement can reach up to 3.5 %. After introducing the HAFB module, the MSNet-H-HAFB model demonstrates a 6.5 % increase in mAP50:95 relative to the baseline network, with less than a 38 % increase in parameter count. It is noteworthy that the mid-stage fusion consistently delivered the best detection performance in all experiments, providing clear design guidance for selecting fusion locations in future multimodal networks. In addition, comparisons with various RGB-only instance segmentation models show that all the proposed MSNet-HAFB fusion models significantly outperform single-modal models in rapeseed count detection tasks, confirming the potential advantages of multispectral fusion strategies in agricultural target recognition. Finally, the MSNet was applied in an agricultural case study, including vegetation index level analysis and frost damage classification. The results show that ZN6–2836 and ZS11 were predicted as potential superior varieties, and the EVI2 vegetation index achieved the best performance in rapeseed frost damage classification.

Why it matches plant phenotyping methodsマルチスペクトル画像からナタネの個体・キャノピー領域を抽出するセグメンテーション手法を開発し、性能比較と霜害分類への応用を行っており、植物表現型取得が中心である。

abstractPrecise detection of rapeseed and the growth of its canopy area are crucial phenotypic indicators of its growth status.

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.

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 portable rapeseed quality non-destructive inspection device based on multichannel spectroscopy →

It is essential to develop low-cost, rapid and portable systems for detecting the quality of rapeseed planting, harvesting, and storage. A multichannel spectral detection system for the quantitative assessment of rapeseed oil, protein, glucosinolate, and moisture content was developed in this study. The core hardware of the system comprises a custom-designed spectral acquisition module and a Raspberry Pi. The spectral module consists of a spectrum sensor and a characteristic wavelength LED, featuring 10 channels with a wavelength range of 850–1550 nm included. The results from the test set indicate that the most accurate oil predictions can be achieved using the SPXY+SNV+CARS+PLS method. For protein predictions, the optimal results were obtained using the Random+MSC +UVE+PLS approach. The best predictions for glucosinolates and moisture content were achieved with the Random+SNV+CARS+PLS method. To verify the performance of this systems, independent data were used for external validation. The RMSE, R², MAE results for oil, protein, glucosinolates, and moisture were 2.04 %, 0.69, 1.58 %, 1.52 %, 0.67, 1.25 %, 18.86μmol·g⁻¹, 0.52, 15.03μmol·g⁻¹, 0.36 %, 0.74, 0.33 %, respectively. In general, the developed detection system has potential for rapid detection of rapeseed in the field or market.

Why it matches plant phenotyping methods菜種種子の油分・タンパク質・グルコシノレート・水分という種子形質を対象に、マルチチャネル分光による携帯型測定システムを開発し、外部検証まで行っており、形質取得法が研究の中心である。

abstractA multichannel spectral detection system for the quantitative assessment of rapeseed oil, protein, glucosinolate, and moisture content was developed in this study.

Rapeseed Yield Estimation Using UAV-LiDAR and an Improved 3D Reconstruction Method →

Quantitative estimation of rapeseed yield is important for precision crop management and sustainable agricultural development. Traditional manual measurements are inefficient and destructive, making them unsuitable for large-scale applications. This study proposes a canopy-volume estimation and yield-modeling framework based on unmanned aerial vehicle light detection and ranging (UAV-LiDAR) data combined with a HybridMC-Poisson reconstruction algorithm. At the early yellow ripening stage, 20 rapeseed plants were reconstructed in 3D, and field data from 60 quadrats were used to establish a regression relationship between plant volume and yield. The results indicate that the proposed method achieves stable volume reconstruction under complex canopy conditions and yields a volume–yield regression model. When applied at the field scale, the model produced predictions with a relative error of approximately 12% compared with observed yields, within an acceptable range for remote sensing–based yield estimation. These findings support the feasibility of UAV-LiDAR–based volumetric modeling for rapeseed yield estimation and help bridge the scale from individual plants to entire fields. The proposed method provides a reference for large-scale phenotypic data acquisition and field-level yield management.

Why it matches plant phenotyping methodsUAV-LiDARによる3D再構成とキャノピー体積推定を開発・検証し、植物体積および収量という植物形質を推定する手法が研究の中心である。

abstractThis study proposes a canopy-volume estimation and yield-modeling framework based on unmanned aerial vehicle light detection and ranging (UAV-LiDAR) data combined with a HybridMC-Poisson reconstruction algorithm.

DeepCanola: Phenotyping brassica pods using semi-synthetic data and active learning →

Phenotyping, the measurement of attributes or traits, is crucial in selecting superior cultivars for specific environmental situations. This is a time-consuming process when applied to large populations but can be accelerated through the use of deep learning, resulting in an algorithm that can phenotype images of specimens in negligible amounts of time. The primary issue with deep learning is the large quantities of high-quality training data required to make a viable phenotyping pipeline. To address this, we present a semi-synthetic training data generation system which significantly reduces the amount of human effort spent on data collection. We use active learning alongside this system to create DeepCanola, an instance segmentation model that successfully segments and measures the valves from Brassica napus pods. We demonstrate that the model accurately estimates the effect of different winter cold treatments on a range of different cultivars and crop types as effectively as manually curated measurements. Furthermore, the resulting model is effective on data from various experimental settings and on different, but related, species such as Arabidopsis thaliana, Allaria petiolate (garlic mustard) and Raphanus raphanistrum subsp. sativus (radish). This robust tool could be easily scaled, thereby accelerating breeding or fundamental research programs. Code and model weights: https://github.com/kieranatkins/deepcanola.

Why it matches plant phenotyping methods植物の莢画像からバルブを分割・測定する深層学習フェノタイピング手法を開発し、半合成データとアクティブラーニング、複数条件・種での性能検証を行っているため。

abstractWe use active learning alongside this system to create DeepCanola, an instance segmentation model that successfully segments and measures the valves from Brassica napus pods.

Use of near-infrared spectroscopy for screening the oil content, protein, phytic acid, glucosinolates, and fatty acid profile in oilseed Brassica species. →

The escalating global demand for vegetable oils underscores the need to enhance the quality and yield of oilseed crops with Brassica species, due to their rich oil content and nutritional benefits. Traditional methods for assessing seed quality traits are often slow and destructive, limiting their scalability in breeding programs. This study presents Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid, non-destructive alternative to evaluate these critical traits across 80 diverse Brassica genotypes, including three species, namely, Brassica juncea, Brassica napus , and Brassica rapa . By integrating FT-NIR with principal component analysis and partial least squares regression, we developed robust calibration models, achieving high predictive accuracy (R 2 > 0.85 for key fatty acids; R 2 = 0.92 for oil content) and low error rates (MAE Brassica cultivars with optimized nutritional profiles high in beneficial polyunsaturated fatty acids and low in anti-nutritional factors.

Why it matches plant phenotyping methodsFT-NIR分光法を用いてBrassica種子の品質・組成形質を非破壊推定し、校正モデルの精度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に該当する。

abstractThis study presents Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid, non-destructive alternative to evaluate these critical traits across 80 diverse Brassica genotypes

Dynamic whole-life cycle measurement of individual plant height in oilseed rape through the fusion of point cloud and crop root zone localization →

Plant height (PH) of oilseed rape, as a crucial phenotypic indicator, provides essential data for seedling diagnosis and breeding selection when accurately monitored throughout the life cycle of individual plants. However, it is a challenge to obtain precise PH measurements as rapeseed leaves and other crops shade each other after flowering. In this study, a rail-based platform equipped with LiDAR and the BeiDou differential positioning system was designed and manufactured to autonomously collect time-series point cloud of oilseed rape populations in the field. The point cloud data of oilseed rape during the regreening stage was segmented using an improved fast Euclidean clustering algorithm, followed by extraction of the root collar region via an objective function. Centered on the identified root collar region, an adaptive plant envelope area (PEA) was generated based on the distance between adjacent plants to isolate individual rapeseed specimens. Within the PEA corresponding to each plant’s root collar region, the ground position during sowing and the canopy apex at distinct growth stages were precisely localized, enabling automated extraction of individual PH across the full life cycle. The coefficient of determination (R²) between the algorithm and the manual measurement results at 140, 150 and 165 days after sowing were 0.9742, 0.9667, and 0.9208, respectively. And Root Mean Square Error (RMSE) between the algorithm and the manual measurement results at 140, 150 and 165 days were 0.038, 0.043 and 0.061 m, respectively. These results confirm that integrating BeiDou positioning with 3D point cloud processing achieves high-precision phenotyping of crop height dynamics. Furthermore, PHs were applied to frost damage and lodging susceptibility analysis, which indicate that the growth rate of rapeseed slows down as the severity of frost damage increases, and plants that reach a height of approximately 1 m during the flowering stage are prone to lodging after rainfall. These results have the potential to provide guidance for frost damage assessment and variety selection in smart agriculture applications.

Why it matches plant phenotyping methodsLiDAR・BeiDou搭載プラットフォームと3D点群処理により、個体の生育全期間の草丈を自動抽出する手法を開発し、手測定と検証しているため、植物フェノタイピング手法が中心である。

abstracta rail-based platform equipped with LiDAR and the BeiDou differential positioning system was designed and manufactured to autonomously collect time-series point cloud of oilseed rape populations in the field.

Early detection of clubroot in canola using drone-based hyperspectral imaging and machine learning →

Clubroot (Plasmodiophora brassicae) is spreading rapidly on canola (Brassica napus) in Canada. The disease often occurs first in small patches and then spreads across the field if not recognized and treated. Early detection is challenging because above-ground symptoms develop after the crop starts to flower, when scouting is difficult. Clubroot interferes with water uptake and delays flowering, which may result in changes in spectral reflectance that could be detected using a hyperspectral camera. The objective was to determine if a drone-mounted hyperspectral camera could be used to identify patches of clubroot from the air. Twenty-three research and commercial canola fields were imaged in Alberta and Saskatchewan during flowering from 2021 to 2023, using a remotely piloted aircraft system outfitted with a hyperspectral camera. One research site in Alberta offered an ideal mix of infected and non-infected canola for training a predictive classification model. Model development using machine learning (ML) and detailed plot mapping yielded the best results. Stochastic Gradient Boosting (SGB) consistently outperformed other ML classification algorithms tested. A 31-spectral band SGB model was subsequently used to assess 21 images from locations where comparisons with field sampling could be made with certainty. These comparisons yielded 100 % agreement in clubroot detection at the field level and > 90 % agreement for individual patches. Near infrared bands 758–764 nm were most important, especially 760 and 764 nm. Use of drones and hyperspectral technology offers promise for improved detection of clubroot so growers could choose appropriate crop rotations or treat infested patches.

Why it matches plant phenotyping methodsドローン搭載ハイパースペクトル画像と機械学習により、圃場内の感染キャノピー状態（クラブルート罹病パッチ）を直接推定し、現地サンプリングで精度検証しているため、植物病害フェノタイピング手法が中心です。

abstractThe objective was to determine if a drone-mounted hyperspectral camera could be used to identify patches of clubroot from the air.

Rapid diagnosis of herbicidal activity and mode of action using spectral image analysis and machine learning →

Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape (Brassica napus), and analyzed using MATLAB 2021b to quantify NDI, ExG, Fd/Fₘ, and plant leaf temperature. NDI, ExG and Fd/Fₘ decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in Fd/Fₘ, while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 ​h enabled the diagnosis of herbicide MOAs with 89.6 ​% accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 ​%. The indices acquired at 6 ​h, and Fd/Fₘ and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 ​%, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.

Why it matches plant phenotyping methods植物のスペクトル画像から葉の生理状態・温度指標を抽出し、機械学習で除草剤作用機構を診断する手法の開発と精度検証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採用。

abstractthis study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).

Data-Efficient and Accurate Rapeseed Leaf Area Estimation by Self-supervised Vision Transformer for Germplasms Early Evaluation →

Abstract Early-stage, accurate and high-throughput phenotyping‌ through leaf area estimation is ‌critical‌ for future rapeseed breeding, but faces ‌two key constraints‌: expensive data annotation and persistent challenge of leaf occlusion. To address these issues, we present a ‌data-efficient‌ deep learning framework using smartphone-captured top-down RGB images for rapeseed leaf area quantification. Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated corpus of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning method. This pre-trained model is then fine-tuned on a custom rapeseed dataset using a novel Canopy-Mix data augmentation technique to handle fragmented views analogous to occlusion, and a hybrid loss function combining Smooth L1 and Log-Cosh for robust convergence. Through rigorous 5-fold cross-validation, our proposed model achieved state-of-the-art predictive performance (Coefficient of Determination, R$^2$=0.805). What’s more, the predicted leaf area demonstrated a remarkably strong correlation with both fresh weight (r=0.900) and dry weight (r=0.885). The model significantly outperformed a range of baselines, including models trained from scratch, those pre-trained on ImageNet, and a heuristic method based on manually annotated bounding boxes. Ablation studies confirmed the essential contribution of each component, while qualitative analysis of attention maps demonstrated the model's ability to precisely localize the leaf canopy and ignore background distractors. This study demonstrates that domain-specific self-supervised pre-training offers a powerful solution to overcome data limitations in agricultural vision, providing a robust and scalable tool for non-destructive phenotyping that can potentially accelerate the rapeseed breeding cycle.

Why it matches plant phenotyping methods画像から rapeseed の葉面積を推定する計算・画像ベースの表現型計測手法を開発し、交差検証、ベースライン比較、アブレーションで技術的に評価しているため、方法が研究の中心である。

abstractwe present a ‌data-efficient‌ deep learning framework using smartphone-captured top-down RGB images for rapeseed leaf area quantification.

A controlled environment assay for the rapid evaluation of verticillium stripe resistance in canola →

Abstract Verticillium stripe disease is an emerging threat to canola production in Western Canada. Accurately assessing verticillium stripe resistance in breeding germplasm collections is crucial for identifying sources of genetic resistance. Currently, field phenotyping is the most widely used method for evaluating verticillium stripe resistance; however, achieving uniform disease pressure under field conditions presents significant challenges. Here we report a novel controlled environment (CE) soil-less assay for the rapid evaluation of verticillium stripe resistance in canola. The CE results were validated in a field trial which showed a strong correlation between the field data and the CE results. This cost-effective and time-efficient assay enables accurate assessment of verticillium stripe symptoms in a one-month testing cycle.

Why it matches plant phenotyping methodsカノーラの病害抵抗性・症状を迅速に評価する新規の環境制御アッセイを開発し、圃場データとの相関で妥当性を検証しており、表現型取得法が中心である。

abstractHere we report a novel controlled environment (CE) soil-less assay for the rapid evaluation of verticillium stripe resistance in canola.

Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria →

Understanding how environmental variability shapes crop biomass is essential for improving yield stability and guiding climate-resilient agriculture. To address this, we compared biomass estimates from a semi-empirical light use efficiency (LUE) model with predictions from a machine learning–remote sensing framework that integrates environmental variables. We applied a combined LUE and random forest (RF) model to estimate the mean biomass of winter wheat and oilseed rape across Bavaria, Germany, from 2001 to 2019. Using a 5 km2 hexagon-based grid, we incorporated landscape metrics (land cover diversity, small woody features), topographic variables (elevation, slope, aspect), soil potential, and seasonal climate predictors (mean and standard deviation of temperature, precipitation, and solar radiation) across the growing season. The RF-based approach improved predictive accuracy over the LUE model alone, particularly for winter wheat. Biomass patterns were shaped by both landscape configuration and climatic conditions. Winter wheat biomass was more influenced by topographic and landscape features, while oilseed rape was more sensitive to solar radiation and soil properties. Moderately diverse landscapes supported higher biomass, whereas an extreme landscape fragmentation or high variability showed lower values. Temperature thresholds, above 21 °C for winter wheat and 12 °C for oilseed rape, were associated with biomass declines, indicating crop-specific sensitivities under Bavarian conditions. This hybrid modeling approach provides a transferable framework to map and understand crop biomass dynamics at scale. The findings offer region-specific insights that can support sustainable agricultural planning in the context of climate change.

Why it matches plant phenotyping methods作物バイオマスという植物形質を、LUEモデルとランダムフォレスト・リモートセンシングの統合手法で広域推定し、予測精度を比較・評価しているため、単なる環境要因研究ではなく形質推定手法の適用が中心です。

abstractThe RF-based approach improved predictive accuracy over the LUE model alone, particularly for winter wheat.

Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model →

Biomass estimation of oilseed rape is crucial for optimizing crop productivity and breeding strategies. While UAV-based imaging has advanced high-throughput phenotyping, current methods often rely on orthophoto images, which struggle with overlapping leaves and incomplete structural information in complex field environments. This study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape. UAV multi-view oblique images from 36 angles were used to perform 3D reconstruction, with the SAM module enhancing point cloud segmentation. The segmented point clouds were then converted into point cloud volumes, which were fitted to ground-measured biomass using linear regression. The results showed that 3DGS (7 k and 30 k iterations) provided high accuracy, with peak signal-to-noise ratios (PSNR) of 27.43 and 29.53 and training times of 7 and 49 min, respectively. This performance exceeded that of structure from motion (SfM) and mipmap Neural Radiance Fields (Mip-NeRF), demonstrating superior efficiency. The SAM module achieved high segmentation accuracy, with a mean intersection over union (mIoU) of 0.961 and an F1-score of 0.980. Additionally, a comparison of biomass extraction models found the point cloud volume model to be the most accurate, with an determination coefficient (R²) of 0.976, root mean square error (RMSE) of 2.92 g/plant, and mean absolute percentage error (MAPE) of 6.81 %, outperforming both the plot crop volume and individual crop volume models. This study highlights the potential of combining 3DGS with multi-view UAV imaging for improved biomass phenotyping.

Why it matches plant phenotyping methodsUAV多視点画像、3D再構成、SAMによる分割、体積からのバイオマス推定を統合・比較検証した、植物表現型取得法が研究の中心である。

abstractThis study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape.

Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model →

Biomass estimation of oilseed rape is crucial for optimizing crop productivity and breeding strategies. While UAV-based imaging has advanced high-throughput phenotyping, current methods often rely on orthophoto images, which struggle with overlapping leaves and incomplete structural information in complex field environments. This study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape. UAV multi-view oblique images from 36 angles were used to perform 3D reconstruction, with the SAM module enhancing point cloud segmentation. The segmented point clouds were then converted into point cloud volumes, which were fitted to ground-measured biomass using linear regression. The results showed that 3DGS (7 k and 30 k iterations) provided high accuracy, with peak signal-to-noise ratios (PSNR) of 27.43 and 29.53 and training times of 7 and 49 min, respectively. This performance exceeded that of structure from motion (SfM) and mipmap Neural Radiance Fields (Mip-NeRF), demonstrating superior efficiency. The SAM module achieved high segmentation accuracy, with a mean intersection over union (mIoU) of 0.961 and an F1-score of 0.980. Additionally, a comparison of biomass extraction models found the point cloud volume model to be the most accurate, with an determination coefficient (R²) of 0.976, root mean square error (RMSE) of 2.92 g/plant, and mean absolute percentage error (MAPE) of 6.81 %, outperforming both the plot crop volume and individual crop volume models. This study highlights the potential of combining 3DGS with multi-view UAV imaging for improved biomass phenotyping.

Why it matches plant phenotyping methodsUAV多視点画像、3D再構成、SAMによる分割を統合し、アブラナのバイオマス推定手法を開発・比較検証しており、表現型取得と技術性能が研究の中心である。

abstractThis study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape.

Phenotypic scoring of Canola Blackleg severity using machine learning image analysis →

Canola blackleg is a fungal disease that causes significant yield loss and plant death of infected canola ( Brassica napus L., Brassica rapa L. , Brassica juncea L. ) fields worldwide. One of the most effective methods for controlling blackleg is through the cultivation of resistant varieties. Consequently, scoring blackleg disease severity of infected plants is a key metric for identifying and selecting resistant varieties. Traditionally, blackleg severity is scored by expert raters who evaluate disease in stem cross sections using established rating scales and reference images; however, human raters are expensive and inconsistent in their scoring. Here, we introduce a machine learning algorithm based on deep learning models that can score blackleg severity from cross-section images of infected plants. We find that expert ratings are largely inconsistent across raters and across years for the same rater, creating substantial noise in susceptibility ratings. Meanwhile, our trained machine learning model performs more consistently than the median rater while maintaining a similar heritability as expert raters for the blackleg susceptibility trait. This model can be used to standardize blackleg susceptibility scoring across locations and years to improve canola breeding outcomes across affected regions. Core Ideas Canola Blackleg is a fungal disease affecting yield of canola, and accurate scoring of Blackleg severity is important for tracking disease and breeding for resistant varieties. The standard practice of utilizing expert raters is expensive, and scores assigned are inconsistent across raters and years. Our deep learning model for assigning blackleg severity scores is more accurate than the median expert rater, opening the door for improved breeding of new resistant varieties.

Why it matches plant phenotyping methods感染植物の画像から黒脚病の重症度という植物病害表現型を推定する深層学習手法を開発し、専門家評価との一貫性・遺伝率を検証しており、表現型取得・評価法が研究の中心である。

abstractHere, we introduce a machine learning algorithm based on deep learning models that can score blackleg severity from cross-section images of infected plants.

Discovery of Germplasm Resources and Molecular Marker-Assisted Breeding of Oilseed Rape for Anticracking Angle. →

Introduction: Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. Materials and Methods: In this study, a dual-track "genotype-phenotype" screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. Results: Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BnSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency ( 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7-48.7% oil content, mycosphaerella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 ( p Conclusions: The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production.

Why it matches plant phenotyping methods油菜角果抗裂这一植物性状的高通量获取方法是研究策略的组成部分，明确建立并应用了标准化随机碰撞表型系统，而非仅作常规性状测量。

abstracta standardized random collision phenotyping system for the complex quantitative trait of angular resistance

Critical nitrogen dilution curves for winter oilseed rape (Brassica napus L.) along the whole crop cycle: A Bayesian analysis →

Nitrogen (N) is the most limiting nutrient for crop growth. Determining the dynamics of N uptake by crops is essential to optimize N management and improve the sustainability of crop production, especially for winter oilseed rape (Brassica napus L.), a highly N-dependent crop. The concept of critical nitrogen concentration is an effective way to diagnose plant nitrogen status, defining the minimum concentration of nitrogen in the aerial parts needed to maximize shoot biomass at a given time in the crop growth. For winter oilseed rape the existing nitrogen dilution curve was developed in the 1990s, but the curve was established only until the beginning of flowering, the reproductive part not being covered. The main objective of this study is to determine new critical N dilution curves for winter oilseed rape over the whole crop cycle. To achieve this, we used data from 36 field trials, conducted between 2003 and 2020 under various N application rates and cultivars, and a new Bayesian statistical framework. For the vegetative phase, the critical curve (Ncvege=4.556 W-0.241) determined in this study was close to the curves found in previous studies. In addition, new critical N dilution curves were determined for the first time in winter oilseed rape, for the reproductive phase (Ncrepro =4.764 W-0.424) and for the entire growth period (Ncunique=7.364 W-0.582). Interestingly the parameters differed markedly from those recently obtained in spring canola. These new dilution curves should provide useful tools for determining the N nutritional status of winter oilseed rape over a longer period, and thus improve nitrogen nutrition diagnosis and enable optimal nitrogen management.

Why it matches plant phenotyping methods冬ナタネの作物全期間における窒素栄養状態を推定・診断する新しい臨界窒素希釈曲線を、Bayesian統計枠組みで開発しており、表現型状態の推定手法が研究の中心である。

abstractThe main objective of this study is to determine new critical N dilution curves for winter oilseed rape over the whole crop cycle.

Reconstruction, Segmentation and Phenotypic Feature Extraction of Oilseed Rape Point Cloud Combining 3D Gaussian Splatting and CKG-PointNet++ →

Phenotypic traits and phenotypic extraction at the seedling stage of oilseed rape play a crucial role in assessing oilseed rape growth, breeding new varieties and estimating yield. Manual phenotyping not only consumes a lot of labor and time costs, but even the measurement process can cause structural damage to oilseed rape plants. Existing crop phenotype acquisition methods have limitations in terms of throughput and accuracy, which are difficult to meet the demands of phenotype analysis. We propose an oilseed rape segmentation and phenotyping measurement method based on 3D Gaussian splatting with improved PointNet++. The CKG-PointNet++ network is designed to integrate CGLU and FastKAN convolutional modules in the SA layer, and introduce MogaBlock and a self-attention mechanism in the FP layer to enhance local and global feature extraction. Experiments show that the method achieves a 97.70% overall accuracy (OA) and 96.01% mean intersection over union (mIoU) on the oilseed rape point cloud segmentation task. The extracted phenotypic parameters were highly correlated with manual measurements, with leaf length and width, leaf area and leaf inclination R2 of 0.9843, 0.9632, 0.9806 and 0.8890, and RMSE of 0.1621 cm, 0.1546 cm, 0.6892 cm2 and 2.1144°, respectively. This technique provides a feasible solution for high-throughput and rapid measurement of seedling phenotypes in oilseed rape.

Why it matches plant phenotyping methods3D点群再構成・セグメンテーション・特徴抽出法を開発し、油糧ナタネの葉形態形質を手測定と検証しており、フェノタイピング手法が中心である。

abstractWe propose an oilseed rape segmentation and phenotyping measurement method based on 3D Gaussian splatting with improved PointNet++.

Rapid diagnosis of herbicidal activity and mode of action using spectral image analysis and machine learning →

Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape ( Brassica napus ), and analyzed using MATLAB 2021b to quantify NDI, ExG, F d /F m , and plant leaf temperature. NDI, ExG and F d /F m decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in F d /F m , while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 ​h enabled the diagnosis of herbicide MOAs with 89.6 ​% accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 ​%. The indices acquired at 6 ​h, and F d /F m and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 ​%, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.

Why it matches plant phenotyping methods植物への除草剤処理を目的とするが、スペクトル画像から葉温度や蛍光などの植物状態を抽出し、機械学習で作用機序を診断する画像解析手法が中心であるため、植物フェノタイピング手法として含める。

abstractTherefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).

A meta-analysis of crop leaf nitrogen, phosphorus and potassium content estimation based on hyperspectral and multispectral remote sensing techniques →

Real-time monitoring of essential nutrient status is crucial for improving fertilizer efficiency and enhancing crop productivity. Hyperspectral and multispectral remote sensing provide effective, non-invasive tools for estimating crop leaf nitrogen, phosphorus, and potassium content (LNC, LPC, and LKC). Therefore, a comprehensive evaluation of these technologies is needed. We conducted a meta-analysis of studies from 2000 to 2023 to identify spectral bands for estimating LNC, LPC, and LKC. Subsequently, nutrient estimation models using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR) were developed based on 4 years of oilseed rape field data, to verify identified the sensitive bands. The meta-analysis revealed an increasing research focus on nutrient estimation from 2017 to 2023, with wheat and rice as the primary crops investigated. Among the three nutrients, LNC was the most frequently analyzed. Commonly adopted modeling approaches included PLSR, Artificial Neural Networks (ANN), SVR, and RF. At the canopy level, LNC exhibited its most sensitive bands within 550–2030 nm, while at the leaf level, the sensitive range was 400–780 nm. LPC was responsive in 517–995 nm and 2030–2269 nm at the canopy level, while responsive in 545–995 nm and around 2166 nm at the leaf level. The bands sensitive to LKC were observed in 519–976 nm and 1513–2058 nm at the canopy level, and 545–995 nm at the leaf level. The RF model consistently achieved the highest prediction accuracy among models based on the identified sensitive bands. At the canopy level, LNC was estimated with the highest accuracy (R²=0.81, RMSE=0.39 %), followed by LPC (R²=0.75, RMSE=0.09 %) and LKC (R²=0.70, RMSE=0.34 %). At the leaf level, LNC again showed the best performance (R²=0.82, RMSE=0.37 %), followed by LKC (R²=0.74, RMSE=0.30 %) outperforming LPC (R²=0.66, RMSE=0.09 %). This study provides a comprehensive evaluation of hyperspectral and multispectral technologies for crop nutrient estimation. The sensitive spectral bands and modeling approaches identified through meta-analysis enable accurate estimation of LNC, LPC, and LKC.

Why it matches plant phenotyping methods作物葉の窒素・リン・カリウム含量という植物形質を、ハイパースペクトル／マルチスペクトル計測と回帰モデルで推定し、メタ分析と実測データによる検証を行っており、フェノタイピング手法が中心である。

titleA meta-analysis of crop leaf nitrogen, phosphorus and potassium content estimation based on hyperspectral and multispectral remote sensing techniques

Rapid quantification of whole seed fatty acid amount, composition, and shape phenotypes from diverse oilseed species with large differences in seed size. →

Background Seed oils are widely used in the food, biofuel, and industrial feedstock industries, with their utility and value determined by total oil content and fatty acid composition. Current high throughput seed oil analysis methods either lack accuracy in total fatty acid profiling or require extensive labor for lipid extraction prior to derivatization to fatty acid methyl esters (FAME) and quantification by gas chromatography (GC). Alternatively, direct whole seed FAME production methods have been developed for the very small seeds in the model species Arabidopsis thaliana but these have generally not been adapted to larger seeds of most oilseed crops. Results High-throughput direct whole seed FAME production methods were optimized for seeds up to 5 mg each utilizing acid-catalyzed esterification. For the oilseed species Camelina sativa, Thlaspi avernse (pennycress), Cuphea viscosissima, and Brassica napus (var. Canola), the total seed fatty acid content and composition from direct seed esterification to FAME matched that of lipid extract derivatization demonstrating the accuracy of the methods. In combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis. For the larger and tougher seeds produced by Limnanthes alba (Meadowfoam) and Cannabis sativa L. (hemp) the whole seed acid-based method proved insufficient, and prior laborious homogenization of seeds was required. Therefore, a rapid one-tube bead homogenization and base catalyzed-esterification method was developed. Base-derived fatty acid esterification cannot derivatize free fatty acids leading to slightly lower total seed fatty acid than acid-catalyzed methods, however the seed oil content and fatty acid composition that is valuable for screening large numbers of samples in research populations was accurately measured. Conclusions New rapid whole seed fatty acid esterification and phenotyping protocols were developed to accurately assess oilseed lipid content. These methods are particularly valuable in oilseed research, breeding, and engineering applications where efficient analysis of large numbers of samples and accurate oil fatty acid profiling is essential. While having been developed for current and emerging oilseed crops, these methods also provide a foundation from which protocols might be established for new and emerging crop species.

Why it matches plant phenotyping methods全粒FAME分析とGridFreeによる種子形状・サイズ・重量・個数の表現型取得を統合した高スループット手法を開発しており、表現型取得ワークフローが研究の中心である。

abstractIn combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis.

Detection of composite heavy metal content in rape leaf using feature clustering and hyperspectral imaging technology. →

Exploring composite heavy metal content in rape is significant for crop growth and human health. The focus of this paper was to assess the viability of detection of composite heavy metal content in rape leaf utilizing hyperspectral imaging technology (HSI). Furthermore, a hybrid feature selection based on feature clustering and symmetric uncertainty (HFCSU) was proposed for spectral data to reduce dimensionality. Firstly, hyperspectral images of rape leaf stressed by different composite heavy metal concentrations were collected. Then, the spectral data in the wavelength range of 480-1000 nm was extracted. Subsequently, the spectral data was preprocessed utilizing Savitzky-Golay (SG) smoothing, standard normalized variable (SNV) and its combination (SG-SNV). Competitive adaptive reweighted sampling (CARS), random frog (RF), genetic algorithm-partial least squares (GA-PLS) and HFCSU were utilized for feature selection. Ultimately, the support vector machine regression (SVR) was utilized to build predictive models of Cd and Pb content. The results demonstrated that the SVR model using HFCSU provided the optimal prediction performance, the R P 2 , RMSEP and RPD for prediction of Cd content were 0.9392, 0.1494 mg·kg -1 and 3.915, respectively, and the R P 2 , RMSEP and RPD for prediction of Pb content were 0.9442, 0.1806 mg·kg -1 and 4.702, respectively. The results indicated that HFCSU can effectively mine features relevant to heavy metals, and HFCSU combined with HSI has a greater potential in the determination of composite heavy metal content in rape leaves.

Why it matches plant phenotyping methods油菜葉の重金属含量をハイパースペクトル画像と特徴選択・回帰モデルで推定する測定手法を開発・評価しており、植物状態の取得が中心的です。

abstracta hybrid feature selection based on feature clustering and symmetric uncertainty (HFCSU) was proposed for spectral data to reduce dimensionality.

Growth monitoring of rapeseed seedlings in multiple growth stages based on low-altitude remote sensing and semantic segmentation →

Rapeseed seedling growth monitoring indicates growth status and detects problems, such as seedling gaps, seedbed unevenness, and diseases or insect pests in time, which play an important role in improving sowing strategies, promoting the decision-making of fertilizer prescription, and increasing economic efficiency. To improve the accuracy of rapeseed seedling growth assessment, a multi-growth stage growth assessment method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and semantic segmentation was proposed to assess the growth of rapeseed into excellent, average, and poor growth. First, to address the problem of complex field scenes and densely planted rapeseed leading to difficult segmentation of rapeseed seedlings and field drains, the original Deeplabv3+ model was improved by selecting the lightweight network MobileNetV2 as the backbone feature extraction network and fusing the coordinate attention（CA）module, which enables the model to better noise removal and feature extraction and improves the model’s accuracy and robustness. Then, a field drain optimal centerline algorithm is proposed to obtain the optimal centerline of all field drain in the image and determine the field box position. Finally, eight growth-related feature values for rapeseed seedling were constructed, and were used as feature vectors in a random forest (RF) to construct multi-growth stage growth assessment model for rapeseed seedlings. The results indicate that the improved DeeplabV3+ network outperformed the original DeeplabV3+ network, with the mean pixel accuracy increasing from 78.43 % to 87.47 % (an improvement of 9.04 %) and the average intersection over union (mIoU) increasing from 67.45 % to 76.89 % (an improvement of 9.44 %). The mean positional deviation of the centerline was –5.29 pixels with a standard deviation of 9.51 and a mean angular deviation of –0.01848 rad with a standard deviation of 0.00791, which can effectively detect the centerline of the field drain. The precision, sensitivity, specificity, and accuracy of the proposed method were 96.35 %, 96.34 %, 97.20 %, and 96.34 %, respectively. The algorithm of this study can efficiently segment rapeseed seedlings and field drains, obtain the optimal centerline of the field drain, and be used for rapeseed seedling multi-growth stage growth monitoring, which provides a theoretical basis and technical reference for rapeseed seedling multi-growth stage growth monitoring.

Why it matches plant phenotyping methodsUAV画像とセマンティックセグメンテーションを用いて、菜種苗の生育関連形質を抽出・評価する手法を開発し、精度検証まで行っており、フェノタイピング手法が中心である。

abstracta multi-growth stage growth assessment method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and semantic segmentation was proposed

DM_CorrMatch: a semi-supervised semantic segmentation framework for rapeseed flower coverage estimation using UAV imagery. →

Rapeseed (Brassica napus L.) inflorescence coverage is a crucial phenotypic parameter for assessing crop growth and estimating yield. Accurate crop cover assessment is typically performed using Unmanned Aerial Vehicles (UAVs) in combination with semantic segmentation methods. However, the irregular and variable morphology of rapeseed inflorescences presents significant challenges in segmentation. To address these challenges, advanced methods that can improve segmentation accuracy, particularly under limited data conditions, are needed. In this study, we propose a cost-effective and high-throughput approach using a semi-supervised learning framework, DM_CorrMatch. This method enhances input images through strong and weak data augmentation techniques, while leveraging the Denoising Diffusion Probabilistic Model (DDPM) to generate additional samples in data-scarce scenarios. We propose an automatic update strategy for labeled data to dilute the proportion of erroneous labels in manual segmentation. Furthermore, a novel network architecture, Mamba-Deeplabv3+, is proposed, combining the strengths of Mamba and Convolutional Neural Networks (CNNs) for both global and local feature extraction. This architecture effectively captures key inflorescence features, even under varying poses, while reducing the influence of complex backgrounds. The proposed method is validated on the Rapeseed Flower Segmentation Dataset (RFSD), which consists of 720 UAV images from the Yangluo experimental station of the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences (CAAS). The experimental results showed that our method outperforms four traditional segmentation methods and eleven deep learning methods, achieving an Intersection over Union (IoU) of 0.886, Precision of 0.942, and Recall of 0.940. The proposed semi-supervised learning-based method, combined with the Mamba-Deeplabv3+ architecture, demonstrates superior performance in accurately segmenting rapeseed inflorescences under challenging conditions. Our approach effectively handles complex backgrounds and various poses of inflorescences, providing a reliable tool for rapeseed flower cover estimation. This method can aid in the development of high-yield cultivars and improve crop monitoring through UAV-based technologies.

Why it matches plant phenotyping methodsUAV画像からナタネ花序被覆率という植物表現型を推定する半教師ありセグメンテーション手法を開発し、データセット上で既存手法と比較検証しているため、方法が中心的である。

abstractwe propose a cost-effective and high-throughput approach using a semi-supervised learning framework, DM_CorrMatch.

Winter Oilseed Rape LAI Inversion via Multi-Source UAV Fusion: A Three-Dimensional Texture and Machine Learning Approach. →

Leaf area index (LAI) serves as a critical indicator for evaluating crop growth and guiding field management practices. While spectral information (vegetation indices and texture features) extracted from multispectral sensors mounted on unmanned aerial vehicles (UAVs) holds promise for LAI estimation, the limitations of single-texture features necessitate further exploration. Therefore, this study conducted field experiments over two consecutive years (2021-2022) to collect winter oilseed rape LAI ground truth data and corresponding UAV multispectral imagery. Vegetation indices were constructed, and canopy texture features were extracted. Subsequently, a correlation matrix method was employed to establish novel randomized combinations of three-dimensional texture indices. By analyzing the correlations between these parameters and winter oilseed rape LAI, variables with significant correlations ( p p p 2 ) of 0.882, a root mean square error (RMSE) of 0.204 cm 2 cm -2 , and a mean relative error (MRE) of 6.498%. This study provides an effective methodology for UAV-based multispectral monitoring of winter oilseed rape LAI and offers scientific and technical support for precision agriculture management practices.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像とテクスチャ特徴量、機械学習を用いて作物のLAIを推定する手法を開発・評価しており、植物形質取得が研究の中心である。

abstractTherefore, this study conducted field experiments over two consecutive years (2021-2022) to collect winter oilseed rape LAI ground truth data and corresponding UAV multispectral imagery.

DM_CorrMatch: A Semi-Supervised Semantic Segmentation Framework for Rapeseed Flower Coverage Estimation Using UAV Imagery →

Abstract Background Rapeseed( Brassica napus L. ) inflorescence coverage is a crucial phenotypic parameter for assessing crop growth and estimating yield. Accurate crop cover assessment is typically performed using Unmanned Aerial Vehicles (UAVs) in combination with semantic segmentation methods. However, the irregular and variable morphology of rapeseed inflorescences presents significant challenges in segmentation. To address these challenges, advanced methods that can improve segmentation accuracy, particularly under limited data conditions, are needed. Results In this study, we propose a cost-effective and high-throughput approach using a semi-supervised learning framework, DM_CorrMatch. This method enhances input images through strong and weak data augmentation techniques, while leveraging the Denoising Diffusion Probabilistic Model (DDPM) to generate additional samples in data-scarce scenarios.We propose an automatic update strategy for labeled data to dilute the proportion of erroneous labels in manual segmentation. Furthermore, a novel network architecture, Mamba-Deeplabv3+, is proposed, combining the strengths of Mamba and Convolutional Neural Networks (CNNs) for both global and local feature extraction. This architecture effectively captures key inflorescence features, even under varying poses, while reducing the influence of complex backgrounds. The proposed method is validated on the Rapeseed Flower Segmentation Dataset (RFSD), which consists of 720 UAV images from the Yangluo experimental station of the Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences (CAAS). The experimental results showed that our method outperforms four traditional segmentation methods and eleven deep learning methods, achieving an Intersection over Union (IoU) of 0.886, Precision of 0.942, and Recall of 0.940. Conclusions The proposed semi-supervised learning-based method, combined with the Mamba-Deeplabv3+ architecture, demonstrates superior performance in accurately segmenting rapeseed inflorescences under challenging conditions. Our approach effectively handles complex backgrounds and various poses of inflorescences, providing a reliable tool for rapeseed flower cover estimation. This method can aid in the development of high-yield cultivars and improve crop monitoring through UAV-based technologies.

Why it matches plant phenotyping methodsUAV画像からナタネ花序被覆率という植物形質を推定する半教師ありセグメンテーション手法を開発し、データセット上で既存手法と比較検証しているため、方法が中心である。

abstractwe propose a cost-effective and high-throughput approach using a semi-supervised learning framework, DM_CorrMatch.

Estimating photosynthetic characteristics of forage rape by fusing the sensitive spectral bands to combined stresses of nitrogen and salt. →

Leaf gas exchange and chlorophyll fluorescence parameters (PGE-CFPs), which respond significantly and quickly to environmental stresses, have been used to assess the early responses of crop physiology to stresses. Most spectral estimations only focus on crop photosynthetic characteristics under a single environmental stress. Thus, the methods proposed previously are not suitable for the estimations under combined stresses (i.e., nitrogen and salt). In this research, the leaf spectral features of forage rape ( Brassica napus L.) under nitrogen stress (NSpe) and salt stress (SSpe) were fused to increase the accuracy of the spectral estimation of photosynthetic characteristics of forage rape under combined stresses in arid region of Xinjiang, China. The results showed that PGE-CFPs' spectral features were extracted with SPA (successive projections algorithm) after preprocessing. Among the SSpe- and NSpe-based models, the RF (random forest) models had higher estimation accuracy than the PLSR (partial least squares regression) and BPNN (backpropagation neural network) models. Specifically, the RF models had a PGE-CFPs estimation accuracy of 0.597-0.712, 0.640-0.715, and 0.377-0.461 under nitrogen stress (NS), salt stress (SS), and NS*SS, respectively. After fusing NSpe and SSpe, the accuracy in estimating PGE-CFPs of forage rape under NS, SS, and NS*SS were 0.729-0.755, 0.667-0.768, and 0.621-0.689, respectively. Then, the constructed models were further validated using field data, and the accuracy obtained was in the range of 0.585-0.711. Therefore, the feature fusion modeling method proposed has strong transferability and applicability. This research will offer a technical reference for crop photosynthesis monitoring at the early stage of environmental stresses.

Why it matches plant phenotyping methods分光特徴量の融合と機械学習により、複合ストレス下の植物光合成特性を推定する手法を開発・検証しており、表現型取得・推定が研究の中心である。

abstractthe methods proposed previously are not suitable for the estimations under combined stresses (i.e., nitrogen and salt).

Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging. →

In this paper, a transfer stack denoising autoencoder (T-SDAE) algorithm is proposed to implement the migration of cadmium (Cd) prediction depth characteristic model of oilseed rape leaves in different silicon environments. Stacked denoising autoencoder (SDAE) algorithm was used to reduce dimensionality, and the most effective SDAE deep learning network was transferred to create the T-SDAE model. The results showed that SVR model using SDAE to extract depth features had the best prediction effect on Cd content in silicon-free, low-silicon and higher-silicon environments. Moreover, the coefficient of determination of prediction set (R p 2 ) were 0.9127, 0.9829 and 0.9606, respectively. Specifically, the R p 2 value of the T-SDAE-SVR optimal prediction set under different silicon environments is 0.9273, RMSEP is 0.01465 mg/kg, and RPD is 3.237. By integrating hyperspectral imaging technology with a deep transfer learning algorithm, accurate detection of various Cd contents in oilseed rape leaves is feasible under different silicon environments.

Why it matches plant phenotyping methods油糧菜葉のCd含量という植物形質を、Vis-NIRハイパースペクトル画像と深層転移学習で非破壊推定する手法の開発・検証が中心である。

titleNondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging.

Novel encoding technique to evolve convolutional neural network as a multi-criteria problem for plant image segmentation →

Despite the success of deep convolutional neural networks (DCNNs) in various applications, optimizing them for specific tasks remains challenging due to the complex manual tuning of hyperparameters. This approach is often ineffective when balancing multiple objectives, as it relies heavily on trial and error. This study proposes an innovative multi-objective genetic algorithm (MOGA) approach to automatically learn highly efficient and resource-saving DCNN architectures, in short, termed as MOGA-DCNN, for a given plant image segmentation task. To this end, a novel encoding technique was proposed to simplify the structure of the candidate solutions and constrain the search space in such a way that a Pareto set of non-dominated solutions can be explored efficiently through genetic operators, reducing computational complexity, and improving performance. We have evaluated this approach on different datasets collected from fruit trees and oilseed rape crops under controlled and uncontrolled conditions. The results demonstrated the capability of the proposed MOGA-DCNN to automatically construct variable-length DCNN architectures for each dataset. The storage size of the architecture parameters (71 K) was only 0.24 % of the well-known SegNet. The evolved model classifies an image 15 – 18 times faster than DeepLab v3+, indicating an overwhelming advantage in image segmentation. These results suggest the prospect of model transferability to different image segmentation tasks, and it could be integrated into embedded system devices with an extremely low computational cost.

Why it matches plant phenotyping methods植物画像セグメンテーションのためのCNNアーキテクチャを遺伝的アルゴリズムで開発・評価しており、植物表現型取得の中核手法である。

abstractThis study proposes an innovative multi-objective genetic algorithm (MOGA) approach to automatically learn highly efficient and resource-saving DCNN architectures, in short, termed as MOGA-DCNN, for a given plant image segmentation task.

Assessment of red-edge based vegetation indices for crop yield prediction at the field scale across large regions in Australia →

Vegetation indices have long been used to monitor vegetation using spectral information. The red-edge (RE) bands have gained attention for improved yield prediction capabilities over traditional red/near-infrared-based indices. This study introduces the triple red-edge index (TREI), a novel vegetation index that leverages the three RE bands provided by the Sentinel-2 satellite. It aims to enhance the accuracy of crop yield predictions. The TREI exploits changes in the transition slope between the red slope, influenced by photosynthesis, and the near-infrared (NIR) slope, affected by cell structure and leaf layers. It was evaluated against indices utilising none, one, two, or three RE bands for yield prediction efficacy. The study also incorporates a simple model combining weather and remote sensing data to predict crop yields, testing the approach across 168 canola and 123 wheat fields. The validation results demonstrated that the TREI significantly improves crop yield predictions by incorporating all three RE bands and effectively describing the RE region. The TREI yielded the highest concordance correlation coefficient (CCC) values for both canola (CCC = 0.89) and wheat (CCC = 0.85) crops, indicating their effectiveness in crop yield prediction. The study concludes that the TREI index outperforms existing vegetation indices in predicting crop yield due to using the Sentinel-2 three RE bands. The highest CCC values corresponded to using the TREI index in the crop yield prediction with a CCC = 0.89 (canola) and CCC = 0.85 (wheat) according to the validation results.

Why it matches plant phenotyping methodsSentinel-2のレッドエッジ情報を用いた新規植生指数TREIを開発し、作物収量という植物形質の予測性能を複数圃場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 introduces the triple red-edge index (TREI), a novel vegetation index that leverages the three RE bands provided by the Sentinel-2 satellite.

Lightweight deep learning model for embedded systems efficiently predicts oil and protein content in rapeseed. →

Conventional methods for determining protein and oil content in rapeseed are often time-consuming, labor-intensive, and costly. In this study, a mobile application was developed using an optimized deep learning method for low-cost, non-destructive and real-time prediction of protein and oil content in rapeseed by inputting rapeseed images. Among the tested models, FasterNet-L showed the optimal performance, with predicted coefficients of determination (R p 2 ) of 0.9366 for oil content and 0.8828 for protein content. The mean square error of prediction (RMSEP) was 0.6982 and 0.6498, and the residual predictive deviation (RPD) was 3.88 and 2.92 for oil and protein content, respectively. Furthermore, three pruning methods were employed, and neural pruning via growth regularization proved to be the most effective, with a 13.18 % improvement in prediction speed and a 15.79 % reduction in model size. Finally, this method can be expanded and applied to other oilseed crops for rapid quality identification and detection.

Why it matches plant phenotyping methodsラペシード画像から油分・タンパク質含量を推定する深層学習モデルとモバイルアプリを開発し、性能評価とモデル圧縮も行っており、植物種子形質の取得・推定手法が中心である。

abstracta mobile application was developed using an optimized deep learning method for low-cost, non-destructive and real-time prediction of protein and oil content in rapeseed by inputting rapeseed images.

Stem-Leaf Segmentation and Morphological Traits Extraction in Rapeseed Seedlings Using a Three-Dimensional Point Cloud →

Developing accurate, non-destructive, and automated methods for monitoring the phenotypic traits of rapeseed is crucial for improving yield and quality in modern agriculture. We used a line laser binocular stereo vision technology system to obtain the three-dimensional (3D) point cloud data of different rapeseed varieties (namely Qinyou 7, Zheyouza 108, and Huyou 039) at the seedling stage, and the phenotypic traits of rapeseed were extracted from those point clouds. After pre-processing the rapeseed point clouds with denoising and segmentation, the plant height, leaf length, leaf width, and leaf area of the rapeseed in the seedling stage were extracted by a series of algorithms and were evaluated for accuracy with the manually measured values. The following results were obtained: the R2 values for plant height data between the extracted values of the 3D point cloud and the manually measured values reached 0.934, and the RMSE was 0.351 cm. Similarly, the R2 values for leaf length of the three kinds of rapeseed were all greater than 0.95, and the RMSEs for Qinyou 7, Zheyouza 108, and Huyou 039 were 0.134 cm, 0.131 cm, and 0.139 cm, respectively. Regarding leaf width, R2 was greater than 0.92, and the RMSEs were 0.151 cm, 0.189 cm, and 0.150 cm, respectively. Further, the R2 values for leaf area were all greater than 0.98 with RMSEs of 0.296 cm2, 0.231 cm2 and 0.259 cm2, respectively. The results extracted from the 3D point cloud are reliable and have high accuracy. These results demonstrate the potential of 3D point cloud technology for automated, non-destructive phenotypic analysis in rapeseed breeding programs, which can accelerate the development of improved varieties.

Why it matches plant phenotyping methods3D点群と分割・抽出アルゴリズムを用いて rapeseed の形態形質を自動推定し、手測定値で精度検証しており、フェノタイピング手法が研究の中心である。

abstractWe used a line laser binocular stereo vision technology system to obtain the three-dimensional (3D) point cloud data of different rapeseed varieties

Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point Clouds →

Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cultivation and breeding, yet traditional two-dimensional imaging is susceptible to reduced segmentation accuracy due to occlusions between plants. The current study proposes the use of binocular stereo-vision technology to obtain three-dimensional (3D) point clouds of rapeseed leaves at the seedling and bolting stages. The point clouds were colorized based on elevation values in order to better process the 3D point cloud data and extract rapeseed phenotypic parameters. Denoising methods were selected based on the source and classification of point cloud noise. However, for ground point clouds, we combined plane fitting with pass-through filtering for denoising, while statistical filtering was used for denoising outliers generated during scanning. We found that, during the seedling stage of rapeseed, a region-growing segmentation method was helpful in finding suitable parameter thresholds for leaf segmentation, and the Locally Convex Connected Patches (LCCP) clustering method was used for leaf segmentation at the bolting stage. Furthermore, the study results show that combining plane fitting with pass-through filtering effectively removes the ground point cloud noise, while statistical filtering successfully denoises outlier noise points generated during scanning. Finally, using the region-growing algorithm during the seedling stage with a normal angle threshold set at 5.0/180.0* M_PI and a curvature threshold set at 1.5 helps to avoid the under-segmentation and over-segmentation issues, achieving complete segmentation of rapeseed seedling leaves, while the LCCP clustering method fully segments rapeseed leaves at the bolting stage. The proposed method provides insights to improve the accuracy of subsequent point cloud phenotypic parameter extraction, such as rapeseed leaf area, and is beneficial for the 3D reconstruction of rapeseed.

Why it matches plant phenotyping methods二眼ステレオビジョンによる3D点群取得、ノイズ除去、葉セグメンテーションを開発・評価し、葉面積などの表現型形質抽出を目的とするため、植物フェノタイピング手法が研究の中心である。

abstractPoint cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping.

Classical and machine learning tools for identifying yellow-seeded Brassica napus by fusion of hyperspectral features. →

Introduction Due to its favorable traits-such as lower lignin content, higher oil concentration, and increased protein levels-the genetic improvement of yellow-seeded rapeseed has attracted more attention than other rapeseed color variations. Traditionally, yellow-seeded rapeseed has been identified visually, but the complex variability in the seed coat color of Brassica napus has made manual identification challenging and often inaccurate. Another method, using the RGB color system, is frequently employed but is sensitive to photographic conditions, including lighting and camera settings. Methods We present four data-driven models to identify yellow-seeded B. napus using hyperspectral features combined with simple yet intelligent techniques. One model employs partial least squares regression (PLSR) to predict the R, G, and B color channels, effectively distinguishing yellow-seeded varieties from others according to globally accepted yellow-seed classification protocols. Another model uses logistic regression (Logit-R) to produce a probability-based assessment of yellow-seeded status. Additionally, we implement two intelligent models, random forest and support vector classifier to evaluate features selected through lasso-penalized logistic regression. Results and discussion Our findings indicate significant recognition accuracies of 96.55% and 98% for the PLSR and Logit-R models, respectively, aligning closely with the accuracy of previous methods. This approach represents a meaningful advancement in identifying yellow-seeded rapeseed, with high recognition accuracy demonstrating the practical applicability of these models.

Why it matches plant phenotyping methodsヒ​​パースペクトル特徴量と複数の機械学習モデルにより、B. napus種子の黄色種子形質を識別する方法が中心であり、認識精度も評価しているため。

abstractWe present four data-driven models to identify yellow-seeded B. napus using hyperspectral features combined with simple yet intelligent techniques.

Mapping rapeseed ( Brassica napus L. ) aboveground biomass in different periods using optical and phenotypic metrics derived from UAV hyperspectral and RGB imagery. →

Aboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in monitoring crop biomass. However, the saturation effect makes it challenging for spectral indices to accurately reflect crop changes at higher biomass levels. It is well established that rapeseed biomass during different growth stages is closely related to phenotypic traits. This study aims to explore the potential of using optical and phenotypic metrics to estimate rapeseed AGB. Vegetation indices (VI), texture features (TF), and structural features (SF) were extracted from UAV hyperspectral and ultra-high-resolution RGB images to assess their correlation with rapeseed biomass at different growth stages. Deep neural network (DNN), random forest (RF), and support vector regression (SVR) were employed to estimate rapeseed AGB. We compared the accuracy of various feature combinations and evaluated model performance at different growth stages. The results indicated strong correlations between rapeseed AGB at the three growth stages and the corresponding indices. The estimation model incorporating VI, TF, and SF showed higher accuracy in estimating rapeseed AGB compared to models using individual feature sets. Furthermore, the DNN model (R 2 = 0.878, RMSE = 447.02 kg/ha) with the combined features outperformed both the RF (R 2 = 0.812, RMSE = 530.15 kg/ha) and SVR (R 2 = 0.781, RMSE = 563.24 kg/ha) models. Among the growth stages, the bolting stage yielded slightly higher estimation accuracy than the seedling and early blossoming stages. The optimal model combined DNN with VI, TF, and SF features. These findings demonstrate that integrating hyperspectral and RGB data with advanced artificial intelligence models, particularly DNN, provides an effective approach for estimating rapeseed AGB.

Why it matches plant phenotyping methodsUAVのハイパースペクトル・RGB画像から構造的および光学的特徴を抽出し、成長段階別のナタネ地上部バイオマスを推定する画像解析・計算手法が研究の中心であるため。

abstractThis study aims to explore the potential of using optical and phenotypic metrics to estimate rapeseed AGB.

Country-Scale Crop-Specific Phenology from Disaggregated PROBA-V →

Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail for smaller plots. This study generated crop-specific phenomaps for mainland France (2016–2020) using PROBA-V data. A spatial disaggregation method reconstructed NDVI time series for individual crops within mixed pixels. Then, phenometrics were extracted from disaggregated PROBA-V and Sentinel-2 separately and compared to observed phenological stages. Results showed that PROBA-V-based phenomaps closely matched observations at regional level, with moderate accuracy at municipal level. PROBA-V demonstrated a higher detection rate than Sentinel-2, especially in cloudy periods, and successfully generated phenomaps before Sentinel-2B’s launch. The study highlights PROBA-V’s potential for operational crop monitoring, i.e., wheat heading and oilseed rape flowering, with performance comparable to Sentinel-2. PROBA-V outputs complement Sentinel-2: phenometrics cannot be generated at plot level but are efficiently produced at regional or national scales to study phenological gradients more easily than with Sentinel-2 and with similar accuracy. This approach could be extended to MODIS or SPOT-VGT, to generate historical phenological data, providing that a crop map is available.

Why it matches plant phenotyping methods作物の生育ステージを推定するリモートセンシング手法を開発・適用し、観測値との比較およびSentinel-2との性能評価を行っているため、植物フェノタイピング手法が中心である。

abstractA spatial disaggregation method reconstructed NDVI time series for individual crops within mixed pixels.

Early detection of Sclerotinia sclerotiorum on oilseed rape leaves based on optical properties →

Oilseed rape (Brassica napus L.) is susceptible to Sclerotinia sclerotiorum and its yield would reduce dramatically by the sclerotinia disease. Early detection of the pathogen to prevent the spread of sclerotinia is of great significance. In this paper, the optical response of oilseed rape leaves to Sclerotinia sclerotiorum was analyzed and a new method for early detection of sclerotinia disease based on optical properties was proposed. The optical absorption (μa) and reduced scattering (μs′) coefficients of healthy and infected (invisible-symptom and visible-symptom) oilseed rape leaves were measured by using a single integrating sphere (SIS) system over 500–1000 nm. Results showed that with the development of Sclerotinia sclerotiorum infection from healthy to visible-symptom leaves, μa decreased in 600–700 nm that contained obvious absorption peaks by pigments, while μs′ increased in 500–1000 nm. Then linear discriminant analysis (LDA) and support vector machines (SVM) models were developed to discriminate infected leaves from healthy ones, with the raw data of μa in 500–700 nm, μs′ in 500–700 nm, and (μa-μs′) in 600–700 nm, as well as corresponding effective wavelengths optimally selected by the successive projections algorithm (SPA). Results showed that the LDA models with the raw and SPA-selected data of μa in 500–700 nm, and the SPA-selected (μa-μs′) data, and the SVM model with SPA-selected μa data all provided 100.00% classification accuracy. Overall, this study proved that the optical properties of oilseed rape responded to Sclerotinia sclerotiorum at early stage, and could be a new basis for early detection of sclerotinia disease.

Why it matches plant phenotyping methods光学特性と機械学習を用いて油糠菜葉の感染状態・病害を早期検出する手法を開発しており、植物の病徴状態の取得・判別が研究の中心である。

abstracta new method for early detection of sclerotinia disease based on optical properties was proposed

U3-YOLOXs: An improved YOLOXs for Uncommon Unregular Unbalance detection of the rape subhealth regions →

Agricultural production in high latitudes could be limited by cold climate. Plant factory allows continuous production all year round, where the detection of plant growth is one of the most important tasks. To achieve non-destructive detection of rape in our plant factory, a feasible approach is to automatically detect subhealth areas from the rape images. However, this task faces the following challenges: (1) Uncommon problem: the subhealth regions on rape are the domain-specific objects, so the mainstream transfer learning-based detector is unreliable; (2) Unregular problem: the subhealth regions are difficult to detect due to their variable shapes, sizes and colors. (3) Unbalance problem: localization and classification of subhealth regions each have low-/high-quality bounding box unbalance and easy/hard sample unbalance. In this paper, a novel deep object detector based on the YOLOXs, called U³YOLOXs, is proposed for the detection of subhealth regions on rape at the bolting stage. Specifically, a domain-specific self-supervised pre-training strategy in the backbone is developed for the Uncommon problem; next, a coordinate attention mechanism in the multi-scale neck network is built for the Unregular problem; finally, the focal EIoU and the focal loss in the decoupled head are designed for the Unbalance problem. The experimental results show that the mAP of our U³YOLOXs is 94.38 % with a latency of 20.4 ms per image, which achieves an optimal accuracy-speed tradeoff. Compared to the YOLOXs it achieves a significant improvement of 9.27 % on mAP at the cost of only 3.55 % increase in latency. Experimental analysis further shows the effectiveness of each improvement, and the reliability of porting U³YOLOXs to edge devices for agricultural production.

Why it matches plant phenotyping methodsアブラナ画像から生育不良（subhealth）領域を非破壊検出する深層学習手法を開発し、精度・速度と各改良要素を評価しており、植物状態の取得手法が中心である。

abstractTo achieve non-destructive detection of rape in our plant factory, a feasible approach is to automatically detect subhealth areas from the rape images.

Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model →

Biomass estimation of oilseed rape is crucial for optimizing crop productivity and breeding strategies. While UAV-based imaging has advanced high-throughput phenotyping, current methods often rely on orthophoto images, which struggle with overlapping leaves and incomplete structural information in complex field environments. This study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape. UAV multi-view oblique images from 36 angles were used to perform 3D reconstruction, with the SAM module enhancing point cloud segmentation. The segmented point clouds were then converted into point cloud volumes, which were fitted to ground-measured biomass using linear regression. The results showed that 3DGS (7k and 30k iterations) provided high accuracy, with peak signal-to-noise ratios (PSNR) of 27.43 and 29.53 and training times of 7 and 49 minutes, respectively. This performance exceeded that of structure from motion (SfM) and mipmap Neural Radiance Fields (Mip-NeRF), demonstrating superior efficiency. The SAM module achieved high segmentation accuracy, with a mean intersection over union (mIoU) of 0.961 and an F1-score of 0.980. Additionally, a comparison of biomass extraction models found the point cloud volume model to be the most accurate, with an determination coefficient (R2) of 0.976, root mean square error (RMSE) of 2.92 g/plant, and mean absolute percentage error (MAPE) of 6.81%, outperforming both the plot crop volume and individual crop volume models. This study highlights the potential of combining 3DGS with multi-view UAV imaging for improved biomass phenotyping.

Why it matches plant phenotyping methodsUAV多視点画像、3D再構成、SAMによる分割を統合し、ナタネのバイオマスを推定・検証する手法が研究の中心である。

abstractThis study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape.

Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape →

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: Rpre = 0.8143, RMSEP = 171.9 kg/hm2) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: Rpre = 0.7655, RMSEP = 188.3 kg/hm2) from a multispectral camera. The best models for yield prediction were found by applying BPNN (Rpre = 0.8114, RMSEP = 172.6 kg/hm2) built from optimal EWs and ELM (Rpre = 0.8118, RMSEP = 170.9 kg/hm2) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

Why it matches plant phenotyping methodsUAVマルチセンサー画像とデータ融合・機械学習による作物収量推定を技術的に比較評価し、高スループット表現型解析への応用を中心に扱っているため。

abstractWe explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras.

Optimization of a rapid, sensitive, and high throughput molecular sensor to measure canola protoplast respiratory metabolism as a means of screening nanomaterial cytotoxicity. →

Nanomaterial-mediated plant genetic engineering holds promise for developing new crop cultivars but can be hindered by nanomaterial toxicity to protoplasts. We present a fast, high-throughput method for assessing protoplast viability using resazurin, a non-toxic dye converted to highly fluorescent resorufin during respiration. Protoplasts isolated from hypocotyl canola (Brassica napus L.) were evaluated at varying temperatures (4, 10, 20, 30 ˚C) and time intervals (1-24 h). Optimal conditions for detecting protoplast viability were identified as 20,000 cells incubated with 40 µM resazurin at room temperature for 3 h. The assay was applied to evaluate the cytotoxicity of silver nanospheres, silica nanospheres, cholesteryl-butyrate nanoemulsion, and lipid nanoparticles. The cholesteryl-butyrate nanoemulsion and lipid nanoparticles exhibited toxicity across all tested concentrations (5-500 ng/ml), except at 5 ng/ml. Silver nanospheres were toxic across all tested concentrations (5-500 ng/ml) and sizes (20-100 nm), except for the larger size (100 nm) at 5 ng/ml. Silica nanospheres showed no toxicity at 5 ng/ml across all tested sizes (12-230 nm). Our results highlight that nanoparticle size and concentration significantly impact protoplast toxicity. Overall, the results showed that the resazurin assay is a precise, rapid, and scalable tool for screening nanomaterial cytotoxicity, enabling more accurate evaluations before using nanomaterials in genetic engineering.

Why it matches plant phenotyping methodsカノーラプロトプラストの生存性を測定する高速・高スループットな蛍光アッセイを最適化し、ナノ材料毒性評価に適用しており、植物状態の取得方法が中心的です。

abstractWe present a fast, high-throughput method for assessing protoplast viability using resazurin

Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning. →

Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. High-resolution hyperspectral images provide abundant spectral-textural information, but whether they can enhance the accuracy of crop biomass estimations remains subject to further investigation. This study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images. Specifically, narrowband spectra and vegetation indices were extracted from the hyperspectral images. The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R 2 = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. Compared to the spectra feature scheme, the model's R 2 increased by 11.2%, RMSE decreased by 29%, and NRMSE reduced by 17%. These findings provide a reference for studies on UAV hyperspectral remote sensing monitoring of crop growth status.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像から冬カノーラの地上部バイオマスを推定する特徴抽出・機械学習手法を開発・評価しており、植物形質取得が研究の中心である。

abstractThis study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images.

Enhancing the Performance of Unmanned Aerial Vehicle-Based Estimation of Rape Chlorophyll Content by Reducing the Impact of Crop Coverage →

Estimating leaf chlorophyll content (LCC) in a timely manner and accurately is of great significance for the precision management of rape. The spectral index derived from UAV images has been adopted as a non-destructive and efficient way to map LCC. However, soil background impairs the performance of UAV-based LCC estimation, limiting the accuracy and applicability of the LCC estimation model, and this issue remains to be addressed. Thus, this research was conducted to study the influence of soil pixels in UAV RGB images on LCC estimation. UAV campaigns were conducted from overwintering to flowering stages to cover the process of soil background being gradually covered by rapeseed plants. Three planting densities of 11.25, 18.75, and 26.26 g/m2 were chosen to further enrich the different soil background percentage levels, namely, the rape fractional vegetation coverage (FVC) levels. The results showed that, compared to the insignificant difference observed for the ground measured LCC at a certain growth stage, a significant difference was found for most of the spectral indices extracted without soil background removal, indicating the influence of soil background. Removing soil background during the extraction of the spectral index enhanced the LCC estimation accuracy, with the coefficient of determination (R2) increasing from 0.58 to 0.68 and the root mean square error (RMSE) decreasing from 5.19 to 4.49. At the same time, the applicability of the LCC estimation model for different plant densities (FVC levels) was also enhanced. The lower the planting density, the greater the enhancement. R2 increased from 0.53 to 0.70, and the RMSE decreased from 5.30 to 4.81 under a low planting density of 11.25 g/m2. These findings indicate that soil background removal significantly enhances the performance of UAV-based rape LCC estimation, particularly under various FVC conditions.

Why it matches plant phenotyping methodsUAV画像からの葉緑素含量推定と、土壌背景除去による推定精度改善を中心に検証しており、植物形質の取得・抽出法が中核である。

abstractEstimating leaf chlorophyll content (LCC) in a timely manner and accurately is of great significance for the precision management of rape.

Image-Based Phenotyping Framework for Blackleg Disease in Canola: Progressing towards High-Throughput Analyses via Individual Plant Extraction →

Crop diseases are a significant constraint to agricultural production globally. Plant disease phenotyping is crucial for the identification, development, and deployment of effective breeding strategies, but phenotyping methodologies have not kept pace with the rapid progress in the genetic and genomic characterization of hosts and pathogens, still largely relying on visual assessment by trained experts. Remote sensing technologies were used to develop an automatic framework for extracting the stems of individual plants from RGB images for use in a pipeline for the automated quantification of blackleg crown canker (Leptopshaeria maculans) in mature Brassica napus plants. RGB images of the internal surfaces of stems cut transversely (cross-section) and vertically (longitudinal) were extracted from 722 and 313 images, respectively. We developed an image processing algorithm for extracting and spatially labeling up to eight individual plants within images. The method combined essential image processing techniques to achieve precise plant extraction. The approach was validated by performance metrics such as true and false positive rates and receiver operating curves. The framework was 98% and 86% accurate for cross-section and longitudinal sections, respectively. This algorithm is fundamental for the development of an accurate and precise quantification of disease in individual plants, with wide applications to plant research, including disease resistance and physiological traits for crop improvement.

Why it matches plant phenotyping methods個体植物の抽出と黒脚病の自動定量化を目的とする画像処理フレームワークを開発し、性能指標で検証しており、植物表現型取得法が研究の中心である。

abstractWe developed an image processing algorithm for extracting and spatially labeling up to eight individual plants within images.

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits. →

Fusing three-dimensional (3D) and multispectral (MS) imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge. Acquiring high-quality 3D MS point clouds (3DMPCs) of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure. Here, we present a novel 3D spatial-spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field (NeREF) for radiometric calibration. This approach was used to acquire 3DMPCs of perilla, tomato, and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness (EWT) estimation. The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6% compared with the fixed viewpoints alone. The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error (RMSE) of 58.93% for extracted reflectance spectra. The RMSE for chlorophyll content and EWT predictions decreased by 21.25% and 14.13% using partial least squares regression with the generated 3DMPCs. Collectively, our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions, which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits, and thus will facilitate plant biology and genetic studies as well as crop breeding.

Why it matches plant phenotyping methods無人地上車両を用いた三次元マルチスペクトル点群による植物形質の取得・空間解析手法が題名で明示されており、フェノタイピング手法が中心です。

titleAn unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits

A Novel Optimized Deep Learning Model for Canola Crop Yield Prediction on Edge Devices →

The escalating global demand for food, coupled with challenges in sustaining crop production, deteriorating ocean health, and depleting natural resources, underscores the critical role of agricultural technology. This article addresses the imperative of developing an optimal deep-learning model for predicting canola crop yield using hyperspectral images captured by drone flights. Our primary objective is to identify the most efficient model in terms of performance and size, considering the storage limitations on edge devices like Raspberry Pi 4 (RPi4). We start with the baseline 1D_CNN model, which achieves an$R^{2}$score of 0.82, and compress it into the proposedfs_model(fp32). To achieve the compression, we apply pruning through sparsity and feature selection using SHAP values. Further reduction in model size is accomplished by quantizing the weights of the proposed model to a lower precision, such as int16. This combined approach substantially decreases the proposed model's size by approximately 92.6% and inference time by approximately ×9013 in comparison to the baseline 1D_CNN model. In addition, we propose the novelfsp_modelposit(8,3) that uses posit quantization to further reduce the computation requirements compared to the proposedfs_model(int16). Our findings indicate that the utilization of posit numbers enables us to shrink the model size to 94% of the original base model, while only reducing the$R^{2}$score by 5.7%.

Why it matches plant phenotyping methodsドローンのハイパースペクトル画像からカノーラ収量を推定する深層学習モデルの圧縮・量子化・性能評価が研究の中心であり、植物形質の取得・推定手法に該当する。

titleA Novel Optimized Deep Learning Model for Canola Crop Yield Prediction on Edge Devices

Field-level analysis of phenological cycles and dynamics of sunflower (Helianthus annuus L.) and oil seed rape (Brassica napus L.) flowering within various regions of Hungary →

Phenological observations are expensive and demanding in terms of manpower to monitor the vegetation stages. Therefore, satellite products have opened new possibilities for easier and more widespread data collection. Nowadays biomass estimations extensively rely on these tools, given their extensive spatial and temporal coverage, which are defined by indicators such as vegetation indices, which describe the biomass growth, canopy structure, vegetation health and even water management etc. However, the detection of flowering stages through remote sensing is less explored, with fewer established methods available.This study investigates temporal phenological changes during the blooming period of the most widely cultivated oilseed crops in Hungary in 2021, specifically the sunflower (Helianthus annuus L.) and the winter-cultivated oilseed rape (Brassica napus L.). The objective is to characterize the blooming phase and dynamics of these two crop species utilizing various vegetation indexes and satellite-derived products. The investigation is conducted across seven distinct regions, using honey bees as bioindicators of the fields.Methodologies outlined in prior scientific literature, focusing on the analysis of anthesis timing and duration in major nectar-producing crops utilizing Sentinel-1 SAR and Sentinel-2 optical products, serve as the basis for this research. Within each radius study areas, the parcel-averaged and smoothed daily time series were acquired. The estimation of the blooming phases was achieved through parcel smoothing methods using daily non-parametric local regression (loess) approach which showed better performance compared to the Savitzky-Golay (SG) algorithm. In our flowering detection analysis, we also examined the differences in the ascending and descending orbits and their combined results. In the case of oil seed rape, the NDVI index reached its maximum after flowering, while for sunflower it varied. Additionally, we investigated the outcomes of all polarization and method combinations within each crop type.Our study enables the comprehension of temporal flowering patterns in bee pasture crops through the integration of SAR and optical measurements. Additionally, it supports the utilization of beehive scales to provide field-based reference data for estimating anthesis.The research was funded by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project. Project No. 993788 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2020 funding scheme.

Why it matches plant phenotyping methods衛星SAR・光学データから作物の開花時期・期間を推定する手法が中心で、loessとSavitzky–Golay法の性能比較も行っているため、植物フェノタイピング研究に該当する。

abstractThe objective is to characterize the blooming phase and dynamics of these two crop species utilizing various vegetation indexes and satellite-derived products.

How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits →

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.

Why it matches plant phenotyping methodsSentinel-1レーダー特徴量とInSARコヒーレンスを用いて作物のフェノロジーを追跡し、複数年・軌道・特徴量間で性能を比較しており、植物状態の取得・推定方法が中心である。

abstractThis study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits.

Nondestructive detection of lead content in oilseed rape leaves under silicon action using hyperspectral image. →

This study explored the feasibility of employing hyperspectral imaging (HSI) technology to quantitatively assess the effect of silicon (Si) on lead (Pb) content in oilseed rape leaves. Aiming at the defects of hyperspectral data with high dimension and redundant information, this paper proposed two improved feature wavelength extraction algorithms, repetitive interval combination optimization (RICO) and interval combination optimization (ICO) combined with stepwise regression (ICO-SR). The entire oilseed rape leaves were taken as the region of interest (ROI) to extract the visible near-infrared hyperspectral data within the 400.89-1002.19 nm range. In data processing, Savitzky-Golay (SG) smoothing, detrending (DT), and multiple scatter correction (MSC) were utilized for spectral data preprocessing, while recursive feature elimination (RFE), iteratively variable subset optimization (IVSO), ICO, and the two enhanced algorithms were employed to identify characteristic wavelengths. Subsequently, based on the spectral data of preprocessing and feature extraction, partial least squares regression (PLSR) and support vector regression (SVR) methods were used to construct various Pb content prediction models in oilseed rape leaves, with a comparison and analysis of each model performance. The results indicated that the two improved algorithms were more efficient in extracting representative spectral information than conventional methods, and the performance of SVR models was better than PLSR models. Finally, to further improve the prediction accuracy and robustness of the SVR models, the whale optimization algorithm (WOA) was introduced to optimize their parameters. The findings demonstrated that the MSC-RICO-WOA-SVR model achieved the best comprehensive performance, with R p 2 of 0.9436, RMSEP of 0.0501 mg/kg, and RPD of 3.4651. The results further confirmed the great potential of HSI combined with feature extraction algorithms to evaluate the effectiveness of Si in alleviating Pb stress in oilseed rape and provided a theoretical basis for determining the appropriate amount of Si application to alleviate Pb pollution in oilseed rape.

Why it matches plant phenotyping methods葉の鉛含有量という植物状態を非破壊HSIで推定する手法を開発・比較・最適化しており、特徴波長抽出と予測モデルの技術評価が中心である。

abstractThis study explored the feasibility of employing hyperspectral imaging (HSI) technology to quantitatively assess the effect of silicon (Si) on lead (Pb) content in oilseed rape leaves.

Phenomic Selection for Hybrid Rapeseed Breeding. →

Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.

Why it matches plant phenotyping methods近赤外分光データを用いた表現型予測を複数モデルで評価し、世代間予測や育種利用可能性を検証しており、表現型取得・抽出手法が研究の中心である。

abstractWe performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models.

Estimation of winter canola growth parameter from UAV multi-angular spectral-texture information using stacking-based ensemble learning model →

The leaf chlorophyll content (LCC) and leaf area index (LAI) play a crucial role in assessing crop growth status and optimizing field water-fertilizer management. Compared to labor-intensive traditional measurement methods, low-cost unmanned aerial vehicle (UAV) remote sensing technology provides a unique opportunity for monitoring small-scale farmland crop growth information. Currently, the estimation method combining machine learning with spectral or texture information from UAV images has attracted much attention. However, most studies commonly used spectral or texture information from vertical observation, and the potential of multi-angle spectral-texture information has not been fully explored. Meanwhile, the simultaneous usage of multiple characteristic information increases the computational complexity of the model, presenting challenges for crop growth parameter estimation. To address this, this study proposes a multi-feature fusion framework for canola growth parameter estimation using stacking-based ensemble learning algorithm. Firstly, 17 spectral features (VIₛ and Band) and 136 texture features (TFₛ) are extracted from UAV spectral images with seven view zenith angle (Nadir, ±20◦, ±40◦ and ± 60◦), respectively. Secondly, important feature variables are selected through correlation analysis and then feature datasets are constructed. Finally, a base learners-meta learner stacking structure was employed to ensemble four machine learning models (SVM,PLSR,RF and GBDT) to predict canola growth parameters. The result shows that: (1) Compared with nadir observation, off-nadir observations (especially for −20◦ and −40◦) can provide more spectral-texture information related to canola growth; (2) The stacking-based ensemble learning can achieve accurate estimation of canola LAI (R² = 0.72; RMSE = 0.74) and LCC (R² = 0.78; RMSE = 6.4 ug/cm²); (3) Compared with single machine learning model (LAI: R² = 0.56 ∼ 0.67, RMSE = 0.87 ∼ 1.08; LCC: R² = 0.56 ∼ 0.75, RMSE = 8.1 ∼ 10.6 ug/cm²), the stacking-based ensemble learning has advantage in growth parameters estimation. The corresponding spatiotemporal mapping reflects the impact of field treatment on the canola growth. Overall, These above results demonstrate the application value of multi-angle spectral-textural information and stacking-based ensemble learning in accessing crop growth, providing new insights into remotely quantitative diagnosis of crop growth.

Why it matches plant phenotyping methodsUAVの多角度スペクトル・テクスチャ画像からLAIと葉クロロフィル含量を推定する画像取得・特徴抽出・機械学習手法が研究の中心であり、植物表現型の定量推定を技術的に評価している。

abstractthis study proposes a multi-feature fusion framework for canola growth parameter estimation using stacking-based ensemble learning algorithm.

Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses. →

Key message Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources. Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.

Why it matches plant phenotyping methods種子発芽という植物形質を対象に、フェノミック予測法をゲノミック予測やQTL解析と比較し、予測性能と遺伝資源評価への有用性を検証しているため、方法の適用・評価が中心です。

abstractGenomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination.

Unmanned aerial vehicles (UAVs)-based crop lodging susceptibility and seed yield assessment during different growth stages of rapeseed (Brassica napus) →

Lodging is a great challenge in rapeseed production that significantly affects seed yield and quality. Prediction of lodging susceptibility and seed yield through unmanned aerial vehicles (UAVs)-based framework offers remarkable prospects for higher applicability in agriculture. This study aims to explore the possibility of using a UAV-based framework for predicting the stem and root lodging susceptibility (represented by safety factor, SFs and SFr respectively) and seed yield at different growth stages of rapeseed. The Red-Green-Blue (RGB) and multispectral (MS) images were captured during various growth stages by UAV platforms to calculate 16 vegetation indices (VIs). Furthermore, the relationships of these VIs with lodging susceptibility and seed yield were also established using multiple linear regression (MLR) and four machine learning methods (including random forest machine (RFR), support vector machine, artificial neural network, and K-nearest neighbors). The results revealed that MS-VIs provided a good estimation of seed yield, and stem and root lodging susceptibilities. Among the 16 VIs analyzed, MS-VI SR85 emerged as the best predictor for both seed yield and lodging susceptibility, as is evident by its highest importance scores. Furthermore, when RGB-VIs were coupled with MS-VIs, the R² values for estimating seed yield, stem lodging and root lodging resistance were enhanced by 150%, 69.6% and 106%, respectively, in comparison with RGB-VIs. Similarly, the RFR provided a more accurate machine learning method for predicting seed yield and lodging susceptibility compared to the other three models. Stem elongation stage was the optimum growth stage for the estimation of seed yield, and stem and root lodging susceptibilities due to its maximum prediction accuracy, as is suggested by the highest R² values. It can be inferred that a UAV-based framework in combination with RFR could serve as a high-throughput technique for large-scale prediction of lodging susceptibility and seed yield, as early as at stem elongation stage and thus provides an opportunity for timely agronomic intervention.

Why it matches plant phenotyping methodsUAV RGB・マルチスペクトル画像と機械学習を用いて、ナタネの倒伏感受性と収量という植物形質を推定する枠組み自体が中心的に開発・評価されている。

abstractPrediction of lodging susceptibility and seed yield through unmanned aerial vehicles (UAVs)-based framework offers remarkable prospects for higher applicability in agriculture.

Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions. →

Introduction The micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed ( Brassica napus ). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established. Methods The recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models. Results The recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%. Discussion This study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

Why it matches plant phenotyping methodsアブラナの鉄・ホウ素欠乏症状をハイパースペクトル画像から非破壊認識する手法を開発し、LDA/SVMや特徴波長選択で精度検証しており、表現型取得・抽出が研究の中心である。

abstracta non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established

Quantitative MRI imaging of parenchyma and venation networks in Brassica napus leaves: effects of development and dehydration. →

Background Characterisation of the structure and water status of leaf tissues is essential to the understanding of leaf hydraulic functioning under optimal and stressed conditions. Magnetic Resonance Imaging is unique in its capacity to access this information in a spatially resolved, non-invasive and non-destructive way. The purpose of this study was to develop an original approach based on transverse relaxation mapping by Magnetic Resonance Imaging for the detection of changes in water status and distribution at cell and tissue levels in Brassica napus leaves during blade development and dehydration. Results By combining transverse relaxation maps with a classification scheme, we were able to distinguish specific zones of areoles and veins. The tissue heterogeneity observed in young leaves still occurred in mature and senescent leaves, but with different distributions of T 2 values in accordance with the basipetal progression of leaf blade development, revealing changes in tissue structure. When subjected to severe water stress, all blade zones showed similar behaviours. Conclusion This study demonstrates the great potential of Magnetic Resonance Imaging in assessing information on the structure and water status of leaves. The feasibility of in planta leaf measurements was demonstrated, opening up many opportunities for the investigation of leaf structure and hydraulic functioning during development and/or in response to abiotic stresses.

Why it matches plant phenotyping methods葉の構造と水分状態を定量MRIで空間的に測定する新規手法を開発し、分類法と組み合わせて葉組織・葉脈を評価しており、植物フェノタイピング手法が中心である。

abstractThe purpose of this study was to develop an original approach based on transverse relaxation mapping by Magnetic Resonance Imaging for the detection of changes in water status and distribution at cell and tissue levels in Brassica napus leaves during blade development and dehydration.

High-throughput phenotyping of individual plant height in an oilseed rape population based on Mask-RCNN and UAV images →

Plant height, a key agronomic trait, affects crop structure, photosynthesis, and thus the final yield and seed quality. The combination of digital cameras on unmanned aerial vehicles (UAVs) and use of structure from motion have enabled high-throughput crop canopy height estimation. However, the focus of prior research has mainly been on plot-level height prediction, neglecting precise estimations for individual plants. This study aims to explore the potential of UAV RGB images with mask region-based convolutional neural network (Mask-RCNN) for high-throughput phenotyping of individual-level height (IH) in oilseed rape at different growth stages. Field-measured height (FH) of nine sampling plants in each subplot of the 150 subplots was obtained by manual measurement after the UAV flight. An instance segmentation model for oilseed rape with data augmentation based on the Mask-RCNN model was developed. The IHs were then used to obtain plot-level height based on individual-level height (PHIH). The results show that Mask-RCNN performed better than the conventional Otsu method with the F1 score increased by 60.8% and 26.6% under high and low weed pressure, respectively. The trained model with data augmentation achieved accurate crop height estimation based on overexposed and underexposed UAV images, indicating the model’s applicability in practical scenarios. The PHIH can be predicted with the determination coefficient (r²) of 0.992, root mean square error (RMSE) of 4.03 cm, relative root mean square error (rRMSE) of 7.68%, which outperformed the results in the reported studies, especially in the late bolting stage. The IHs of the whole growth stages of oilseed can be predicted by this method with an r² of 0.983, RMSE of 2.60 cm, and rRMSE of 7.14%. Furthermore, this method enabled a comprehensive Genome-wide association study (GWAS) in a 293-accession genetic population. The GWAS identified 200 and 65 statistically significant single nucleotide polymorphisms (SNPs), which were tightly associated with 28 and 11 candidate genes, at the late bolting and flowering stages, respectively. These findings demonstrated that the proposed method is promising for accurate estimations of IHs in oilseed rape as well as exploring the variations within the subplot, thus providing great potential for high-throughput plant phenotyping in crop breeding.

Why it matches plant phenotyping methodsUAV画像とMask-RCNNを用いて個体別草丈を推定する手法を開発・検証しており、植物表現型の取得と精度評価が研究の中心です。

abstractThis study aims to explore the potential of UAV RGB images with mask region-based convolutional neural network (Mask-RCNN) for high-throughput phenotyping of individual-level height (IH) in oilseed rape at different growth stages.

Determining rapeseed lodging angles and types for lodging phenotyping using morphological traits derived from UAV images →

Crop lodging detrimentally affects crop yield and mechanical harvest efficiency. Traditional remote sensing-based methods primarily focus on the identification and area extraction of lodging using image texture and spectrum. However, the response of image texture and spectrum to lodging is indirect and varies under diverse conditions. Moreover, other important finer details of lodging phenotyping, such as lodging angle and lodging type, have frequently been neglected. In this study, a robust and accurate method was developed for investigating lodging phenotypes in the field. The method was based on the three-dimensional morphological information of rapeseed (Brassica napus L.) canopy reconstructed from unmanned aerial vehicle (UAV) images. In contrast to traditional remote sensing methods that only identify lodging targets and their respective areas, the novel method in this study calculated the total lodging angle (TLA), root lodging angle (RLA), stem lodging angle (SLA = TLA - RLA), and lodging types according to a morphological method and a lodging classification model. Initially, the method employed a geometric model to characterize the stalk shape of lodged rapeseed. After assessing numerous lodging samples from individual rapeseed plants, the circle function was identified as the optimal geometric model. With this optimal function, the canopy height derived from the UAV images was found effective in calculating TLA, RLA, and SLA across 24 rapeseed cultivars in five climatic zones within the Yangtze River Basin (YRB) in China. Results showed that the average root mean square error (RMSE) was 8.3° for TLA and 7.4° for RLA. Subsequently, based on field measured data of SLA and RLA, a decision tree model was constructed to classify lodging types and an accuracy of 95.4% was achieved. Using the classification model and estimated values of RLA and SLA, the spatial distribution information and specific area estimates for different lodging types were obtained. Based on the analysis of these results, the rapeseed cultivars Zhongshuang 11 and Dadi 199 were determined to be the dominant cultivars with lodging resistance in the YRB, even though they did not achieve the mean high yields in multiple climatic zones. However, the lodging-prone cultivars such as Qinyou7 and Qinyou33 fell under the low-yield level in all climatic zones. The robust and cost-effective method proposed in this study for acquiring detailed crop lodging phenotyping data has the potential to enhance mechanized harvesting, accurately estimate the risk of low yield, and assess the lodging status of various crops.

Why it matches plant phenotyping methodsUAV画像から作物倒伏の角度・種類という植物状態を抽出する手法を開発し、複数品種・地域で精度検証しているため、方法が中心的である。

abstractIn this study, a robust and accurate method was developed for investigating lodging phenotypes in the field.

Remote estimation of rapeseed phenotypic traits under different crop conditions based on unmanned aerial vehicle multispectral images →

Rapeseed is an essential oil crop and the third major source of edible oil in the world. Accurate estimation of rapeseed phenotypic traits at field scale is important for precision agriculture to improve agronomic management and ensure edible oil supply. Unmanned aerial vehicle (UAV) remote sensing technology has been applied to estimate crop phenotypic traits at field scale. Machine learning is one of the main methods to develop estimation models for phenotypic traits based on UAV data. However, the accuracy and adaptability of machine learning estimation models are constrained by the representativeness of the training data. Here, we explored the influence of growth stage and crop conditions on the estimation of rapeseed phenotypic traits by machine learning and provided an optimized strategy to construct training data for improving the estimation accuracy. Four machine learning methods were employed, including partial least squares regression, support vector regression (SVR), random forest (RF), and artificial neural network (ANN), with SVR showing the best performance in estimating rapeseed phenotypic traits. The models established for a certain cultivar, planting site, or planting density had low estimation accuracies for other cultivars, planting sites, and planting densities during the entire growth period. The results showed that cultivar and planting site had an unquantifiable influence on phenotypic traits. Integration of stratified sampling and developing estimation models for different growth stages respectively can improve the estimation accuracy for different cultivars and planting sites during the entire growth period. Planting density exhibited a quantifiable influence on phenotypic traits, and the construction of training data with samples of both low and high planting densities could improve the estimation accuracy for different planting densities. Overall, optimization of the training data by considering the influence of crop conditions on phenotypic traits can improve the estimation accuracy of rapeseed phenotypic traits based on machine learning.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習によるナタネ形質推定モデルを開発・比較し、作物条件を考慮した学習データ最適化と精度改善を検証しており、表現型取得・推定手法が研究の中心である。

titleRemote estimation of rapeseed phenotypic traits under different crop conditions based on unmanned aerial vehicle multispectral images

Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses →

Abstract Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment, and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate to high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction better estimated breeding values than genomic prediction. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.

Why it matches plant phenotyping methods種子発芽形質を対象に、フェノミック予測の予測性能とゲノミック予測との比較を主要課題として扱っており、形質推定手法が中心である。

abstractGenomic and phenomic predictions methods provided moderate to high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination.

Detection of oilseed rape clubroot based on low-field nuclear magnetic resonance imaging →

Plant root diseases threat plant growth and eventually cause plant death without proper treatment. It is difficult to diagnose root diseases without digging the roots from the soil, and it is late when the above-ground parts show symptoms under the stress of root diseases. This study used magnetic resonance imaging (MRI) for non-invasive root phenotyping to detect oilseed rape clubroot. MRI images of healthy oilseed rape roots and roots infected by clubroot were obtained. After image preprocessing, average sample grayscale histograms (Avg-SGH) were extracted to build classification models for disease identification using logistic regression (LR), support vector machine (SVM) and random forest (RF). Reconstruction of three-dimensional (3D) root architectures was also conducted. Root architecture parameters were extracted from the reconstructed roots. Analysis of variance (ANOVA) showed that the root architecture parameters differed significantly between healthy and infected roots. RF model using root architecture parameters showed good performances, and the feature importance for clubroot identification was also explored. The overall results showed that MRI could effectively detect clubroot diseases in a non-invasive manner, indicating significant potential for plant root phenotyping.

Why it matches plant phenotyping methodsMRIによる非侵襲的な根の表現型取得、3D根系再構成、根系形態パラメータ抽出、およびクラブルート識別モデルを中心に扱っており、植物病害状態のフェノタイピング手法として中心的です。

abstractThis study used magnetic resonance imaging (MRI) for non-invasive root phenotyping to detect oilseed rape clubroot.

Determination of lead content in oilseed rape leaves in silicon-free and silicon environments based on deep transfer learning and fluorescence hyperspectral imaging. →

The ability of fluorescence hyperspectral imaging to predict heavy metal lead (Pb) concentration in oilseed rape leaves was studied in silicon-free and silicon environments. Further, the transfer stacked convolution auto-encoder (T-SCAE) algorithm was proposed based on the stacked convolution auto-encoder (SCAE) algorithm. Fluorescence hyperspectral images of oilseed rape leaves under different Pb stress contents were obtained in the silicon-free and silicon environments. The entire region of oilseed rape leaves was chosen as the region of interest (ROI) to obtain fluorescence spectra. First of all, standard normalized variable (SNV) algorithm was implemented as the preferred preprocessing method, and the fluorescence spectral data processed by SNV was utilized for further analysis. Further, SCAE was used to reduce the dimensionality of the best pre-processed spectral data, and compared with the traditional dimensionality reduction algorithm. Finally, the optimal SCAE deep learning network was transferred to obtain the T-SCAE model to verify the transferability between the deep learning models in silicon-free and silicon environments. The results show that the SVR model based on the depth features extracted by SCAE has the best performance in predicting different Pb concentrations in silicon-free or silicon environments, and the coefficient of determination (R p 2 ), root mean square error (RMSEP) and residual predictive deviation (RPD) of prediction set in silicon-free or silicon environments were 0.9374, 0.02071 mg/kg and 3.268, and 0.9416, 0.01898 mg/kg and 3.316, respectively. Moreover, the SVR model based on the depth feature extracted by T-SCAE has the best performance in predicting different Pb concentrations in silicon-free and silicon environments, and the R p 2 , RMSEP and RPD of the optimal prediction set were 0.9385, 0.02017 mg/kg and 3.291, respectively. The combination of hyperspectral fluorescence imaging and deep transfer learning algorithm can effectively detect different Pb concentrations in oilseed rape leaves in both non-silicon environment and silicon environment.

Why it matches plant phenotyping methods蛍光ハイパースペクトル画像と深層転移学習を用いて、ナタネ葉のPb濃度という植物状態を推定する手法を開発・検証しており、表現型取得・抽出が研究の中心である。

abstractThe ability of fluorescence hyperspectral imaging to predict heavy metal lead (Pb) concentration in oilseed rape leaves was studied in silicon-free and silicon environments.

Quantifying consistency of crop establishment using a lightweight U-Net deep learning architecture and image processing techniques →

Consistency of crop establishment is a measure of uniformity of crop attributes, such as plant stand count, crop emergence rate, and plant spacing across the field. Quantifying consistency during the early crop growth stage is important for establishment decisions to use targeted nutrients and to facilitate timely replanting in inconsistent crop regions. Crop consistency can be analysed using two key parameters: plant stand count and spacing statistics since they provide insight into plant density and its emergence percentage. However, manual assessment of them is time-consuming, prone to errors, and labour-intensive in large fields. An alternative method is proposed to automate estimating these parameters using field imagery under uncontrolled settings. We use the YOLOv5-based object detection model for plant counting, which attains a mean average precision of 0.956 to detect Canola plants. A Lightweight U-Net model is proposed to segment rows, followed by Guo–Hall thinning and Probabilistic Hough Transform to determine inter-row and inter-plant spacing. Our proposed row segmentation model achieves a mean Intersection over Union (mIoU) of 0.8444 with class-wise IoU of 0.9925 and 0.6963 for background and crop using fewer parameters. The new architecture uses only 14M parameters and achieves performance comparable to the state-of-the-art U-Net (32.5M) and SegNet (29M).

Why it matches plant phenotyping methods圃場画像から作物個体数、出芽率、株間・条間を自動推定する画像解析手法を開発・評価しており、植物形質の取得が研究の中心である。

abstractAn alternative method is proposed to automate estimating these parameters using field imagery under uncontrolled settings.

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models →

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

Why it matches plant phenotyping methods圃場高スループット表現型計測とセンサー特徴量・機械学習による被覆作物バイオマス推定が研究の中心であり、手法の比較・性能評価も実施している。

abstractFour ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated.

Rape seedling density estimation in‐field conditions based on improved multi‐column convolutional neural network →

Early‐stage rape seedling density is closely related to yield estimation, growth diagnosis, cultivated area statistics, and field management. Currently, manual sampling and counting, which are inefficient and inaccurate, are heavily relied upon to estimate rape seedling density. Computer vision techniques have emerged as a promising solution to the automation of this task, as digital images have become more commonplace. Farmland field environments, however, face many challenges, including scale variation, denseness, and background occlusion. An improved multi‐column convolutional neural network, called seedling rape density prediction network (SRDPNet), has been proposed in this study to resolve the issues related to accurate density estimation and counting of rape seedlings in complex farmland scenarios. Based on the multi‐column convolutional attention encoder, filters of different sizes are used to capture the basic feature of rape seedlings at various scales. The channel attention and position attention modules are introduced into branches to alleviate the impact of low counting accuracy caused by background error and growth state differences. The SRDPNet was validated using the seedling rapeseed plant counting (SRPC) dataset created in this study. The experimental results showed that the SRDPNet demonstrated high accurate counting performance for the SRPC dataset with a high coefficient of determination (R² = 0.97396) and mean absolute error (MAE = 3.26, mean square error = 4.56), which are superior to that of the comparison method. SRDPNet can effectively solve the visual challenges of rape seedlings in complex farmland scenes and improve the robustness for complex visual variations.

Why it matches plant phenotyping methods菜種幼苗の密度・個体数という植物形質を画像から推定するCNN手法を開発し、作成したデータセットで検証しており、表現型取得・抽出法が中心である。

abstractAn improved multi‐column convolutional neural network, called seedling rape density prediction network (SRDPNet), has been proposed in this study to resolve the issues related to accurate density estimation and counting of rape seedlings in complex farmland scenarios.

Spatial heterogeneity analysis of silique chlorophyll a fluorescence-based photosynthetic traits for rapeseed yield and quality assessment →

Photosynthesis is an important process that contributes to capturing light energy and transforming it into chemical energy in plants. To date, most studies on the photosynthetic traits focus on plant leaves, while the photosynthetic response of non-foliar organs such as siliques of oilseed rapes at the vertical distribution of the canopy, and their contribution to yield and quality traits remain poorly understood. Therefore, this study aims to elucidate the spatial heterogeneity of silique chlorophyll a fluorescence features and estimate targeted rapeseed yield and quality traits in a non-destructive manner before harvest. Field experiments were conducted for silique chlorophyll a fluorescence, morphological traits, yield and quality traits measurements within different spatial layers under varied nitrogen treatment levels. Correlation analysis and random forest (RF) algorithm were applied to identify crucial JIP-test parameters in different spatial layers and to evaluate their performance on rapeseed yield and quality evaluation. Our results found that several JIP-test parameters (ABS/RC, TRₒ/RC, and ETₒ/RC) of the siliques at the top layer related to the activity of reaction center (RC) presented superior photoprotection and photosynthetic capacities compared to those in the middle and bottom layers, indicating a significant spatial heterogeneity of the primary photosynthesis of siliques. As for yield and quality traits, rapeseeds had the highest oil content at the bottom layer followed by the middle and top layers, whereas protein content and yield traits were completely opposite to this tendency. RF model further confirmed that the JIP-test parameters acquired from siliques of the upper layers were more sensitive to the yield and protein content prediction, while the oil content can be better estimated using the JIP-test parameters of siliques at the bottom layer. Furthermore, a relatively weak relationship was identified between the glucosinolates and erucic acid content and JIP-test parameters. We expect that findings in this study could provide a new way to understand the spatial heterogeneity of silique photosynthetic traits, which might be useful for developing high yield and quality cultivars in rapeseed breeding.

Why it matches plant phenotyping methodsシリケのクロロフィル蛍光を用いて収量・品質形質を非破壊推定し、JIPパラメータとRFモデルの性能を評価しており、植物表現型の取得・推定が中心的である。

abstractestimate targeted rapeseed yield and quality traits in a non-destructive manner before harvest

Biomass‐based lateral root morphological parameter models for rapeseed (Brassica napus L.) →

Abstract Lateral roots, including adventitious roots, are the main component of rapeseed roots with support, absorb, and synthesis functions and their morphological parameters directly affecting the plant's aboveground growth and yield. Root biomass, as a material base for lateral root growth, can be used as a link between plant phenotypes and their physiological processes, as well as to enhance root 3D growth model mechanisms and accuracy. To quantify the relationships between lateral root morphological indices and the corresponding organ biomass for rapeseed, we used two cultivars, NY 22 (conventional) and NZ 1818 (hybrid), and conducted cultivar and fertilizing cylindrical tube experiments during the 2016–2019, with two fertilizer levels, no fertilizer, and 180 kg N ha−1 fertilizer. The lateral root biomass and morphological parameters were determined during the whole growth period. The biomass‐based lateral root morphological parameter models were developed by analyzing the quantitative relationship between the lateral root morphological indices and their corresponding biomass, and the descriptive models were verified with independent experimental data. The results showed that the correlation (r) of simulated and observed values for the lateral root morphological parameters are all greater than 0.9 with significant levels at p < 0.001. The absolute values of the average absolute difference (da) of simulated and observed values for the lateral root length (LLR), lateral root average diameter (ADLR), lateral root surface area (SALR), and lateral root volume (VLR) are −30.408 cm, −0.003 mm, 12.902 cm2, and 0.039 cm3, respectively. The RMSE values are 175.183 cm, 0.010 mm, 59.710 cm2, and 1.513 cm3, respectively. The ratio of da to the average observed values (dap) for the LLR and VLR are all less than 5%, and the ADLR and SALR are all <6%. The models developed in this paper have good performance and reliability for predicting lateral root morphological parameters of rapeseed. The study provides a mechanistic method for linking the rapeseed growth model with the morphological model using corresponding organic biomass and laying a good foundation for establishing a 3D morphological model for rapeseed root system based on biomass.

Why it matches plant phenotyping methodsラテラルルートの形態形質をバイオマスから推定するモデルを開発し、独立実験データで検証しており、根形態フェノタイピング手法が研究の中心である。

abstractThe biomass‐based lateral root morphological parameter models were developed by analyzing the quantitative relationship between the lateral root morphological indices and their corresponding biomass, and the descriptive models were verified with independent experimental data.

Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data →

Nitrogen is an essential nutrient in crop growth cycle and directly affects the photosynthesis of crops. The leaf chlorophyll content (LCC) and leaf area index (LAI) are important for characterizing photosynthetic capacity of crops and are critical indicators for diagnosing nitrogen status of crops. Hyperspectral remote sensing technology provided a means to achieve crop LCC and LAI estimation. However, the redundancy of spectral data and canopy structure effect can cause poor robustness of the estimating models, further hindering the development and application of estimating models across different crop species. In this study, a method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops. First, for the spectral dataset generated by the PROSAIL model, a deep neural network was used to construct LCC and LAI estimation models (called DNNCA model). Secondly, for the hyperspectral data collected from field crops (soybean, canola and wheat), incremental learning using regularization (LwF algorithm) was used to update the DNNCA model parameters. Finally, the dilution curve model based on the LCC-LAI anisotropic growth relationship was developed to assess crop nitrogen status. The results showed that: (1) The constrained bi-objective optimizated DNNCA model can consider the interactive effect of LAI and LCC on spectral reflectance, and achieved reliable estimation on PROSAIL simulation data set (LAI:R² = 0.82, RMSE = 0.77 m²/m²; LCC:R² = 0.91, RMSE = 6.4 ug/cm²). (2) By incremental learning, DNNCA model has continuous learning capability and stable estimation on cross-crop (canola, soybean and wheat) field-measured data (LAI:R² = 0.64–0.82, RMSE = 0.58–1.02 m²/m²; LCC:R² = 0.56–0.82, RMSE = 3.9–10.5 ug/cm²). (3) The relationship between the NNILCC and the NNILNC was significant. The NNILCC derived from the anisotropy relationship between crop LAI and LCC was an effective tool for crop nitrogen status diagnosis. (4) The process of crop LAI, LCC and NNILCC reflected the effect of water and nitrogen supply on crop growth. The appropriate water-nitrogen treatments contributed to LAI increase and LCC accumulation. The study demonstrated that hyperspectral remote sensing technology combined with incremental learning is an effective method for cross-crop growth monitoring and nitrogen diagnosis. These results provide a reference and basis for filed water-nitrogen supply and management.

Why it matches plant phenotyping methodsハイパースペクトルデータからLAI・葉クロロフィル含量を推定し、増分学習によるモデル更新と作物間検証を行う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に該当する。

abstracta method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops

Editorial: Machine vision and machine learning for plant phenotyping and precision agriculture →

Machine vision and machine learning for plant phenotyping and precision agriculturePlant phenotyping (PP) describes the physiological and biochemical properties of plants affected by both genotypes and environments.It is an emerging research field assisting the breeding and cultivation of new crop varieties to be more productive and resilient to challenging environments.Precision agriculture (PA) uses sensing technologies to observe crops and then manages them optimally to ensure that they grow in healthy conditions, have maximum productivity, and have minimal adverse effects on the environment.Traditionally, the observation of plant traits heavily relies on human experts, which is labour-intensive, time-consuming, and subjective.Although PP and PA are two different fields, they share similar sensing and data processing technologies in many respects.Recently, driven by computer and sensor technologies, machine vision (MV) and machine learning (ML) have contributed to accurate, high-throughput and nondestructive sensing and data processing technologies to PP and PA.However, these technologies are still in their infant stage, and many challenges and questions related to them still need to be addressed.This Research Topic aims to share the latest research results on applying MV and ML to PP and PA.It demonstrates cutting-edge technologies, bottle-necks and future research directions for MV and ML in crop breeding, crop cultivation, and disease or pest management.This Research Topic of Frontiers in Plant Sciences published a total of 28 peer-reviewed research articles, including one review paper for the phenotyping of Prunoideae fruits (Liu et al.).These articles reveal the latest research trends regarding different crop species, data types and algorithms.The summary of the published reports shows that cotton (Gossypium), canola or oilseed rape (Brassica napus), wheat (Triticum) and maize (Z.mays) are the most important crops for study in PP and PA (Figure 1A).Cotton stands out as the most frequently examined crop, with a total of five articles dedicated to it.Yan et al. developed a leaf segmentation method in the field environments.Tang et al. investigated early detection

Why it matches plant phenotyping methods植物フェノタイピングにおける機械視覚・機械学習技術を中心に扱う編集論文であり、方法論の動向と応用を概説しているため。

titleEditorial: Machine vision and machine learning for plant phenotyping and precision agriculture

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm. →

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R 2 c, R 2 p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R 2 c, R 2 p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.

Why it matches plant phenotyping methodsスペクトル計測とCWT・SPA・回帰モデルを用いて、ラピナス葉の光合成特性を非破壊推定する手法の構築・精度評価が研究の中心である。

abstractRemote sensing technology can quickly and non-destructively obtain crop information under salinity stress

Use of an unmanned aerial vehicle for monitoring and prediction of oilseed rape crop performance →

The flowering stage of oilseed rape (Brassica napus L.) is of vital interest in precision agriculture. It has been shown that data describing the flower production of oilseed rape (OSR), at stage 3, in spring can be used to predict seed yield at harvest. Traditional field-based techniques for assessing OSR flowers are based on a visual assessment which is subjective and time consuming. However, a high throughput phenotyping technique, using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera, was used to investigate the growth stages of OSR (in terms of crop height) and to quantify its flower production. A simplified approach using a normalised difference yellowness index (NDYI) was coupled with an iso-cluster classification method to quantify the number of OSR flower pixels and incorporate the data into an OSR seed yield estimation. The estimated OSR seed yield showed strong correlation with the actual OSR seed yield (R2 = 0.86), as determined using in-situ sensors mounted on the combine harvester. Also, using our approach allowed the variation in crop height to be assessed across all growing stages; the maximum crop height of 1.35 m OSR was observed at the flowering stage. This methodology is proposed for effectively predicting seed yield 3 months prior to harvesting.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と画像分類により、ナタネの草高・花数を定量化し収量予測へ利用する高スループット表現型計測手法が中心である。

abstracta high throughput phenotyping technique, using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera, was used to investigate the growth stages of OSR (in terms of crop height) and to quantify its flower production.

Understanding the trade–off between lodging resistance and seed yield, and developing some non–destructive methods for predicting crop lodging risk in canola production →

Efficient nitrogen (N) nutrient management is important for developing sustainable strategies to increase seed yield while reducing negative environmental impacts. Motivation to increase seed yield by appropriately increasing N application rates would make crop lodging a potential problem. Fewer studies have been conducted to elucidate the trade–off between yield gain and lodging susceptibility in canola (Brassica napus L.), and a non–destructive and high–throughput assessment of lodging is seriously lacking. In this regard, a field study consisting of two varieties and five combinations of rates and timing of N application was conducted to explore the strength of their trade–offs for better N fertilization recommendation and to determine the feasibility of non–destructive technique for diagnosing canola lodging susceptibility. Two non–destructive techniques including root electrical capacitance and normalized difference vegetative index (NDVI) were verified in this study. The susceptibility of stem and root lodging were quantified by “safety factor” method. The results showed that there was a trade–off between seed yield and lodging resistance under conditions of seed yield greater than 1.3 t ha–¹. Under high–yielding condition of 2021 cropping season, split–N treatment, 50 kg N ha–¹ at preplant plus 50 kg N ha–¹ topdressed at the 6–leaf stage, increased seed yield by 20% and lodging resistance by 38% for hybrid ‘Invigor L233P’, compared with the equivalent preplant–only N application. However, under low–yielding condition, split–N treatments did not always show advantages over the equivalent preplant–only N treatments in terms of lodging resistance and seed yield, whereas the highest seed yield was generally attained under the split–N application in all experimental years. Root capacitance and impedance were significantly correlated with root morphological traits, which in turn affected lodging resistance and seed yield, while NDVI was closely related to lodging resistance (P < 0.01). A split–N application strategy with moderate N rates (100–150 kg N ha–¹) can be highly recommended for canola production in eastern Canada. Indirect prediction of lodging susceptibility through root electrical measurements and NDVI mapping have high applicability due to their low cost and non–destructive properties, and are expected to serve as high–throughput techniques for guiding N fertilizer management to improve seed yield, while reducing lodging risk.

Why it matches plant phenotyping methods根の電気容量・インピーダンスとNDVIを用いた倒伏感受性の非破壊・高スループット推定を検証しており、表現型取得法が実質的な研究目的に含まれる。

abstracta non–destructive and high–throughput assessment of lodging is seriously lacking

Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development →

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations (σVV0, σVH0), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance, σVH0 of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola σVH0 of Sentinel-1 offered higher performance (r = 0.88). Generally, σVV0, σVH0 performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture.

Why it matches plant phenotyping methodsSARセンサー由来指標と時系列フィルタリングを比較・評価し、作物の生育段階や草丈などの植物形質追跡への有用性を検証しており、フェノタイピング手法の評価が中心である。

abstractthis work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato.

Segmentation and Phenotype Calculation of Rapeseed Pods Based on YOLO v8 and Mask R-Convolution Neural Networks →

Rapeseed is a significant oil crop, and the size and length of its pods affect its productivity. However, manually counting the number of rapeseed pods and measuring the length, width, and area of the pod takes time and effort, especially when there are hundreds of rapeseed resources to be assessed. This work created two state-of-the-art deep learning-based methods to identify rapeseed pods and related pod attributes, which are then implemented in rapeseed pots to improve the accuracy of the rapeseed yield estimate. One of these methods is YOLO v8, and the other is the two-stage model Mask R-CNN based on the framework Detectron2. The YOLO v8n model and the Mask R-CNN model with a Resnet101 backbone in Detectron2 both achieve precision rates exceeding 90%. The recognition results demonstrated that both models perform well when graphic images of rapeseed pods are segmented. In light of this, we developed a coin-based approach for estimating the size of rapeseed pods and tested it on a test dataset made up of nine different species of Brassica napus and one of Brassica campestris L. The correlation coefficients between manual measurement and machine vision measurement of length and width were calculated using statistical methods. The length regression coefficient of both methods was 0.991, and the width regression coefficient was 0.989. In conclusion, for the first time, we utilized deep learning techniques to identify the characteristics of rapeseed pods while concurrently establishing a dataset for rapeseed pods. Our suggested approaches were successful in segmenting and counting rapeseed pods precisely. Our approach offers breeders an effective strategy for digitally analyzing phenotypes and automating the identification and screening process, not only in rapeseed germplasm resources but also in leguminous plants, like soybeans that possess pods.

Why it matches plant phenotyping methodsナタネ莢の画像セグメンテーション、計数、長さ・幅・面積推定手法を開発し、手動測定との相関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 work created two state-of-the-art deep learning-based methods to identify rapeseed pods and related pod attributes

A standardized and efficient technique to estimate seed traits in plants with numerous small propagules. →

Premise Variation in seed traits is common within and among populations of plant species and often has ecological and evolutionary implications. However, due to the time-consuming nature of manual seed measurements and the level of variability in imaging techniques, quantifying and interpreting the extent of seed variation can be challenging. Methods We developed a standardized high-throughput technique to measure seed number, as well as individual seed area and color, using a derived empirical scale to constrain area in Arabidopsis thaliana, Brassica rapa , and Mimulus guttatus . We develop a specific rational model using seed area measured at various spatial scales relative to the pixel count, observing the asymptotic value of the seed area as the modeled number of pixels approaches infinity. Results We found that our model has high reliability in estimating seed traits and efficiently processes large numbers of images, facilitating the quantification of seed traits in studies with large sample sizes. Discussion This technique facilitates consistency between imaging sessions and standardizes the measurement of seed traits. These novel advances allow researchers to directly and reliably measure seed traits, which will enable tests of the ecological and evolutionary causes of their variation.

Why it matches plant phenotyping methods種子数・面積・色を画像から高スループットに推定する標準化手法を開発しており、植物形質の取得・抽出が研究の中心である。

abstractWe developed a standardized high-throughput technique to measure seed number, as well as individual seed area and color

Determination of water-soluble carbohydrates by near-infrared spectroscopy for canola, maize, and sorghum stem fractions. →

Near infrared spectroscopy (NIRS) was evaluated as a rapid and non-destructive method for determining the concentration of water-soluble carbohydrates (WSC) in stem fractions for winter canola (Brassica napus L.), maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) crops. For each crop at different growth stages, stem WSC concentration was determined using NIRS, and benchmarked against the anthrone reagent method, chemical lab analysis. Partial least squares regression was implemented to associate the WSC predicted via NIRS relative to those obtained by laboratory analysis. Spectral regions between 1100 and 1480 nm were critical for WSC determination. The predictive models resulted in coefficient of determinations of 0.93, 0.94, and 0.95, and a Root Mean Square Error of prediction of 10, 20, and 17 for winter-canola, maize and sorghum crops, respectively. The NIRS spectroscopy is a reliable method for WSC determination in stem tissues on these major field crops.

Why it matches plant phenotyping methods作物の茎組織における水溶性炭水化物濃度をNIRSで非破壊推定し、化学分析とベンチマークして予測性能を検証しているため、植物形質計測法が中心である。

abstractNear infrared spectroscopy (NIRS) was evaluated as a rapid and non-destructive method for determining the concentration of water-soluble carbohydrates (WSC) in stem fractions

Identification of Robust Hybrid Inversion Models on the Crop Fraction of Absorbed Photosynthetically Active Radiation Using PROSAIL Model Simulated and Field Multispectral Data →

The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage.

Why it matches plant phenotyping methodsキャノーラの植物キャノピー形質であるFPARを、マルチスペクトルデータ、PROSAIL、植生指数、機械学習により推定する手法を開発・比較しており、形質取得・推定法が研究の中心である。

abstractThe objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR.

Prediction of Seedling Oilseed Rape Crop Phenotype by Drone-Derived Multimodal Data →

In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data and multi-source non-remote sensing data. This study employs Image Guided Filtering Fusion (GFF) to obtain high-resolution multispectral images (HR-MSs) and selects three vegetation indices (VIs) based on correlation analysis and feature reduction in HR-MS for multi-source sensing data. As a supplement to remote sensing data, multi-source non-remote sensing data incorporate two meteorological conditions: temperature and precipitation. This research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD). To validate the monitoring effectiveness of multimodal data, the study constructs four model frameworks based on multimodal data input and employs Support Vector Regression (SVR), Partial Least Squares (PLS), Backpropagation Neural Network (BPNN), and Nonlinear Model Regression (NMR) machine learning models to create winter rapeseed quantitative monitoring models. The findings reveal that the model framework, which integrates multi-source remote sensing data and non-remote sensing data, exhibits the highest average precision (R2 = 0.7454), which is 28%, 14.6%, and 3.7% higher than that of the other three model frameworks, enhancing the model’s robustness by incorporating meteorological data. Furthermore, SVR consistently performs well across various multimodal model frameworks, effectively evaluating the vigor of rapeseed seedlings and providing a valuable reference for rapid, non-destructive monitoring of winter rapeseed.

Why it matches plant phenotyping methodsUAVマルチモーダルデータからLAI、地上部バイオマス、葉窒素、クロロフィルなどの植物形質を推定する監視モデルを構築・検証しており、表現型取得・推定手法が研究の中心である。

abstractThis research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD).

Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model →

Unmanned aerial vehicle (UAV) platform has been perceived as a useful tool for high-throughput field phenotyping of crop growth traits. While interpretation of UAV image data and retrieval of reliable and accurate phenotypic information are still challengeable due to the variations in sensors, crops and environment conditions. The aim of this study, therefore, is to explore the potential of UAV-based field phenotyping with the PROSAIL model to estimate biomass of rice and oilseed rape crops. Field experiments were designed for rice and oilseed rape with different nitrogen (N) treatments, and a UAV platform mounted with a multispectral camera was used to collect multi-temporal field images. Simultaneously, field measurements of leaf chlorophyll content (Cₐb), leaf area index (LAI), canopy chlorophyll content (CCC) and biomass were conducted. The results showed that coupling UAV-based multispectral images at the spectral region of 604–872 nm with the PROSAIL model successfully retrieved Cₐb, LAI and CCC of rice with the root mean square error (RMSE) of 5.40 μg/cm², 1.13, and 43.50 μg/cm², respectively. Further, the Cₐb, LAI and CCC retrieved from the PROSAIL model achieved the satisfactory biomass estimation in rice with the RMSE of 0.32 kg/m², 0.23 kg/m² and 0.22 kg/m², respectively, which was comparable or superior to those obtained from commonly used empirical models. The proposed method also presented the robust performance for rice biomass estimation at different growth stages. In addition, model validation with the oilseed rape dataset showed an acceptable accuracy of biomass estimation with the determination coefficient (r²), RMSE and relative RMSE of 0.81, 0.03 kg/m² and 27.82%, respectively, and still outperformed the empirical models with the better estimation performance. These findings demonstrate the potential of the proposed biomass retrieval strategy for UAV-based multispectral images, which also extend the application of PROSAIL model in field phenotyping of crop growth traits.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像とPROSAILモデルを用いて、作物の生育形質（葉面積指数、クロロフィル、バイオマス）を推定する手法を開発・検証しており、フェノタイピング手法が中心である。

titleUnmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model

Abundance considerations for modeling yield of rapeseed at the flowering stage. →

Introduction To stabilize the edible oil market, it is necessary to determine the oil yield in advance, so the accurate and fast technology of estimating rapeseed yield is of great significance in agricultural production activities. Due to the long flowering time of rapeseed and the characteristics of petal color that are obviously different from other crops, the flowering period can be carefully considered in crop classification and yield estimation. Methods A field experiment was conducted to obtain the unmanned aerial vehicle (UAV) multispectral images. Field measurements consisted of the reflectance of flowers, leaves, and soils at the flowering stage and rapeseed yield at physiological maturity. Moreover, GF-1 and Sentinel-2 satellite images were collected to compare the applicability of yield estimation methods. The abundance of different organs of rapeseed was extracted by the spectral mixture analysis (SMA) technology, which was multiplied by vegetation indices (VIs) respectively to estimate the yield. Results For the UAV-scale, the product of VIs and leaf abundance (AbdLF) was closely related to rapeseed yield, which was better than the VIs models for yield estimation, with the coefficient of determination (R2) above 0.78. The yield estimation models of the product of normalized difference yellowness index (NDYI), enhanced vegetation index (EVI) and AbdLF had the highest accuracy, with the coefficients of variation (CVs) below 10%. For the satellite scale, most of the estimation models of the product of VIs and rapeseed AbdLF were also improved compared with the VIs models. The yield estimation models of the product of AbdLF and renormalized difference VI (RDVI) and EVI (RDVI×AbdLF and EVI×AbdLF) had the steady improvement, with CVs below 13.1%. Furthermore, the yield estimation models of the product of AbdLF and normalized difference VI (NDVI), visible atmospherically resistant index (VARI), RDVI, and EVI had consistent performance at both UAV and satellite scales. Discussion The results showed that considering SMA could improve the limitation of using only VIs to retrieve rapeseed yield at the flowering stage. Our results indicate that the abundance of rapeseed leaves can be a potential indicator of yield prediction during the flowering stage.

Why it matches plant phenotyping methodsUAV・衛星マルチスペクトル画像とスペクトル混合分析を用いて、開花期の葉量から rapeseed の収量を推定し、複数スケールで精度比較・検証しているため、フェノタイピング手法が中心である。

abstractThe abundance of different organs of rapeseed was extracted by the spectral mixture analysis (SMA) technology, which was multiplied by vegetation indices (VIs) respectively to estimate the yield.

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning. →

Introduction Leaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production. Method In this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods). Results The average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed. Discussion The growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed.

Why it matches plant phenotyping methods画像取得装置と深層学習によってナタネの葉面積を非破壊・自動推定する手法を開発し、既存法との誤差比較および精度評価を行っており、植物表現型取得が研究の中心である。

abstractan image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed.

Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping →

In this study, we propose a high-throughput and low-cost automatic detection method based on deep learning to replace the inefficient manual counting of rapeseed siliques. First, a video is captured with a smartphone around the rapeseed plants in the silique stage. Feature point detection and matching based on SIFT operators are applied to the extracted video frames, and sparse point clouds are recovered using epipolar geometry and triangulation principles. The depth map is obtained by calculating the disparity of the matched images, and the dense point cloud is fused. The plant model of the whole rapeseed plant in the silique stage is reconstructed based on the structure-from-motion (SfM) algorithm, and the background is removed by using the passthrough filter. The downsampled 3D point cloud data is processed by the DGCNN network, and the point cloud is divided into two categories: sparse rapeseed canopy siliques and rapeseed stems. The sparse canopy siliques are then segmented from the original whole rapeseed siliques point cloud using the sparse-dense point cloud mapping method, which can effectively save running time and improve efficiency. Finally, Euclidean clustering segmentation is performed on the rapeseed canopy siliques, and the RANSAC algorithm is used to perform line segmentation on the connected siliques after clustering, obtaining the three-dimensional spatial position of each silique and counting the number of siliques. The proposed method was applied to identify 1457 siliques from 12 rapeseed plants, and the experimental results showed a recognition accuracy greater than 97.80%. The proposed method achieved good results in rapeseed silique recognition and provided a useful example for the application of deep learning networks in dense 3D point cloud segmentation.

Why it matches plant phenotyping methodsスマートフォン映像と3D点群、深層学習・幾何処理を用いてナタネの莢を自動検出・計数する手法が研究の中心であり、植物形態形質の取得方法を技術的に検証している。

abstractwe propose a high-throughput and low-cost automatic detection method based on deep learning to replace the inefficient manual counting of rapeseed siliques.

Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield. →

Background Studying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY. Results To achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019-2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R 2 ) were used to evaluate the performance of the algorithms. The best performance with all fifteen measured traits as inputs was achieved by the Nu-support vector regression algorithm with quadratic polynomial kernel function (R 2 = 0.860, RMSE = 0.266, MAE = 0.210). The multilayer perceptron neural network algorithm with identity activation function (MLPNN-Identity) using three traits obtained from stepwise and backward selection methods appeared to be the most efficient combination of algorithms and feature selection methods (R 2 = 0.843, RMSE = 0.283, MAE = 0.224). Feature selection suggested that the set of pods per plant and days to physiological maturity along with plant height or first pod height from the ground are the most influential traits in predicting rapeseed SY. Conclusion The results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed.

Why it matches plant phenotyping methods機械学習と特徴選択による種子収量という植物形質の予測手法を比較・評価し、育種への再利用可能な推定ワークフローを中心に扱っているため。

abstractOur main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY.

New insight into the genetic basis of oil content based on noninvasive three-dimensional phenotyping and tissue-specific transcriptome in Brassica napus →

Abstract Background Increasing seed oil content is the most important breeding goal in Brassica napus , and phenotyping is crucial to dissect its genetic basis in crops. To date, QTL mapping for oil content has been based on whole seeds, and the lipid distribution is far from uniform in different tissues of seeds in B. napus . In this case, the phenotype based on whole seeds was unable to sufficiently reveal the complex genetic characteristics of seed oil content. Results Here, the three-dimensional (3D) distribution of lipid was determined for B. napus seeds by magnetic resonance imaging (MRI) and 3D quantitative analysis, and ten novel oil content-related traits were obtained by subdividing the seeds. Based on a high-density genetic linkage map, 35 QTLs were identified for 4 tissues, the outer cotyledon (OC), inner cotyledon (IC), radicle (R) and seed coat (SC), which explained up to 13.76% of the phenotypic variation. Notably, 14 tissue-specific QTLs were reported for the first time, 7 of which were novel. Moreover, haplotype analysis showed that the favorable alleles for different seed tissues exhibited cumulative effects on oil content. Furthermore, tissue-specific transcriptomes revealed that more active energy and pyruvate metabolism influenced carbon flow in the IC, OC and R than in the SC at the early and middle seed development stages, thus affecting the distribution difference in oil content. Combining tissue-specific QTL mapping and transcriptomics, 86 important candidate genes associated with lipid metabolism were identified that underlie 19 unique QTLs, including the fatty acid synthesis rate-limiting enzyme-related gene CAC2 , in the QTLs for OC and IC. Conclusions The present study provides further insight into the genetic basis of seed oil content at the tissue-specific level.

Why it matches plant phenotyping methodsMRIと3D定量解析を用いて種子組織別の脂質分布から複数の油含量形質を抽出しており、植物フェノタイピング手法の適用が研究の中心的要素です。

titlenoninvasive three-dimensional phenotyping

BR-Net: Band reweighted network for quantitative analysis of rapeseed protein spectroscopy. →

Compared with the complexity of chemical methods, near-infrared spectroscopy (NIRS) is widely used in the detection of protein content because of its advantages of being fast and non-destructive. Aiming to tackle the problem that the raw near-infrared spectroscopy contains many redundant wavelengths, which affects the accuracy of quantitative prediction and requires expertise to process, we propose an end-to-end network: Band Reweighted Network (BR-Net) that automates wavelength reweighted and quantitative prediction of protein content in rapeseed. Unlike extracting part of wavelengths by the traditional wavelength selection methods, BR-Net retains all spectral wavelengths and assigns different weights to the wavelengths to express the correlation with the corresponding concentration, which enables wavelength selection without ignoring the information contained in the less relevant wavelengths. We compare BR-Net with traditional selection methods such as SPA, LARS, CARS, and UVE to verify its efficiency and robustness, finding that the R 2 of the training set and test set are 0.9797 and 0.9215, the RMSEC and RMSEP are 0.4053 and 0.8501, respectively, and the RPD is 3.5686, which prove BR-Net outperforms all the traditional methods. The network described here is universally applicable to a variety of NIR quantitative analyses.

Why it matches plant phenotyping methodsラプシード種子のタンパク質含量という植物形質をNIRスペクトルから定量する手法を開発し、既存手法と比較検証しており、フェノタイピング手法が研究の中心である。

abstractwe propose an end-to-end network: Band Reweighted Network (BR-Net) that automates wavelength reweighted and quantitative prediction of protein content in rapeseed.

Integrated Phenomics and Genomics reveals genetic loci associated with inflorescence growth in Brassica napus →

A fundamental challenge to the production of climate-resilient crops is how to measure dynamic yield-relevant responses to the environment, such as growth rate, at a scale which informs mechanistic understanding and accelerates breeding. The timing, duration and architectural characteristics of inflorescence growth are crucial for optimising crop productivity and have been targets of selection during domestication. We report a robust and versatile procedure for computationally assessing environmentally-responsive flowering dynamics. In the oilseed crop, Brassica napus, there is wide variation in flowering response to winter cold (vernalization). We subjected a diverse set of B. napus accessions to different vernalization temperatures and monitored shoot responses using automated image acquisition. We developed methods to computationally infer multiple aspects of flowering from this dynamic data, enabling characterisation of speed, duration and peaks of inflorescence development across different crop types. We input these multiple traits to genome- and transcriptome-wide association studies, and identified potentially causative variation in a priori phenology genes (including EARLY FLOWERING3) for known traits and in uncharacterised genes for computed traits. These results could be used in marker assisted breeding to design new ideotypes for improved yield and better adaptation to changing climatic conditions.

Why it matches plant phenotyping methods自動画像取得と計算手法により、開花・花序成長の動態形質を推定する方法を開発しており、表現型取得・抽出が研究の中心である。

abstractWe report a robust and versatile procedure for computationally assessing environmentally-responsive flowering dynamics.

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany →

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.

Why it matches plant phenotyping methodsLandsat-MODIS時空間融合によるNDVI取得・検証が研究の中心で、作物収量推定という植物状態・形質の推定に技術的に利用・評価されているため、地域推定を含むが植物フェノタイピング手法として含める。

abstractA synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019.

Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China →

To explore the Hyperspectral Estimation Method for estimating the chlorophyll content of rape leaves, so as to provide a scientific basis for rapid and nondestructive monitoring of the chlorophyll content of rape crops in Northwest China.Taking the rapeseed crops in the northwest region as the research object, through the correlation analysis of the SPAD value and the spectral parameters of the rape leaves, the spectral parameters sensitive to SPAD were screened, and the single factor model,the partial least square regression model (PLSR) and BP neural network model optimized by genetic algorithm based on multiple linear stepwise regression based on the spectral parameters were constructed respectively and were compared.The results showed that: 1) The general trend of the spectral curve of rape leaves was the same, and the spectral reflectance decreased with the increase of chlorophyll content; 2) The correlation of seven spectral parameters involved in the modeling was above 0.770, all of which reached significant correlation at 0.01 level; 3) In each growth period, the BP neural network model optimized by genetic algorithm based on multiple linear stepwise regression is the optimal model. The modeling R 2 is above 0.77, and the maximum can reach 0.91. It is verified that R 2 is above 0.73, the maximum can reach 0.92, RMSE is between 1.32–3.22, RE is between 2.50% − 4.49%. BP neural network model optimized by genetic algorithm based on multiple linear stepwise regression is an inversion method which can estimate the SPAD value of rape leaves accurately and quickly.

Why it matches plant phenotyping methodsラップ葉のクロロフィル含量という植物形質を、ハイパースペクトル計測と回帰・ニューラルネットワークで推定する手法の構築および検証が研究の中心である。

abstractTo explore the Hyperspectral Estimation Method for estimating the chlorophyll content of rape leaves

Phenotyping of Silique Morphology in Oilseed Rape Using Skeletonization with Hierarchical Segmentation →

Silique morphology is an important trait that determines the yield output of oilseed rape ( Brassica napus L .). Segmenting siliques and quantifying traits are challenging because of the complicated structure of an oilseed rape plant at the reproductive stage. This study aims to develop an accurate method in which a skeletonization algorithm was combined with the hierarchical segmentation (SHS) algorithm to separate siliques from the whole plant using 3-dimensional (3D) point clouds. We combined the L1-median skeleton with the random sample consensus for iteratively extracting skeleton points and optimized the skeleton based on information such as distance, angle, and direction from neighborhood points. Density-based spatial clustering of applications with noise and weighted unidirectional graph were used to achieve hierarchical segmentation of siliques. Using the SHS, we quantified the silique number (SN), silique length (SL), and silique volume (SV) automatically based on the geometric rules. The proposed method was tested with the oilseed rape plants at the mature stage grown in a greenhouse and field. We found that our method showed good performance in silique segmentation and phenotypic extraction with R 2 values of 0.922 and 0.934 for SN and total SL, respectively. Additionally, SN, total SL, and total SV had the statistical significance of correlations with the yield of a plant, with R values of 0.935, 0.916, and 0.897, respectively. Overall, the SHS algorithm is accurate, efficient, and robust for the segmentation of siliques and extraction of silique morphological parameters, which is promising for high-throughput silique phenotyping in oilseed rape breeding.

Why it matches plant phenotyping methods3D点群からシリクを分離し、形態形質を自動抽出する手法の開発と性能評価が研究の中心であるため。

abstractThis study aims to develop an accurate method in which a skeletonization algorithm was combined with the hierarchical segmentation (SHS) algorithm to separate siliques from the whole plant using 3-dimensional (3D) point clouds.

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision. →

Global warming is a challenge for plants and pathogens, involving profound changes in the physiology of both contenders to adapt to the new environmental conditions and to succeed in their interaction. Studies have been conducted on the behavior of oilseed rape plants and two races (1 and 4) of the bacterium Xanthomonas campestris pv. campestris (Xcc) and their interaction to anticipate our response in the possible future climate. Symptoms caused by both races of Xcc were very similar to each other under any climatic condition assayed, although the bacterial count from infected leaves differed for each race. Climate change caused an earlier onset of Xcc symptoms by at least 3 days, linked to oxidative stress and a change in pigment composition. Xcc infection aggravated the leaf senescence already induced by climate change. To identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves. Classification accuracies were above 0.85 out of 1.0 in all cases, with k-nearest neighbor analysis and support vector machines performing best under the tested climatic conditions.

Why it matches plant phenotyping methods蛍光画像、植生指数、熱画像から無症状感染植物を分類する手法を構築・評価しており、植物病害状態の抽出が中心的な技術貢献である。

abstractTo identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves.

Mapping Crop Leaf Area Index and Canopy Chlorophyll Content Using UAV Multispectral Imagery: Impacts of Illuminations and Distribution of Input Variables →

Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と放射伝達モデルを用いて作物のLAI・群落クロロフィル含量を推定し、照明条件やLUT分布の影響を比較・検証しており、形質取得手法が研究の中心である。

titleMapping Crop Leaf Area Index and Canopy Chlorophyll Content Using UAV Multispectral Imagery: Impacts of Illuminations and Distribution of Input Variables

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves →

BACKGROUND: Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non‐essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near‐infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves. RESULTS: Two‐layer estimation models were established by combining visible–NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root‐mean‐square error for prediction of 5.8969 mg kg⁻¹. A pseudo‐color image was developed using this stacking model to visualize the content and distribution of Cd. CONCLUSION: The combination of visible–NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive. © 2022 Society of Chemical Industry.

Why it matches plant phenotyping methods油菜葉のCd含量という植物状態を、可視–NIRハイパースペクトル画像とアンサンブル学習で非破壊推定・可視化する方法が研究の中心であり、技術開発・検証に該当する。

abstractTwo‐layer estimation models were established by combining visible–NIR HSI with ensemble learning methods (stacking and blending).

Automated extraction of pod phenotype data from micro-computed tomography. →

Introduction Plant image datasets have the potential to greatly improve our understanding of the phenotypic response of plants to environmental and genetic factors. However, manual data extraction from such datasets are known to be time-consuming and resource intensive. Therefore, the development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial. Methods In this paper, a current gold standard computed vision method for detecting and segmenting objects in three-dimensional imagery (StartDist-3D) is applied to X-ray micro-computed tomography scans of oilseed rape ( Brassica napus ) mature pods. Results With a relatively minimal training effort, this fine-tuned StarDist-3D model accurately detected (Validation F1-score = 96.3%,Testing F1-score = 99.3%) and predicted the shape (mean matched score = 90%) of seeds. Discussion This method then allowed rapid extraction of data on the number, size, shape, seed spacing and seed location in specific valves that can be integrated into models of plant development or crop yield. Additionally, the fine-tuned StarDist-3D provides an efficient way to create a dataset of segmented images of individual seeds that could be used to further explore the factors affecting seed development, abortion and maturation synchrony within the pod. There is also potential for the fine-tuned Stardist-3D method to be applied to imagery of seeds from other plant species, as well as imagery of similarly shaped plant structures such as beans or wheat grains, provided the structures targeted for detection and segmentation can be described as star-convex polygons.

Why it matches plant phenotyping methods植物のX線マイクロCT画像から種子を検出・分割し、個数・サイズ・形状・間隔・位置を抽出する機械学習手法の開発と検証が中心であるため。

abstractthe development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial.

Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed. →

The main objective of this study is to develop an automatic classification model for winter rapeseed varieties, to assess seed maturity and damage based on seed colour using a convolutional neural network (CNN). A CNN with a fixed architecture was built, consisting of an alternating arrangement of five classes Conv2D, MaxPooling2D and Dropout, for which a computational algorithm was developed in the Python 3.9 programming language, creating six models depending on the type of input data. Seeds of three winter rapeseed varieties were used for the research. Each imaged sample was 20.000 g. For each variety, 125 weight groups of 20 samples were prepared, with the weight of damaged or immature seeds increasing by 0.161 g. Each of the 20 samples in each weight group was marked by a different seed distribution. The accuracy of the models' validation ranged from 80.20 to 85.60%, with an average of 82.50%. Higher accuracy was obtained when classifying mature seed varieties (average of 84.24%) than when classifying the degree of maturity (average of 80.76%). It can be stated that classifying such fine seeds as rapeseed seeds is a complex process, creating major problems and constraints, as there is a distinct distribution of seeds belonging to the same weight groups, which causes the CNN model to treat them as different.

Why it matches plant phenotyping methodsCNN画像解析手法を開発し、セイヨウアブラナ種子の品種、成熟度、損傷を画像から自動評価することが研究の中心であるため、植物表現型計測手法として収録する。

abstractThe main objective of this study is to develop an automatic classification model for winter rapeseed varieties, to assess seed maturity and damage based on seed colour using a convolutional neural network (CNN).

Quantification of the three-dimensional root system architecture using an automated rotating imaging system. →

BACKGROUND: Crop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time. RESULTS: We proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline. CONCLUSIONS: The results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops.

Why it matches plant phenotyping methods自動回転撮像システムと3D画像処理パイプラインを開発し、根系形態と局所根形質を自動定量する研究であり、植物フェノタイピング手法が中心である。

abstractWe proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage.

Prediction of Oleic Acid Content in Brassica napus L. Seeds Based on Hyperspectral Parameters at Seedling Stage: A New Method for Rapidly Screening Germplasm with Different Oleic Acid Content at Early Growth Stage of Rapeseed →

Background: Remote sensing prediction techniques are new methods to assist crop production and screen breeding materials, mainly to predict crop conditions. In rapeseed production and breeding, remote sensing can predict growth status and harvest quality through spectral reflection at seedling stage. Results: : We bulit a spectral early-prediction system (SEP) containing multiple feedback formulas and predicted the oleic acid content of rapeseeds at harvest stage through reverse validation. We established a recognition model for early prediction of oleic acid in range of view of 56-85% with a resolution of 1%. The Root Mean Square Error (RMSE) between prediction result and ideal model below 1 was found for verification measurements. Conclusion: Besides other practical features such as nondestructive collection of material information, simple operation, and the ability to accurate prediction of oleic acid per plant, the SEP system is a promising tool for rapid screening of different oleic acid breeding materials, offering an easy setup to process in breeding work or field production.

Why it matches plant phenotyping methods幼苗期のハイパースペクトル情報から個体の収穫時オレイン酸含量を推定するシステムを開発・検証しており、植物形質の非破壊取得と育種スクリーニングが中心です。

abstractWe bulit a spectral early-prediction system (SEP) containing multiple feedback formulas and predicted the oleic acid content of rapeseeds at harvest stage through reverse validation.

Spectral Unmixing for Label-Free, In-Liquid Characterization of Biomass Microstructure and Biopolymer Content by Coherent Raman Imaging. →

Characterization of lignocellulosic biomass microstructure with chemical specificity and under physiological conditions could provide invaluable insights to our understanding of plant tissue development, microstructure, origins of recalcitrance, degradation, and solubilization. However, most methods currently available are either destructive, are not compatible with hosting a physiological environment, or introduces exogenous probes, complicating their use for studying changes in microstructure and mechanisms of plant development, recalcitrance, or degradation in situ. To address these challenges, we here present a multi-modal chemically specific imaging technique based on coherent anti-Stokes Raman scattering (CARS) microspectroscopy with simplex maximization and entropy-based spectral unmixing enabling label-free, chemically specific characterization of plant microstructure in liquid. We describe how spatial drift of samples suspended in liquid can introduce artifacts in spectral unmixing procedures for single-frequency CARS and propose a mitigative strategy toward these effects using simultaneously acquired forward-scattered CARS signals and epi-detected autofluorescence. We further apply the technique for chemical and microstructural characterization of untreated and liquid hot water pretreated rapeseed straw by CARS and show how the framework can be extended for 3D imaging with chemical specificity. Finally, we provide examples of the intricate chemical and microstructural details recovered by this hybrid imaging technique, including discerning between primary and secondary cell walls, localization of aqueous components to cell lumina, and the presence of funnel-type pits in samples of Brassica napus .

Why it matches plant phenotyping methodsCARS顕微分光とスペクトルアンミキシングを組み合わせ、植物組織の微細構造・化学成分を非破壊かつ液中で画像化する手法を開発しており、植物表現型の取得が中心である。

abstractwe here present a multi-modal chemically specific imaging technique based on coherent anti-Stokes Raman scattering (CARS) microspectroscopy with simplex maximization and entropy-based spectral unmixing enabling label-free, chemically specific characterization of plant microstructure in liquid.

NAPPN Annual Conference Abstract: Automated root phenotyping via deep learning- based landmark detection using SLEAP →

A high-throughput image analysis pipeline was developed to facilitate root phenotyping by reducing time-consuming labeling while maintaining phenotyping accuracy. This pipeline leverages a deep learning-based tool named SLEAP (SLEAP Estimates Animal Poses) which is designed to automate the detection of distinct morphological landmarks. By training SLEAP to detect the root branch points, tips, and midline of each root imaged in a gel cylinder, we were able to robustly and efficiently recover the root system geometry. We trained models to identify these landmarks on primary, lateral, and seminal roots across a range of crop plants, including soybean, rice, canola, and pennycress. We find that our SLEAP models are robust across genotypes and experiments, enabling automated root system quantification at the rate of hundreds of plants per hour. Using predictions of root landmark locations, we developed Python-based pipelines to extract phenotypic traits, including tip depths, root lengths, convex hulls, root angles, measures of curviness, and lateral root distribution (available at https://github.com/talmolab/sleap-roots). In order to extract meaningful patterns from this high-dimensional description of plant phenotypes, we use machine learning-based methods for dimensionality reduction and manifold embedding, allowing us to capture the statistical structure of root phenotypes present in our screens. In future work, we will use these quantitative phenotypic traits as a predictor for root system traits that enhance carbon sequestration capabilities in genome-wide association studies.

Why it matches plant phenotyping methods深層学習による根のランドマーク検出と画像解析パイプラインを開発し、根系形態形質を自動抽出する研究であり、植物フェノタイピング手法が中心である。

abstractA high-throughput image analysis pipeline was developed to facilitate root phenotyping by reducing time-consuming labeling while maintaining phenotyping accuracy.

Enhanced Leaf Area Index Estimation With CROP-DualGAN Network →

Quantitative estimation of regional leaf area index (LAI) is an important basis for large-scale crop growth monitoring and yield estimation. With the development of deep learning, theoretically, the use of neural networks can effectively improve the accuracy of LAI estimation, but sufficient training samples are often required due to a large number of network parameters. In an actual regional LAI quantitative estimation, there are only a few samples, which is difficult to train in networks. Therefore, a crop dual-learning generative adversarial network (CROP-DualGAN) was proposed in this article for data enhancement of small samples to estimate regional LAI. The method uses dual learning to generate hyperspectral reflectance and corresponding LAI, including two groups of generative adversarial networks, in which the generator is used to generate data that conforms to the distribution of the training set, and the discriminator is used to judge the true or false generated samples. The generators and discriminators are constantly optimized in the confrontation so that the distribution of generated data is closer to that of training samples. In single crop type experiments, 30 training samples with enhanced in VGG16 achieved the R2of cereal, maize and rape seed as 0.921, 0.990 and 0.956, and in SSLLAI-Net achieved the R2of cereal, maize and rape seed as 0.971, 0.991 and 0.962. In multiple crop types experiments, the result is lower than individual crop estimation, but higher than that of without enhancement. Finally, non-parametric test is used to prove that most improvement in LAI estimation is significant, and the accuracy won’t decrease when improvement is not significant. In all, proposed method is universal and can effectively help benchmark models to improve regional LAI estimation accuracy with neural networks.

Why it matches plant phenotyping methods地域LAIという植物群落形質の推定を対象に、少数サンプルを拡張するCROP-DualGANとLAI推定精度を検証しており、表現型取得・抽出手法が中心である。

abstracta crop dual-learning generative adversarial network (CROP-DualGAN) was proposed in this article for data enhancement of small samples to estimate regional LAI.

Path analysis and near-infrared spectroscopy in canola crop →

ABSTRACT: This study measured the effect of the association between agronomic traits related to the yield of canola grains grown at different sowing dates through path analysis. Another objective was to obtain a method to predict the oil content in the grains, fitting a multivariate model through near-infrared (NIR) spectroscopy analysis. The experiment was conducted in the field using a randomized block design in plots subdivided by time, with four plots (sowing dates), six subplots (canola hybrids), and four replicates. In each hybrid, phenological observations were performed, and the grain yield was determined. The data were subjected to analysis of variance in the R environment using the F test at 5% probability. The oil content in the grains was determined by the traditional chemical method, and based on the NIR spectral signature of the grain samples, partial least squares regression (PLS-R) was established to estimate the oil content in the canola grains. The sowing dates influenced the production components and oil content of the grains of all hybrids. The trait number of grains in five plants (0.6857) and their height (0.4943) had greater estimates of positive correlations with grain yield, as well as higher values of positive direct effects on yield (0.2494 and 0.1595, respectively). The NIR technique combined with PLS-R was able to predict the oil content in the grains, resulting in good predictive models (R2 of 0.86 and root mean square error (RMSE) of 1.56 in external validation).

Why it matches plant phenotyping methodsカノーラ種子の油含量という植物形質を、NIRスペクトルとPLS-Rで推定する方法を構築し、外部検証しており、形質取得法が研究の中心的目的である。

abstractAnother objective was to obtain a method to predict the oil content in the grains, fitting a multivariate model through near-infrared (NIR) spectroscopy analysis.

Detection of lead content in oilseed rape leaves and roots based on deep transfer learning and hyperspectral imaging technology. →

The evaluation capability of hyperspectral imaging technology was studied for the forecasts of heavy metal lead concentration of oilseed rape plant. In addition, a transfer stacked auto-encoder (T-SAE) algorithm including two network methods, the dual-model T-SAE and the single-model T-SAE, was proposed in this paper. The hyperspectral images of oilseed rape leaf and root were acquired under different Pb stress concentrations. The entire region of the oilseed rape leaf (or root) was selected as the region of interest (ROI) to extract the spectral data, and standard normalized variable (SNV), first derivative (1st Der) and second derivative (2nd Der) were used to preprocess the ROI spectra. Besides, the principal component analysis (PCA) algorithm was used to reduce the dimensionality of the spectral data before and after preprocessing. Hence, the best pre-processed data was determined for subsequent research and analysis. Furthermore, the SAE deep learning networks were built based on the oilseed rape leaf data, oilseed rape root data, and the combined data of oilseed rape leaf and root based on the best pre-processed spectral data. Finally, the T-SAE models were obtained through transfer learning of the best SAE deep learning network. The results show that the best preprocessing algorithms of the oilseed rape leaf and root spectra were SNV and 1st Der algorithm, respectively. In addition, the prediction set recognition accuracy of the best T-SAE model of Pb stress gradient in oilseed rape plants was 98.75%. Additionally, the prediction set coefficient of determination of the best T-SAE model of the Pb content in the oilseed rape leaf and root data were 0.9215 and 0.9349, respectively. Therefore, a deep transfer learning method combined with hyperspectral imaging technology can effectively realize the the qualitative and quantitative detection of heavy metal Pb in oilseed rape plants.

Why it matches plant phenotyping methods油糧ナタネの葉・根におけるPb濃度を、ハイパースペクトル画像と転移学習で定量推定する手法を開発・評価しており、植物状態の取得・抽出が研究の中心である。

abstracta transfer stacked auto-encoder (T-SAE) algorithm including two network methods, the dual-model T-SAE and the single-model T-SAE, was proposed in this paper.

A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging. →

The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing algorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decomposition layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters R p 2 , RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals.

Why it matches plant phenotyping methods油糧菜葉の鉛含量という植物状態を蛍光ハイパースペクトル画像から推定する手法を、前処理・ウェーブレット変換・深層学習・回帰モデルとして開発し、性能評価しているため、植物フェノタイピング手法が中心である。

titleA deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging.

PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage →

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

Why it matches plant phenotyping methodsタイトルから、ナタネ個体の3D点群を対象とした植物セグメンテーション手法の開発が中心と判断でき、植物表現型取得の中核手法に該当します。

titlePST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves. →

Background Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves. Results Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg -1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd. Conclusion The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive. © 2022 Society of Chemical Industry.

Why it matches plant phenotyping methodsナタネ葉のCd含量という植物状態を、可視近赤外ハイパースペクトル画像とアンサンブル学習で非破壊推定・可視化する手法を開発しており、フェノタイピング手法が中心である。

abstractIt is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves.

High‐throughput phenotyping‐based quantitative trait loci mapping reveals the genetic architecture of the salt stress tolerance of Brassica napus →

Salt stress is a major limiting factor that severely affects the survival and growth of crops. It is important to understand the salt stress tolerance ability of Brassica napus and explore the underlying related genetic resources. We used a high-throughput phenotyping platform to quantify 2111 image-based traits (i-traits) of a natural population under three different salt stress conditions and an intervarietal substitution line (ISL) population under nine different stress conditions to monitor and evaluate the salt stress tolerance of B. napus over time. We finally identified 928 high-quality i-traits associated with the salt stress tolerance of B. napus. Moreover, we mapped the salt stress-related loci in the natural population via a genome-wide association study and performed a linkage analysis associated with the ISL population, respectively. These results revealed 234 candidate genes associated with salt stress response, and two novel candidate genes, BnCKX5 and BnERF3, were experimentally verified to regulate the salt stress tolerance of B. napus. This study demonstrates the feasibility of using high-throughput phenotyping-based quantitative trait loci mapping to accurately and comprehensively quantify i-traits associated with B. napus. The mapped loci could be used for genomics-assisted breeding to genetically improve the salt stress tolerance of B. napus.

Why it matches plant phenotyping methods高スループット画像表現型解析プラットフォームで多数の植物形質を定量し、その実行可能性と有用性を評価することが研究の中心であるため。

abstractWe used a high-throughput phenotyping platform to quantify 2111 image-based traits (i-traits)

Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm →

Rapeseed (Brassica napus L.) is an important oil-bearing cash crop. Effective identification of the rapeseed flowering date is important for yield estimation and disease control. Traditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective. In this study, red, green and blue (RGB) images of rapeseed flowering derived from unmanned aerial vehicles (UAVs) were acquired with a total of seventeen available orthomosaic images, covering the whole flowering period for 299 rapeseed varieties. Five different machine learning methods were employed to identify and to extract the flowering areas in each plot. The results suggested that the accuracy of flowering area extraction by the decision tree-based segmentation model (DTSM) was higher than that of naive Bayes, K-nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) in all varieties and flowering dates, with R² = 0.97 and root mean square error (RMSE) = 0.051 pixels/pixels. Data on the proportion of flowering area and its dynamics showed differences in the time and duration of each flowering date among varieties. All varieties were classified into four clusters based on k-means clustering analysis. There were significant differences in eight phenotypic parameters among the four clusters, especially in the time of maximum flowering ratio and the time entering the early and medium flowering dates. The results from this study could provide a basis for rapeseed breeding based on flowering dynamics.

Why it matches plant phenotyping methodsUAV画像と機械学習による rapeseed の開花面積・開花動態の抽出と精度比較が研究の中心であり、植物形質の取得手法を評価している。

abstractTraditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective.

Development of a Crop Spectral Reflectance Sensor →

In this study, a low-cost, self-balancing crop spectral reflectance sensor (CSRS) was designed for real-time, nondestructive monitoring of the spectral reflectance and vegetation index of crops such as tomato and rapeseed. The sensor had a field of view of 30°, and a narrow-band filter was used for light splitting. The filter’s full width at half-maximum was 10 nm, and the spectral bands were 710 nm and 870 nm. The sensor was powered by a battery and used WiFi for communication. Its software was based on the Contiki operating system. To make the sensor work in different light intensity conditions, the photoelectric conversion automatic gain circuit had a total of 255 combinations of amplification. The gimbal of the sensor was mainly composed of an inner ring and an outer ring. Under the gravity of the sensor, the central axis of the sensor remained vertical, such that the up-facing and down-facing photosensitive units stayed in the horizontal position. The mechanical components of the sensor were designed symmetrically to facilitate equal mass distribution and to meet the needs of automatic balancing. Based on the optical signal transmission process of the sensor and the dark-current characteristics of the photodetector, a calibration method was theoretically deduced, which improved the accuracy and stability of the sensor under different ambient light intensities. The calibration method is also applicable for the calibration of other crop growth information sensors. Next, the standard reflectance gray scale was taken as the measurement variable to test the accuracy of the sensor, and the results showed that the root mean square error of the reflectance measured by the sensor at 710 nm and 870 nm was 1.10% and 1.27%, respectively; the mean absolute error was 0.95% and 0.89%, respectively; the relative error was below 4% and 3%, respectively; and the coefficient of variation was between 1.0% and 2.5%. The reflectance data measured by the sensor under different ambient light intensities suggested that the absolute error of the sensor was within ±0.5%, and the coefficients of variation at the two spectral bands were 1.04% and 0.39%, respectively. With tomato and rapeseed as the monitoring targets, the proposed CSRS and a commercial spectroradiometer were used to measure at the same time. The results showed that the reflectance measured by the two devices was very close, and there was a linear relationship between the normalized difference vegetation index of the CSRS and that of the commercial spectroradiometer. The coefficient of determination (R2) for tomato and rapeseed were 0.9540 and 0.9110, respectively.

Why it matches plant phenotyping methods作物の反射率と植生指数を測定するセンサーを開発し、校正・精度評価・市販機との比較検証まで行っており、植物表現型取得法が研究の中心である。

abstracta low-cost, self-balancing crop spectral reflectance sensor (CSRS) was designed for real-time, nondestructive monitoring of the spectral reflectance and vegetation index of crops such as tomato and rapeseed

HSI-PP: A flexible open-source software for hyperspectral imaging-based plant phenotyping →

Hyperspectral imaging has become one of the most popular techniques for high-throughput plant phenotyping. Extracting and analyzing useful plant phenotypic traits from hyperspectral images represents a major bottleneck for plant science and breeding communities. This study aims to present a stand-alone easy-to-use software platform called HSI-PP to process and analyze hyperspectral images for high-throughput plant phenotyping. The HSI-PP software integrates pre-processing, feature extraction, and modeling functions. The application of HSI-PP is exemplified by investigating the response of different Arabidopsis thaliana genotypes to drought stress, and the impact of various imaging angles on predicting the canopy nitrogen content (CNC) of oilseed rape (Brassica napus L.). The results showed that HSI-PP can process 10 GB on an ordinary PC in time ranging from 30 to 73 min according to image size and the complexity of the pipeline. HSI-PP extracted multiple phenotyping traits (spectral, textural, and morphological) of Arabidopsis thaliana from a large image dataset (104 GB) within five hours. The fusion of these features achieved higher accuracy (94%) than only using spectral information (85%) as early as day 4 after drought stress treatment. For oilseed rape, about 384 GB image data was processed within eighteen hours, and it was found that the tilted imaging angle of 75° had the optimized PLSR fitting (0.83) to the ground truth. The results demonstrate that HSI-PP is a stand-alone, automated, and open-source hyperspectral image processing platform adapted to various applications in plant phenotyping without requiring professional programming skills to serve the plant research community.

Why it matches plant phenotyping methods植物フェノタイピング用のハイパースペクトル画像処理ソフトウェアを開発・提示し、特徴抽出、モデリング、処理性能、検証例を中心に扱っているため。

abstractThis study aims to present a stand-alone easy-to-use software platform called HSI-PP to process and analyze hyperspectral images for high-throughput plant phenotyping.

High-throughput unmanned aerial vehicle-based phenotyping provides insights into the dynamic process and genetic basis of rapeseed waterlogging response in the field →

Waterlogging severely affects the growth, development, and yield of crops. Accurate high-throughput phenotyping is important for exploring the dynamic crop waterlogging response in the field, and the genetic basis of waterlogging tolerance. In this study, a multi-model remote sensing phenotyping platform based on an unmanned aerial vehicle (UAV) was used to assess the genetic response of rapeseed (Brassica napus) to waterlogging, by measuring morphological traits and spectral indices over 2 years. The dynamic responses of the morphological and spectral traits indicated that the rapeseed waterlogging response was severe before the middle stage within 18 d after recovery, but it subsequently decreased partly. Genome-wide association studies identified 289 and 333 loci associated with waterlogging tolerance in 2 years. Next, 25 loci with at least nine associations with waterlogging-related traits were defined as highly reliable loci, and 13 loci were simultaneously identified by waterlogging tolerance coefficients of morphological traits, spectral indices, and common factors. Forty candidate genes were predicted in the regions of 13 overlapping loci. Our study provides insights into the understanding of the dynamic process and genetic basis of rapeseed waterlogging response in the field by a high-throughput UAV phenotyping platform. The highly reliable loci identified in this study are valuable for breeding waterlogging-tolerant rapeseed cultivars.

Why it matches plant phenotyping methodsUAVリモートセンシングによる形態形質・スペクトル指標の高スループット取得が研究の中心的手法であり、圃場での水害応答を反復評価している。

abstractAccurate high-throughput phenotyping is important for exploring the dynamic crop waterlogging response in the field

Nondestructive evaluation of Zn content in rape leaves using MSSAE and hyperspectral imaging. →

Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed rape industry. This study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI). The dropout mechanism was introduced to improve the stacked sparse autoencoder (SSAE) and named modified SSAE (MSSAE). MSSAE extracted deep spectral features of samples based on pixel-level spectral information (the wavelength range of the spectrum is 431-962 nm). Subsequently, the deep spectral features were applied as the inputs for support vector regression (SVR) and least squares support vector regression (LSSVR) to predict the Zn content in oilseed rape leaves. In addition, the successive projections algorithm (SPA) and the variable iterative space shrinkage approach (VISSA) were investigated as wavelength selection algorithms for comparison. The results showed that the MSSAE-LSSVR model had the best prediction performance (the coefficient of determination (R 2 ) and root mean square error (RMSE) of the prediction set were 0.9566 and 1.0240 mg/kg, respectively). The overall results showed that the MSSAE was able to extract the deep features of HSI data and validated the possibility of HSI combined with a DL method for nondestructive testing of Zn content in oilseed rape leaves.

Why it matches plant phenotyping methods油菜葉の亜鉛含量という植物形質を、ハイパースペクトル画像とMSSAE・回帰モデルで非破壊推定する手法が研究の中心であり、性能評価も実施している。

abstractThis study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI).

PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage →

Segmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, previous studies mainly focus on the hard voxelization-based or down-sampling-based methods, which are limited to segmenting simple plant organs. Segmentation of complex plant point clouds with a high spatial resolution still remains challenging. In this study, we proposed a deep learning network plant segmentation transformer (PST) to achieve the semantic and instance segmentation of rapeseed plants point clouds acquired by handheld laser scanning (HLS) with the high spatial resolution, which can characterize the tiny siliques as the main traits targeted. PST is composed of: (i) a dynamic voxel feature encoder (DVFE) to aggregate the point features with the raw spatial resolution; (ii) the dual window sets attention blocks to capture the contextual information; and (iii) a dense feature propagation module to obtain the final dense point feature map. The results proved that PST and PST-PointGroup (PG) achieved superior performance in semantic and instance segmentation tasks. For the semantic segmentation, the mean IoU, mean Precision, mean Recall, mean F1-score, and overall accuracy of PST were 93.96%, 97.29%, 96.52%, 96.88%, and 97.07%, achieving an improvement of 7.62%, 3.28%, 4.8%, 4.25%, and 3.88% compared to the second-best state-of-the-art network PAConv. For instance segmentation, PST-PG reached 89.51%, 89.85%, 88.83% and 82.53% in mCov, mWCov, mPerc90, and mRec90, achieving an improvement of 2.93%, 2.21%, 1.99%, and 5.9% compared to the original PG. This study proves that the deep-learning-based point cloud segmentation method has a great potential for resolving dense plant point clouds with complex morphological traits.

Why it matches plant phenotyping methodsラッペシード植物の高解像度点群から形態形質を抽出するセグメンテーション手法を開発し、性能評価しているため、植物フェノタイピング手法が中心である。

abstractSegmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping.

Assessment oil composition and species discrimination of Brassicas seeds based on hyperspectral imaging and portable near infrared (NIR) spectroscopy tools and chemometrics →

Brassica is a genus of oilseed plants mainly used to produce edible oils, modified lipids, industrial oils, and biofuels. Oil and fatty acid content are the main chemical indicators for Brassicas seed quality (e.g. low content of erucic acid indicate seeds appropriate for food industry, while high contents indicate are suitable in the cosmetic, pharmaceutical and fuel industry). The goal of this work was to implement and compare the portable Near Infrared spectroscopy (NIRS) and NIR-Hyperspectral Imaging (NIR-HSI) based analytical methods to quantify oil content and fatty acid and classify seeds species. Spectral data was analyzed by non-supervised (principal component analysis, PCA) and supervised (partial least square regression, PLSR, and discriminant analysis, PLS-DA) chemometrics tools in order to generate new prediction models. PLS-DA analysis showed satisfactory discrimination between Brassicas species, with correct classification rate of 94.9 and 100 % for portable NIR spectrometer and NIR-HSI devices, respectively, in external validation. The best prediction models were obtained based on interval selection (iPLS) for erucic acid, MUFAs and PUFAs using NIR-HSI spectra. Although these NIR-HSI models have better results than the NIR spectrometer, both the NIR and NIR-HSI devices could be adapted to quantify the oil content and composition in Brassica seeds, according to the needs of the industry or the consumer.

Why it matches plant phenotyping methodsBrassica種子の油含量・脂肪酸組成という植物器官形質を、NIRおよびハイパースペクトル画像で定量する手法を実装・比較し、外部検証しているため、フェノタイピング手法が中心である。

abstractThe goal of this work was to implement and compare the portable Near Infrared spectroscopy (NIRS) and NIR-Hyperspectral Imaging (NIR-HSI) based analytical methods to quantify oil content and fatty acid and classify seeds species.

Synergy of optical and synthetic aperture radar data for early-stage crop yield estimation: a case study over a state of Germany →

Traditional crop cutting experiment-based yield estimation method captures the regional yield variability but lacks field-level information. Satellite images hold enormous crop information at finer spatial resolution. Crop yield mapping with optical images is particularly challenging if cloud-free images are unavailable during the crucial crop developmental stages. All-weather availability and sensitivity to crop structure, dielectric properties make synthetic aperture radar (SAR) images an excellent resource for yield estimation. Both types of data provide complementary information about crop conditions. A random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation. We utilized the crop harvest and quality survey (BEE) yield data set collected by the Hessisches Statistisches Landesamt (HSL), Wiesbaden, Germany, over 490 fields. We prepared 20 m resolution yield maps for winter wheat, winter barley, winter rye and winter rapeseed. Input features for the yield estimation model are selected based on the prior knowledge of remote sensing of vegetation. Baseline random forest regression models are developed for all the four crop types with optical and SAR input features. An optimized random forest regression model with genetic algorithm-based feature selection results in performance improvement. Dissimilarity in genetic algorithm selected image features highlights the significance of crop-specific feature selection for yield estimation. The optimized models reliably estimate yield by achieving correlation coefficient (r) of 0.65–0.86, mean absolute error 0.93–1.16 t ha–1 and root mean square error 1.12–1.56 t ha–1 with BEE yield on testing data set. The proposed models could estimate the intra-field yield variation when winter wheat, winter barley, winter rye were in the shooting phase to the beginning of ear-shifting, and winter rapeseed began to flower or was already flowering. These results demonstrate the merits of our model for early-stage crop yield estimation at the field level with mono-temporal image and adaptability for the cropping season with high cloud cover.

Why it matches plant phenotyping methods衛星光学・SAR画像から圃場内の作物収量を推定するモデルを開発・検証しており、植物の収量という形質の取得・推定手法が研究の中心である。

abstractA random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation.

EPSA-YOLO-V5s: A novel method for detecting the survival rate of rapeseed in a plant factory based on multiple guarantee mechanisms →

As one of the important products of modern agricultural development, plant factories can provide a suitable environment for the growth and development of crops. Intelligently detecting the survival rate of crops in multiple key growth stages can not only improve the space utilization of plant factory, but also help increase crop yields. In this work, our main task is to use a novel method to detect the survival rate of rape seedlings at multiple growth stages in the plant factory. First of all, for the key growth stages where seedlings may die, we obtained image datasets of the whole process of seed germination, the early, and the middle stage of seedling transplanting. Second, we used the state-of-the-art method YOLO-V5s to construct the target detection model for the rape seedling dataset of the three key growth stages, and achieved good performance of the model mAP@0.5 as 0.994, 0.996, and 0.996 respectively. Finally, in order to construct a model suitable for the detection of the survival rate of rape in multiple key growth stages, we propose a new method called ESPA-YOLO-V5s, and achieved a good model performance with a mAP@0.5 of 0.996. The experimental results prove that our method has laid a good foundation for the survival rate detection of the key growth stages of plant.

Why it matches plant phenotyping methodsナタネ幼苗の生存状態・生存率を画像から推定するYOLOベースの手法を開発し、複数生育段階で性能評価しており、植物表現型取得が中心である。

abstractour main task is to use a novel method to detect the survival rate of rape seedlings at multiple growth stages in the plant factory.

Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning. →

Background Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts. Methods To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and conclusion The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33[Formula: see text]) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

Why it matches plant phenotyping methodsUAV画像と深層学習を用いて rapeseed の凍害耐性という植物状態を自動認識する手法が研究の中心であり、技術比較と精度評価も行っている。

abstractwe propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images

Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method. →

To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position. Since the orthomosaic images are stitched from a large number of smaller images, there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot. To overcome this, we have developed a three-level hierarchical optimization method. First, the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area. Then, columns of microplots are repositioned, constrained by their expected spacing. Finally, the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation, minimizes spacing variance between microplots, and maximizes each microplot's alignment relative to other microplots in the same row and column. The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials. The algorithm was able to detect 99.7% of microplots for canola and 99% for wheat. The automatically segmented microplots were compared to ground truth segmentations, resulting in an average DSC of 91.2% and 89.6% across all microplots and orthomosaics in the canola and wheat datasets.

Why it matches plant phenotyping methodsUAV画像から育種試験のマイクロプロットを自動検出・分割する画像解析手法を開発し、正解データと比較して性能検証しているため、植物フェノタイピング手法が中心です。

abstractTo perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps.

Comparison of non-subjective relative fungal biomass measurements to quantify the Leptosphaeria maculans-Brassica napus interaction. →

Background Blackleg disease, caused by the fungal pathogen Leptosphaeria maculans, is a serious threat to canola (Brassica napus) production worldwide. Quantitative resistance to this disease is a highly desirable trait but is difficult to precisely phenotype. Visual scores can be subjective and are prone to assessor bias. Methods to assess variation in quantitative resistance more accurately were developed based on quantifying in planta fungal biomass, including the Wheat Germ Agglutinin Chitin Assay (WAC), qPCR and ddPCR assays. Results Disease assays were conducted by inoculating a range of canola cultivars with L. maculans isolates in glasshouse experiments and assessing fungal biomass in cotyledons, petioles and stem tissue harvested at different timepoints post-inoculation. PCR and WAC assay results were well correlated, repeatable across experiments and host tissues, and able to differentiate fungal biomass in different host-isolate treatments. In addition, the ddPCR assay was shown to differentiate between L. maculans isolates. Conclusions The ddPCR assay is more sensitive in detecting pathogens and more adaptable to high-throughput methods by using robotic systems than the WAC assay. Overall, these methods proved accurate and non-subjective, providing alternatives to visual assessments to quantify the L. maculans-B. napus interaction in all plant tissues throughout the progression of the disease in seedlings and mature plants and have potential for fine-scale blackleg resistance phenotyping in canola.

Why it matches plant phenotyping methodsカノーラ黒脚病に対する量的抵抗性の表現型評価を目的に、WAC、qPCR、ddPCRによる菌体量測定法を比較・検証しており、病害状態の非主観的な表現型取得が中心である。

abstractMethods to assess variation in quantitative resistance more accurately were developed based on quantifying in planta fungal biomass, including the Wheat Germ Agglutinin Chitin Assay (WAC), qPCR and ddPCR assays.

Analysis of Cruciferin Content in Whole Seeds of Brassica napus L. by Near-Infrared Spectroscopy →

Globally, there is an increasing demand for sources of plant-based protein. While Brassica napus L. is an important oilseed crop worldwide, there is also interest in improving its ability to serve as a valuable source of plant-based protein. Cruciferin, a seed storage protein that makes up 60% of the protein found in mature seeds of B. napus , is of interest for human consumption as a source of protein and as an ingredient in food products due to its functional properties. Existing methods for quantification of cruciferin protein are often time consuming and destroy the seed. This study explored the potential for the measurement of cruciferin protein content in whole seeds of B. napus by near-infrared spectroscopy (NIRS), to allow for efficient and non-destructive screening of breeding material. An enzyme-linked immunosorbent assay (ELISA)-based reference method was utilized to assess cruciferin content in a diverse population of B. napus . Scanning of whole seed samples produced spectra that were used to develop NIRS calibration equations. Statistical analysis of the calibration results indicated that the NIRS equations developed are poorly suited for prediction of cruciferin content.

Why it matches plant phenotyping methodsBrassica napus種子の貯蔵タンパク質含量をNIRSで非破壊推定する校正式を開発・評価しており、植物形質取得法が研究の中心です。

abstractThis study explored the potential for the measurement of cruciferin protein content in whole seeds of B. napus by near-infrared spectroscopy (NIRS), to allow for efficient and non-destructive screening of breeding material.

Automatic Freezing-Tolerant Rapeseed Material Recognition Using UAV Images and Deep Learning →

Abstract Background: Freezing injury is a serious and common damage that occurs to winter rapeseed during the overwintering period. The freezing injury directly reduces the rapeseed yield and causes serious economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipment. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of experts. Methods: To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning technology and unmanned aerial vehicle (UAV) images captured by a consumer drone. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning technology. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials collected by the Phantom 4 Pro. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and Conclusion: The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33%) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based approach significantly outperforms the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

Why it matches plant phenotyping methodsUAV画像と深層学習により、ナタネの凍結耐性という植物状態を自動推定する手法を開発・比較評価しており、表現型取得・抽出が研究の中心である。

abstractwe propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning technology and unmanned aerial vehicle (UAV) images

BiophyNet: A Regression Network for Joint Estimation of Plant Area Index and Wet Biomass From SAR Data →

In this study, we propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean. The PAI and wet biomass have considerable importance for crop growth stage mapping and monitoring. RADARSAT-2 quad-pol data along within situmeasurements of canola and soybean obtained from the SMAPVEX16 campaign over Manitoba, Canada, are utilized for evaluating the efficiency and accuracy of the proposed estimation methodology. The analysis indicates promising results for the two crops with a correlation coefficient$(r)$in the range of 0.69–0.87. The results also confirm intercorrelation between the PAI and wet biomass for canola and soybean.

Why it matches plant phenotyping methodsSARデータから植物面積指数と湿潤バイオマスを推定するニューラルネットワーク手法を提案・評価しており、植物形質の取得方法が研究の中心である。

abstractwe propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean

A novel approach to identify the spectral bands that predict moisture content in canola and wheat →

Due to the relevance of agriculture in economy and human development, the inclusion of technology in this activity is of utmost importance, and moisture content prediction is relevant for assessing the degree of maturity of a crop, which relates to efficient harvesting and quality control. This paper presents an accurate deep learning model for the prediction of the moisture content of canola and wheat crops, based on hyperspectral images taken by several drone flights. This model serves as the starting point for a supervised band selection process that involves a novel approach based on a game-theory model-interpretability analysis. The deep learning model for moisture content prediction included a final ensemble of two branches for analysis of spatial and spectral features, and it reached a coefficient of determination of 0.916 and 0.818 for the canola and wheat test datasets, respectively. SHapley Additive exPlanations analysis allowed us to study the individual predictions of the models, which is the most important contribution of this paper because this approach could eventually lead to the design and implementation of more tailored software and hardware for the analysis of spectral information. The obtained results validate the idea that using this approach actually obtains the spectral bands that are important for this task, since they are similar to PCA results, and they fall on the NIR part of the spectrum, which is widely used in moisture measurement of agricultural products and vegetation analysis.

Why it matches plant phenotyping methodsドローン搭載ハイパースペクトル画像から作物の含水量を推定し、重要波長帯を選択する深層学習・解釈手法が研究の中心であるため、植物状態の計測手法として採用。

abstractThis paper presents an accurate deep learning model for the prediction of the moisture content of canola and wheat crops, based on hyperspectral images taken by several drone flights.

High-throughput phenotyping-based QTL mapping reveals the genetic architecture of the salt stress tolerance of Brassica napus →

Salt stress is a major limiting factor that severely affects the survival and growth of crops. It is important to understand the salt tolerance ability of Brassica napus and explore the underlying related genetic resources. We used a high-throughput phenotyping platform to quantify 2,111 image-based traits (i-traits) of a natural population under 3 different salt stress conditions and an intervarietal substitution line (ISL) population under 9 different stress conditions to monitor and evaluate the salt stress tolerance of B. napus over time. We finally identified 928 high-quality i-traits associated with the salt stress tolerance of B. napus. Moreover, we mapped the salt stress-related loci in the natural population via a genome-wide association study (GWAS) and performed a linkage analysis associated with the ISL population, respectively. The results revealed 234 candidate genes associated with salt stress response, and two novel candidate genes, BnCKX5 and BnERF3, were experimentally verified to regulate the salt stress tolerance of B. napus. This study demonstrates the feasibility of using high-throughput phenotyping-based QTL mapping to accurately and comprehensively quantify i-traits associated with B. napus. The mapped loci could be used for genomics-assisted breeding to genetically improve the salt stress tolerance of B. napus.

Why it matches plant phenotyping methods高スループット画像表現型解析プラットフォームで多数の画像形質を定量し、塩ストレス耐性の評価とQTL解析に用いる方法が研究の中心である。

abstractWe used a high-throughput phenotyping platform to quantify 2,111 image-based traits (i-traits)

Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera →

The three-dimensional reconstruction method using RGB-D camera has a good balance in hardware cost and point cloud quality. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a 3D reconstruction method using Azure Kinect to solve these inherent problems. Shoot color images, depth images and near-infrared images of the target from six perspectives by Azure Kinect sensor with black background. Multiply the binarization result of the 8-bit infrared image with the RGB-D image alignment result provided by Microsoft corporation, which can remove ghosting and most of the background noise. A neighborhood extreme filtering method is proposed to filter out the abrupt points in the depth image, by which the floating noise point and most of the outlier noise will be removed before generating the point cloud, and then using the pass-through filter eliminate rest of the outlier noise. An improved method based on the classic iterative closest point (ICP) algorithm is presented to merge multiple-views point clouds. By continuously reducing both the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the integral color point cloud. Many experiments on rapeseed plants show that the success rate of cloud registration is 92.5% and the point cloud accuracy obtained by this method is 0.789 mm, the time consuming of a integral scanning is 302 s, and with a good color restoration. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower when building a automatic scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of rapeseed and other crops phenotype.

Why it matches plant phenotyping methodsRGB-D画像による植物体の3D再構成・点群登録手法を開発し、精度・成功率・処理時間を検証しており、非破壊的な作物表現型計測が中心である。

abstractThis paper proposes a 3D reconstruction method using Azure Kinect to solve these inherent problems.

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations →

Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations. We extracted several radar features from polarimetric Synthetic Aperture Radar (SAR) data and Vegetation Indices (VIs) from optical images to model crops’ LAI and dry biomass. Then, the mutual correlations between these features and Random Forest feature importance were calculated. We considered two scenarios to estimate crop parameters. First, Machine Learning (ML) algorithms, e.g., Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized to estimate two crop biophysical parameters. To this end, crops’ dry biomass and LAI were estimated using three input data; (1) SAR polarimetric features; (2) spectral VIs; (3) integrating both SAR and optical features. Second, a deep artificial neural network was created. These input data were fed to the mentioned algorithms and evaluated using the in-situ measurements. These observations of three cash crops, including soybean, corn, and canola, have been collected over Manitoba, Canada, during the Soil Moisture Active Validation Experimental 2012 (SMAPVEX-12) campaign. The results showed that GB and XGB have great potential in parameter estimation and remarkably improved accuracy. Our results also demonstrated a significant improvement in the dry biomass and LAI estimation compared to the previous studies. For LAI, the validation Root Mean Square Error (RMSE) was reported as 0.557 m2/m2 for canola using GB, and 0.298 m2/m2 for corn using GB, 0.233 m2/m2 for soybean using XGB. RMSE was reported for dry biomass as 26.29 g/m2 for canola utilizing SVR, 57.97 g/m2 for corn using RF, and 5.00 g/m2 for soybean using GB. The results revealed that the deep artificial neural network had a better potential to estimate crop parameters than the ML algorithms.

Why it matches plant phenotyping methodsSAR・光学リモートセンシングと機械学習／深層学習を用いて、作物のLAIと乾燥バイオマスという植物形質を推定し、実測値で評価しており、形質取得・推定手法が研究の中心である。

abstractThis paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations.

Early prediction of the seed yield in winter oilseed rape based on the near-infrared reflectance of vegetation (NIRv) →

Demands for rape seeds oil rapidly increase in recent years. The empirical model based on the remotely sensed data provides an efficient approach to predict the rapeseed yield at small scale. The vegetation indices (VIs) derived from the remotely sensed data at the stage of the peak leaf area index (LAI) are usually the main determinant of the empirical model. The LAI of oilseed rape reaches the peak during the flowering stage. However, yellow flowers elevate the reflectance in the red band, making it difficult to capture the maximum LAI with the commonly-used VIs, such as the normalized difference vegetation index (NDVI). This study tested the hypothesis that the near-infrared reflectance of vegetation (NIRv) may be more sensitive to the LAI in the oilseed rape throughout different growth stages, particularly in the flowering stage, and thus may provide more accurate early prediction of the rapeseed yield as well as the above-ground dry mass (DM). In addition, a random forest (RF) regression model based on the multi-stage NIRv was built to predict the rapeseed yield. Three small-plot experiments with different nitrogen and potassium fertilizer treatments were conducted at two sites in Hubei province, China. NIRv and the studied VIs were derived from multi-spectral images captured by an unmanned aerial vehicle. NIRv was calculated with the DN values (NIRv_DN) and the reflectance values (NIRv_refl) of the near-infrared band separately. Results demonstrated that NIRv_DN and NIRv_refl were strongly and consistently correlated with the LAI before the flowering stage and in the flowering stage. In comparison, the relationships between the studied VIs and LAI in the flowering stage deviated from the relationships of all growth stages. The NIRv features and VIs were all significantly correlated with the above-ground DM, but the DM estimation by NIRv_DN had a lower RMSE value. The NIRv_DN and NIRv_refl in the flowering stage showed the strongest correlation with the rapeseed yield (Cali_ R² = 0.74 and Vali_RMSE = 389.84 kg/ha for NIRv_DN; Cali_ R² = 0.73 and Vali_RMSE = 410.85 kg/ha for NIRv_refl). The NIRv_DN, NIRv_refl, and the soil adjusted vegetation index (SAVI) in the budding stage were also strongly correlated with the rapeseed yield. The variable importance score derived from the RF model corroborated the significant contribution of the NIRv in the flowering and budding stage to the yield prediction. The RF model using the NIRv_DN in the flowering and budding stage achieved the high accuracy of the rapeseed yield prediction (Cali_ R² = 0.91, Cali_RMSE = 248.48 kg/ha, and Vali_RMSE = 282.44 kg/ha). Results from this research demonstrated the great potential of the NIRv in the flowering stage to provide the accurate early prediction of the rapeseed yield.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNIRvを抽出し、LAI・乾物量・収量を推定する手法を比較・検証しており、植物形質取得と予測ワークフローが研究の中心である。

abstractNIRv and the studied VIs were derived from multi-spectral images captured by an unmanned aerial vehicle.

Assessing the Effect of Phenotyping Scoring Systems and SNP Calling and Filtering Parameters on Detection of QTL Associated with Reaction of Brassica napus to Sclerotinia sclerotiorum →

The polyploid nature of canola (Brassica napus) represents a challenge for the accurate identification of single-nucleotide polymorphisms (SNPs) and the detection of quantitative trait loci (QTL). In this study, combinations of eight phenotyping scoring systems and six SNP calling and filtering parameters were evaluated for their efficiency in detection of QTL associated with response to Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, in two doubled haploid canola mapping populations. Most QTL were detected in lesion length, relative areas under the disease progress curve (rAUDPC) for lesion length, and binomial-plant mortality data sets. Binomial data derived from lesion size were less efficient in QTL detection. Inclusion of additional phenotypic sets to the analysis increased the numbers of significant QTL by 2.3-fold; however, the continuous data sets were more efficient. Between two filtering parameters used to analyze genotyping-by-sequencing data, imputation of missing data increased QTL detection in one population with a high level of missing data but not in the other. Inclusion of segregation-distorted SNPs increased QTL detection but did not impact their R 2 values significantly. In all, 12 of 16 detected QTL were on chromosomes A02 and C01, and the rest were on A07, A09, and C03. Marker A02-7594120, associated with a QTL on chromosome A02, was detected in both populations. Results of this study suggest that the impact of genotypic variant calling and filtering parameters may be population dependent while deriving additional phenotyping scoring systems such as rAUDPC datasets and mortality binary may improve QTL detection efficiency. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Why it matches plant phenotyping methodsカノーラの菌核病反応を測定する8種類の表現型スコアリング体系を比較評価し、病徴・病害進展・枯死データがQTL検出に与える影響を検証しているため、表現型取得法の評価が中心的です。

abstractcombinations of eight phenotyping scoring systems and six SNP calling and filtering parameters were evaluated for their efficiency in detection of QTL associated with response to Sclerotinia stem rot

Phenotyping Flowering in Canola (Brassica napus L.) and Estimating Seed Yield Using an Unmanned Aerial Vehicle-Based Imagery →

Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola ( Brassica napus L.) flowers, the bright yellow flowers, indeterminately increase over a protracted period. Flower production of canola plays an important role in yield determination. Yellowness of canola petals may be a critical reflectance signal and a good predictor of pod number and, therefore, seed yield. However, quantifying flowering based on traditional visual scales is subjective, time-consuming, and labor-consuming. Recent developments in phenotyping technologies using Unmanned Aerial Vehicles (UAVs) make it possible to effectively capture crop information and to predict crop yield via imagery. Our objectives were to investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes, including 53 B. napus lines, two Brassica carinata lines, and a Brassica juncea variety, were grown near Saskatoon, SK, Canada from 2016 to 2018 and near Melfort and Scott, SK, Canada in 2017. Aerial imagery with geometric and radiometric corrections was collected through the flowering stage using a UAV mounted with a multispectral camera. We found that the normalized difference yellowness index (NDYI) was a useful vegetation index for representing canola yellowness, which is related to canola flowering intensity during the full flowering stage. However, the flowering pixel number estimated by the thresholding method improved the ability of NDYI to detect yellow flowers with coefficient of determination ( R 2 ) ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel numbers integrated over time covers more growth information and can be a good predictor of pod number and thus, canola yield with R 2 up to 0.42. These results indicate that NDYI-based flowering pixel numbers can perform well in estimating flowering intensity. Integrated flowering intensity extracted from imagery over time can be a potential phenotype associated with canola seed yield.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像とNDYI・閾値処理により、カノーラの開花強度を抽出・推定し、技術性能を評価した方法中心の研究。

abstractOur objectives were to investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield.

Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping →

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

Why it matches plant phenotyping methods植物フェノタイピング用UAV画像の超解像モデルと前処理ワークフローを開発・比較検証しており、植物形質測定への適用性が中心的である。

titleSpatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

Crop height estimation based on UAV images: Methods, errors, and strategies →

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for determining the dynamic phenotypic traits of crops in the field in a rapid and cost-effective manner. Crop height is a common and important phenotypic trait, and its acquisition with high accuracy usually requires spatial auxiliary (SA) information, such as a digital terrain model in the early growing season, digital surface models later in the season, ground control points, and ground truth of crop height. The reasonable selection of SA information involves balancing the cost and accuracy of crop height acquisition, but this problem has not been systematically studied and it needs to be resolved urgently in the agricultural industry. In this study, we compared four rapeseed height estimation methods using UAV images collected at three growth stages based on the structure from motion algorithm, where one method had complete data and the other three had incomplete SA information. To reduce the crop height estimation errors with incomplete data, improved methods were developed to construct the missing SA information. The optimum results were obtained using complete SA information, where R² was 0.932 and the root mean square error (RMSE) was 0.026 m. For crop height acquisition using incomplete data, the R² values could be controlled above 0.445 and RMSE below 0.146 m. In this study, systematic strategies were developed for selecting appropriate methods to acquire crop height with reasonable accuracy while balancing the cost requirement for use in scientific research and agricultural production.

Why it matches plant phenotyping methodsUAV画像とSfMを用いた作物高という植物形質の取得法を比較・改良し、誤差と精度を評価しているため、フェノタイピング手法が研究の中心です。

abstractCrop height is a common and important phenotypic trait

3D Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera →

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower and it is easy to automate the scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of crop phenotype.

Why it matches plant phenotyping methodsRGB-Dカメラによる植物3D再構成法を開発・検証し、作物表現型の非破壊測定への利用可能性を評価しており、表現型取得手法が研究の中心である。

abstractThis paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems.

A New Hyperspectral Redundant Band Detection Method Based on Local Hurst Exponent →

Abstract Hyperspectrum reflectance is a curve in a certain wavelength range. Its complex dynamic structure reflects rich information of the object at variable bands. However, the potential redundancy will seriously affect accurate extraction of spectral features, therefore, information redundancy detection is a critical pretreatment for spectral analysis. In this paper, by using the local detrended fluctuation analysis, we propose a new method to detect the redundant bands. The method focuses on the spectral auto-correlation represented by local Hurst exponent in moving windows. Thus, the redundant band can be determined by the comparison of auto-correlation between two adjacent windows. To test our method, using the fractal feature of the removing redundant bands as augment, rapeseed oleic acid's prediction model is constructed based on random decision forest method. As comparison, the same feature of the original spectrum is also employed as augment for the model. The result shows that the feature of removing the redundant bands will bring better model performance than the feature of original spectrum does.

Why it matches plant phenotyping methods局所Hurst指数を用いたハイパースペクトル冗長帯検出法を開発し、ナタネのオレイン酸予測で性能検証しているため、植物形質推定の方法開発が中心である。

abstractwe propose a new method to detect the redundant bands

Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment →

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we propose three polarimetric descriptors: the pseudo scattering-type parameter (θc), the pseudo scattering entropy parameter (Hc), and the co-pol purity parameter (mc) from dual-pol S1 GRD SAR data. We also introduce a novel unsupervised clustering framework using Hc and θc with six clustering zones to represent various scattering mechanisms. We implemented the proposed algorithm on the cloud-based Google Earth Engine (GEE) platform for Sentinel-1 SAR data. We have shown the sensitivity of these descriptors over a time series of data for wheat and canola crops at a test site in Canada. From the leaf development stage to the flowering stage for both crops, the pseudo scattering-type parameter θc changes by approximately 17°. Moreover, within the entire phenology window, both mc and Hc varies by about 0.6. The effectiveness of θc and Hc to cluster the phenological stages for the two crops is also evident from the clustering plot. During the leaf development stage, about 90 % of the sampling points were clustered into the low to medium entropy scattering zone for both the crops. Throughout the flowering stage, the entire cluster shifted into the high entropy vegetation scattering zone. Finally, during the ripening stage, the clusters of sample points were split between the high entropy vegetation scattering zone and the high entropy distributed scattering zone, with > 55 % of the sampling points in the high entropy distributed scattering zone. This innovative clustering framework will facilitate the operational use of S1 GRD SAR data for agricultural applications. This article is submitted to ISPRS Journal of Photogrammetry and Remote Sensing

Why it matches plant phenotyping methodsSentinel-1 SARから作物の生育・フェノロジー段階を推定する極化記述子とクラスタリング手法を開発・適用しており、植物状態の取得・抽出が中心である。

abstractIn this study, we propose three polarimetric descriptors: the pseudo scattering-type parameter (θc), the pseudo scattering entropy parameter (Hc), and the co-pol purity parameter (mc) from dual-pol S1 GRD SAR data.

Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment →

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we propose three polarimetric descriptors: the pseudo scattering-type parameter (θc), the pseudo scattering entropy parameter (Hc), and the co-pol purity parameter (mc) from dual-pol S1 GRD SAR data. We also introduce a novel unsupervised clustering framework using Hc and θc with six clustering zones to represent various scattering mechanisms. We implemented the proposed algorithm on the cloud-based Google Earth Engine (GEE) platform for Sentinel-1 SAR data. We have shown the sensitivity of these descriptors over a time series of data for wheat and canola crops at a test site in Canada. From the leaf development stage to the flowering stage for both crops, the pseudo scattering-type parameter θc changes by approximately 17°. Moreover, within the entire phenology window, both mc and Hc varies by about 0.6. The effectiveness of θc and Hc to cluster the phenological stages for the two crops is also evident from the clustering plot. During the leaf development stage, about 90 % of the sampling points were clustered into the low to medium entropy scattering zone for both the crops. Throughout the flowering stage, the entire cluster shifted into the high entropy vegetation scattering zone. Finally, during the ripening stage, the clusters of sample points were split between the high entropy vegetation scattering zone and the high entropy distributed scattering zone, with > 55 % of the sampling points in the high entropy distributed scattering zone. This innovative clustering framework will facilitate the operational use of S1 GRD SAR data for agricultural applications. This article is submitted to ISPRS Journal of Photogrammetry and Remote Sensing

Why it matches plant phenotyping methodsSentinel-1 SARから作物の生育・フェノロジー状態を推定する新規偏波記述子とクラスタリング手法を開発・実装しており、植物表現型取得が中心である。

abstractIn this study, we propose three polarimetric descriptors: the pseudo scattering-type parameter (θc), the pseudo scattering entropy parameter (Hc), and the co-pol purity parameter (mc) from dual-pol S1 GRD SAR data.

High‐throughput phenotyping of canopy height in cool‐season crops using sensing techniques →

Abstract Plant breeders are interested in plant height data, which is an important agronomic data associated with lodging and mechanical harvest. Manual measurement of plant height with limited samples per plot and data acquisition frequency remains the standard method in breeding programs. To overcome such limitations, this study focuses on plant height estimation in canola (Brassica napus L./winter canola, and B. napus L. and B. rapa L./spring canola), pea (Pisum sativum L.), chickpea (Cicer arietinum L.), and camelina (Camelina sativa L.) breeding trials using sensors. Plant height data were collected using a light detection and ranging (LiDAR) sensor system mounted on a tractor (for pea and chickpea) and an unmanned aerial system (UAS) integrated with a Red–Green–Blue (RGB) camera (for four crops). The LiDAR data and UAS‐based images were processed to extract six plant height features. Significant (P < .0001) correlations between LiDAR estimated and manually measured plant height data were observed with correlation coefficient (r) of .74 and .91 in chickpea and pea, respectively. Image‐based plant height estimations were also correlated (P < .0001) with manually measurement in the four crops (r = .57 – .98). This study demonstrated that the plant height of four cool‐season crops can be estimated using either proximal or remote sensing techniques even if the canopy architectures of these crops pose challenges. Such high throughput phenotyping technologies can be applied in plant breeding and crop production to monitor plant height and associated traits such as lodging in an efficient and timely manner.

Why it matches plant phenotyping methodsLiDARおよびUAS画像を用いて作物の草丈を推定し、手測定と相関検証した高スループット表現型計測が研究の中心である。

abstractthis study focuses on plant height estimation in canola (Brassica napus L./winter canola, and B. napus L. and B. rapa L./spring canola), pea (Pisum sativum L.), chickpea (Cicer arietinum L.), and camelina (Camelina sativa L.) breeding trials using sensors.

Imaging approaches for chromosome structures →

This review describes image analyses for chromosome visible structures, focusing on the chromosome imaging system CHIAS (Chromosome Image Analyzing System). CHIAS is the first comprehensive imaging system for the analysis and characterization of plant chromosomes. A simulation method for human vision for capturing band positive regions was developed and used for the image analysis of large plant chromosomes with bands. Applying this method to C-banded Crepis chromosomes enabled recognition of band positive regions as seen by human vision. Furthermore, a new image parameter, condensation pattern was developed and successfully applied to identify small plant chromosomes such as rice and brassicas. Condensation profile (CP) derived from condensation pattern was also effective in developing quantitative chromosome maps. The result was quantitative chromosomal maps of several plants with small chromosomes, including Arabidopsis, diploid brassicas, rapeseed, rice, spinach, and sugarcane. In the final chapter, various applications of imaging techniques to the analysis of pachytene chromosomes, improved visibility of multicolor FISH images, 3D reconstruction of a human chromosome based on cross-section images obtained by a FIB/SEM, automatic extraction of chromosomal regions by machine learning, etc. are described.

Why it matches plant phenotyping methods植物染色体の画像解析システムと定量的画像パラメータを中心に扱う方法論レビューであり、植物染色体構造の画像ベース計測が中核です。

abstractThis review describes image analyses for chromosome visible structures, focusing on the chromosome imaging system CHIAS (Chromosome Image Analyzing System).

A backlight and deep learning based method for calculating the number of seeds per silique →

Rapeseed is one of the most important oil crops in the world, and the rapeseed yield is increasing every year. The number of seeds per silique is one of the critical factors in the rapeseed yield. Studies that examine the number of seeds per silique have an important influence on the rapeseed yield measurement and the breeding of high-yield rapeseed varieties. Image-analysis-based seed counting methods have the advantages of being fast, accurate, and convenient. Based on the light-transmitting characteristic of siliques, this study used the backlight method to obtain images of the siliques' inner seeds. Three methods, the OTSU, Faster-RCNN, and DeepLabV3+, were used for the rapeseed segmentation and counting under different light intensities. The results showed that the silique images obtained under the 18,600 l× light intensity were the most conducive to seed segmentation. Under this condition, the DeepLabV3+ method had the best accuracy for the segmentation and counting of rapeseed. The Recall and F1-score were greater than 91% and 94%, respectively.

Why it matches plant phenotyping methodsバックライト画像と深層学習によるシリクの種子数という植物形質の取得・計数手法を開発・比較しており、フェノタイピング手法が中心である。

titleA backlight and deep learning based method for calculating the number of seeds per silique

Virulence Spectrum of Single-Spore and Field Isolates of Plasmodiophora brassicae Able to Overcome Resistance in Canola ( Brassica napus ). →

Clubroot, caused by Plasmodiophora brassicae Woronin, is an important disease of canola ( Brassica napus L.) that is managed mainly by planting clubroot-resistant (CR) cultivars. Field isolates of P. brassicae can be heterogeneous mixtures of various pathotypes, making assessments of the genetics of host-pathogen interactions challenging. Thirty-four single-spore isolates were obtained from nine field isolates of the pathogen collected from CR canola cultivars. The virulence patterns of the single-spore and field isolates were assessed on the 13 host genotypes of the Canadian Clubroot Differential (CCD) set, which includes the differentials of Williams and Somé et al. Indices of disease (IDs) severity of 25, 33, and 50% (±95% confidence interval) were compared as potential thresholds to distinguish between resistant and susceptible reactions, with an ID of 50% giving the most consistent responses for pathotype classification purposes. With this threshold, 13 pathotypes could be distinguished based on the CCD system, 7 on the differentials of Williams, and 3 on the hosts of Somé et al. The highest correlations were observed among virulence matrices generated using the three threshold IDs on the CCD set. Genetically homogeneous single-spore isolates gave a clearer profile of the P. brassicae pathotype structure. Novel pathotypes, not reported in Canada previously, were identified among the isolates. This large collection of single-spore isolates can serve as a reference in screening and breeding for clubroot resistance.

Why it matches plant phenotyping methods植物の病徴重症度を用いた病害表現型の閾値比較と病原型分類を中心に、測定・分類法を技術的に評価しているため、植物フェノタイピング手法の検証・適用として含める。

abstractIndices of disease (IDs) severity of 25, 33, and 50% (±95% confidence interval) were compared as potential thresholds to distinguish between resistant and susceptible reactions, with an ID of 50% giving the most consistent responses for pathotype classification purposes.

Root Morphological Traits of Seedlings Are Predictors of Seed Yield and Quality in Winter Oilseed Rape Hybrid Cultivars. →

The root system is responsible for soil resources acquisition. Hence, optimizing crop root characteristics has considerable implications for agricultural production. This study evaluated a panel of twenty-eight European modern cultivars of oilseed rape ( Brassica napus L.) cultivated in laboratory and field environments. Root morphology was screened using a high-throughput hydroponic growth system with two divergent nitrogen supplies. The panel showed an important diversity for biomass production and root morphological traits. Differences in root and shoot dry biomasses and lateral root length were mainly explained by the genotype, and differences in primary root length by nitrogen nutrition. The cultivars were tested in a pluriannual field trial. The field variation for yield and seed quality traits attributed to the genotype was more important than the year or the genotype × year interaction effects. The total root length measured at the seedling stage could predict the proportion of nitrogen taken up from the field and reallocated to seed organs, a component of the nitrogen use efficiency. The genetic interrelationship between cultivars, established with simple sequence repeat markers, indicated a very narrow genetic base. Positive correlations were found between the genetic distance measures, root morphological traits during nitrogen depletion and yield components. This study illustrates a root phenotyping screen in the laboratory with a proof of concept evaluation in the field. The results could assist future genetic improvements in oilseed rape for desirable root characteristics to reduce nutrient losses in the environment.

Why it matches plant phenotyping methods高スループット水耕システムによる根形態形質の取得と圃場での予測検証が研究の中心であり、根系フェノタイピング手法の実質的な適用・評価に該当する。

abstractRoot morphology was screened using a high-throughput hydroponic growth system with two divergent nitrogen supplies.

Evaluation of Rapeseed Winter Crop Damage Using UAV-Based Multispectral Imagery →

This research is related to the exploitation of multispectral imagery from an unmanned aerial vehicle (UAV) in the assessment of damage to rapeseed after winter. Such damage is one of a few cases for which reimbursement may be claimed in agricultural insurance. Since direct measurements are difficult in such a case, mainly because of large, unreachable areas, it is therefore important to be able to use remote sensing in the assessment of the plant surface affected by frost damage. In this experiment, UAV images were taken using a Sequoia multispectral camera that collected data in four spectral bands: green, red, red-edge, and near-infrared. Data were acquired from three altitudes above the ground, which resulted in different ground sampling distances. Within several tests, various vegetation indices, calculated based on four spectral bands, were used in the experiment (normalized difference vegetation index (NDVI), normalized difference vegetation index—red edge (NDVI_RE), optimized soil adjusted vegetation index (OSAVI), optimized soil adjusted vegetation index—red edge (OSAVI_RE), soil adjusted vegetation index (SAVI), soil adjusted vegetation index—red edge (SAVI_RE)). As a result, selected vegetation indices were provided to classify the areas which qualified for reimbursement due to frost damage. The negative influence of visible technical roads was proved and eliminated using OBIA (object-based image analysis) to select and remove roads from classified images selected for classification. Detection of damaged areas was performed using three different approaches, one object-based and two pixel-based. Different ground sampling distances and different vegetation indices were tested within the experiment, which demonstrated the possibility of using the modern low-altitude photogrammetry of a UAV platform with a multispectral sensor in applications related to agriculture. Within the tests performed, it was shown that detection using UAV-based multispectral data can be a successful alternative for direct measurements in a field to estimate the area of winterkill damage. The best results were achieved in the study of damage detection using OSAVI and NDVI and images with ground sampling distance (GSD) = 10 cm, with an overall classification accuracy of 95% and a F1-score value of 0.87. Other results of approaches with different flight settings and vegetation indices were also promising.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数・OBIA・分類手法を用いて、ナタネの冬枯れ被害面積（植物状態）を推定・検証しており、フェノタイピング手法が中心である。

abstractit is therefore important to be able to use remote sensing in the assessment of the plant surface affected by frost damage.

GridFree: A Python Package of Image Analysis for Interactive Grain Counting and Measuring →

Abstract Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting the genetic architecture of these traits toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users’ experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crops, including alfalfa, canola, lentil, wheat, chickpea, and soybean. GridFree was implemented in Python with a friendly graphical user interface to allow users to easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website ( https://zzlab.net/GridFree ).

Why it matches plant phenotyping methods穀粒画像からの分割・計数・形質測定を目的とするソフトウェアを開発し、既存ソフトウェアとの性能比較も行っており、植物フェノタイピング手法が研究の中心である。

abstractIn this study, we developed a software package, named GridFree, to overcome these challenges.

Optimal temporal-spatial fluorescence techniques for phenotyping nitrogen status in oilseed rape. →

Nitrogen (N) fertilizer maximizes the growth of oilseed rape (Brassica napus L.) by improving photosynthetic performance. Elucidating the dynamic relationship between fluorescence and plant N status could provide a non-destructive diagnosis of N status and the breeding of N-efficient cultivars. The aim of this study was to explore the impacts of different N treatments on photosynthesis at a spatial-temporal scale and to evaluate the performance of three fluorescence techniques for the diagnosis of N status. One-way ANOVA and linear discriminant analysis were applied to analyze fluorescence data acquired by a continuous excitation chlorophyll fluorimeter (OJIP transient analysis), pulse amplitude-modulated chlorophyll fluorescence (PAM-ChlF), and multicolor fluorescence (MCF) imaging. The results showed that the maximum quantum efficiency of PSII photochemistry (Fv/Fm) and performance index for photosynthesis (PIABS) of bottom leaves were sensitive to N status at the bolting stage, whereas the red fluorescence/far-red fluorescence ratio of top leaves was sensitive at the early seedling stage. Although the classification of N treatments by the three techniques achieved comparable accuracies, MCF imaging showed the best potential for early diagnosis of N status in field phenotyping because it had the highest sensitivity in the top leaves, at the early seedling stage. The findings of this study could facilitate research on N management and the breeding of N-efficient cultivars.

Why it matches plant phenotyping methods油糠菜氮状态诊断中，研究核心是比较并评估三种叶绿素荧光测量技术及其时空性能，属于植物生理表型获取与验证，而非单纯生物学实验。

abstractto evaluate the performance of three fluorescence techniques for the diagnosis of N status

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination →

Summary Efficient seed germination and establishment are important traits for field and glasshouse crops. Large‐scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large‐scale germination scoring. Here, we present the SeedGerm system, which combines cost‐effective hardware and open‐source software for seed germination experiments, automated seed imaging, and machine‐learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination‐ and establishment‐related traits, in both comma‐separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists’ scoring of radicle emergence. Germination curves were produced based on seed‐level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large‐scale seed phenotyping and testing, for both research and routine seed technology applications.

Why it matches plant phenotyping methods種子発芽・定着形質の画像取得と機械学習による自動抽出を行うハードウェア／ソフトウェア基盤を詳細に開発・検証しており、フェノタイピング手法が研究の中心である。

abstractIn this article, we describe the hardware and software design in detail.

Rapid and Efficient Determination of Relative Water Contents of Crop Leaves Using Electrical Impedance Spectroscopy in Vegetative Growth Stage →

Crop water stress is a deficiency in plants in water supply when the transpiration rate becomes higher than the water absorption capacity. The stress may be detected by a reduction in soil water content, or by the change in physiological properties of the crop. The leaf water content (LWC) is commonly used to assess the water status of plants, which is one of the indicators of crop water stress. In this work, the leaf relative water contents of four different crops: canola, wheat, soybeans, and corn—all in vegetative growth stage—were determined by a noninvasive tool called, electrical impedance spectroscopy (EIS). Using a frequency range of 5–15 kHz, a strong correlation between leaf water contents and leaf impedances was obtained using multiple linear regression. The trained dataset was validated by analysis of variance tests. Regression results were obtained using the least square method. The optimized regression model coefficients for different crops were proposed by selecting features using the wrapper backward elimination method. Multi-collinearity among the features was considered and individual T-tests were made in the feature selection. A maximum correlation coefficient (R) of 0.99 was obtained for canola compared to the other crops; the corresponding coefficient of determination (R2) of 0.98, an adjusted R2 of 0.93, and root mean square error (rmse) of 0.30% were obtained for 36 features. Therefore, the results show that the proposed technique using EIS can be used to develop a low-cost and effective tool for determining the leaf water contents rapidly and efficiently in multiple crops.

Why it matches plant phenotyping methodsEISを用いて作物葉の相対含水量という生理形質を非侵襲的に推定する手法を開発し、回帰モデルを検証しており、フェノタイピング手法が研究の中心である。

abstractthe leaf relative water contents of four different crops: canola, wheat, soybeans, and corn—all in vegetative growth stage—were determined by a noninvasive tool called, electrical impedance spectroscopy (EIS).

A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops. →

Non-invasive determination of leaf nitrogen (N) and water contents is essential for ensuring the healthy growth of the plants. However, most of the existing methods to measure them are expensive. In this paper, a low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively. Four different species of plants-canola, corn, soybean, and wheat-are used as test plants to investigate the utility of the proposed device. The sensor system comprises two multispectral sensors, visible (VIS) and near-infrared (NIR), detecting reflectance at 12 wavelengths (six from each sensor). Two separate experiments were performed in a controlled greenhouse environment, including N and water experiments. Spectral data were collected from 307 leaves (121 for N and 186 for water experiment), and the rational quadratic Gaussian process regression (GPR) algorithm was applied to correlate the reflectance data with actual N and water content. By performing five-fold cross-validation, the N estimation showed a coefficient of determination () of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola showed an of 18.02%, corn showed an of 68.41%, soybean showed an of 46.38%, and wheat showed an of 64.58%. The result reveals that the proposed low-cost sensor with an appropriate regression model can be used to determine N content. However, further investigation is needed to improve the water estimation results using the proposed device.

Why it matches plant phenotyping methods葉の窒素・水分含量という植物生理形質を推定する低コスト携帯型マルチスペクトルセンサーと回帰解析を開発・検証しており、表現型取得手法が研究の中心である。

abstracta low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively.

Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops. →

The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6–2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15–30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2–3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops.

Why it matches plant phenotyping methods画像センサー、解像度、画像処理を比較・評価し、開花強度を定量化するフェノタイピング手法の検証が中心であるため。

abstractwe evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity

SoilCam: A Fully Automated Minirhizotron using Multispectral Imaging for Root Activity Monitoring. →

A minirhizotron is an in situ root imaging system that captures components of root system architecture dynamics over time. Commercial minirhizotrons are expensive, limited to white-light imaging, and often need human intervention. The implementation of a minirhizotron needs to be low cost, automated, and customizable to be effective and widely adopted. We present a newly designed root imaging system called SoilCam that addresses the above mentioned limitations. The imaging system is multi-modal, i.e., it supports both conventional white-light and multispectral imaging, with fully automated operations for long-term in-situ monitoring using wireless control and access. The system is capable of taking 360° images covering the entire area surrounding the tube. The image sensor can be customized depending on the spectral imaging requirements. The maximum achievable image quality of the system is 8 MP (Mega Pixel)/picture, which is equivalent to a 2500 DPI (dots per inch) image resolution. The length of time in the field can be extended with a rechargeable battery and solar panel connectivity. Offline image-processing software, with several image enhancement algorithms to eliminate motion blur and geometric distortion and to reconstruct the 360° panoramic view, is also presented. The system is tested in the field by imaging canola roots to show the performance advantages over commercial systems.

Why it matches plant phenotyping methods根系の経時的表現型を取得する自動・マルチスペクトル画像システムと画像処理ソフトウェアを開発し、既存システムとの性能比較も行っており、フェノタイピング手法が中心である。

abstractWe present a newly designed root imaging system called SoilCam that addresses the above mentioned limitations.

Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions →

Unmanned aerial vehicles (UAVs) equipped with spectral sensors have become useful in the fast and non-destructive assessment of crop growth, endurance and resource dynamics. This study is intended to inspect the capabilities of UAV-onboard multispectral sensors for non-destructive phenotype variables, including leaf area index (LAI), leaf mass per area (LMA) and specific leaf area (SLA) of rapeseed oil at different growth stages. In addition, the raw image data with high ground resolution (20 cm) were resampled to 30, 50 and 100 cm to determine the influence of resolution on the estimation of phenotype variables by using vegetation indices (VIs). Quadratic polynomial regression was applied to the quantitative analysis at different resolutions and growth stages. The coefficient of determination (R2) and root mean square error results indicated the significant accuracy of the LAI estimation, wherein the highest R2 values were attained by RVI = 0.93 and MTVI2 = 0.89 at the elongation stage. The noise equivalent of sensitivity and uncertainty analyses at the different growth stages accounted for the sensitivity of VIs, which revealed the optimal VIs of RVI, MTVI2 and MSAVI in the LAI estimation. LMA and SLA, which showed significant accuracies at (R2 = 0.85, 0.81) and (R2 = 0.85, 0.71), were estimated on the basis of the predicted leaf dry weight and LAI at the elongation and flowering stages, respectively. No significant variations were observed in the measured regression coefficients using different resolution images. Results demonstrated the significant potential of UAV-onboard multispectral sensor and empirical method for the non-destructive retrieval of crop canopy variables.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と回帰モデルを用いて、ラピッドシードのLAI・LMA・SLAという植物形質を非破壊推定し、解像度や生育段階による精度を検証しているため、センシング手法の評価が中心です。

abstractThis study is intended to inspect the capabilities of UAV-onboard multispectral sensors for non-destructive phenotype variables, including leaf area index (LAI), leaf mass per area (LMA) and specific leaf area (SLA) of rapeseed oil at different growth stages.

Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies →

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C 4 grass Setaria , a diversity panel of sorghum ( S. bicolor ), and the founder panel for a nested association mapping population of canola ( Brassica napus L. ). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

Why it matches plant phenotyping methods画像から処置応答を自動検出・定量する新規フェノタイピング手法を開発し、実データと合成データで検証しており、手法が研究の中心である。

abstractIn this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images.

Potential of vibrational spectroscopy for rapid and accurate determination of the hydrogen peroxide treatment of plant leaves. →

Detection and characterization of interactions between crop plants and hydrogen peroxide (H 2 O 2 ) is significant for the exploration of the mechanisms in plant pathology. The objective of this research is to estimate spectral characteristics of rapeseed leaves (Brassica napus L.) during treatment with different H 2 O 2 concentrations (0, 0.5, 1.0, and 3.0 mmol/L) by using Raman spectroscopy (RS) (800-1800 cm -1 ) and hyperspectral imaging (HSI) (400-1000 nm). Cluster analysis of RS and HSI data between the control and treated samples was conducted using kernel principal component analysis (KPCA) and principal component analysis (PCA), respectively. Characteristic Raman shifts at 1012, 1163, and 1530 cm -1 and hyperspectral featured wavelengths at 452, 558, 655, and 703 nm were selected for discriminating control and treated samples. The one-way analysis of variance (ANOVA) was applied to demonstrate the significant difference in spectral signatures of samples, and results showed that 452 nm is promising to assess the control and treated samples at the p -1 combined with 452 nm produced the best recognize rate (RR) of 81.7% to detect the control and treated leaves than other models. Therefore, the results encouraged multiple sensor fusion to improve models for better model performance and to detect plant treatment situations with H 2 O 2 solutions.

Why it matches plant phenotyping methodsラマン分光とハイパースペクトル画像を用いて植物葉の処理状態を推定し、特徴波長選択・センサ融合・認識性能を評価しており、表現型取得・判別手法が中心である。

abstractestimate spectral characteristics of rapeseed leaves (Brassica napus L.) during treatment with different H 2 O 2 concentrations

Estimation of Crop Biomass and Leaf Area Index from Multitemporal and Multispectral Imagery Using Machine Learning Approaches →

Accurate estimation of biomass and Leaf Area Index (LAI) requires appropriate models and predictor variables. These biophysical parameters are indicative of crop productivity, and thus, are of interest in applications such as crop yield forecasting and precision farming. This study evaluated the potential of leveraging vegetation indices derived from multi-temporal RapidEye data using a machine learning approach to estimate crop biomass and LAI. Both near-infrared and red-edge based indices were considered in this study. In-situ measurements of these two parameters for three main cash crops, including canola, corn, and soybeans, were collected during a field campaign and used for model calibration and validation. Crops models were developed using the artificial neural network (ANN) and support vectors regression (SVR). Results showed that, for each crop, the SVR modeled LAI and biomass more accurately than ANN. For biomass, the SVR’s Root Mean Square Errors (RMSEs) were reported as 25.22 g/m2 for canola, 88.13 g/m2 for corn, 5.91 g/m2 for soybean, and 56.14 g/m2 for all crops pooled. Similarly, for the LAI, SVR provided the best model with RMSE = 0.59 m2/m2 for canola, RMSE = 0.27 m2/m2 for corn, RMSE = 0.21 m2/m2 for soybean, and RMSE = 0.51 m2/m2 for all crops together.

Why it matches plant phenotyping methodsマルチスペクトル画像と機械学習により作物バイオマスおよびLAIを推定し、現地測定でモデルを較正・検証しているため、形質取得手法が中心である。

abstractThis study evaluated the potential of leveraging vegetation indices derived from multi-temporal RapidEye data using a machine learning approach to estimate crop biomass and LAI.

Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm →

Plant color is a key feature for estimating parameters of the plant grown under different conditions using remote sensing images. In this case, the variation in plant color should be only due to the influence of the growing conditions and not due to external confounding factors like a light source. Hence, the impact of the light source in plant color should be alleviated using color calibration algorithms. This study aims to develop an efficient, robust, and cutting-edge approach for automatic color calibration of three-band (red green blue: RGB) images. Specifically, we combined the k-means model and deep learning for accurate color calibration matrix (CCM) estimation. A dataset of 3150 RGB images for oilseed rape was collected by a proximal sensing technique under varying illumination conditions and used to train, validate, and test our proposed framework. Firstly, we manually derived CCMs by mapping RGB color values of each patch of a color chart obtained in an image to standard RGB (sRGB) color values of that chart. Secondly, we grouped the images into clusters according to the CCM assigned to each image using the unsupervised k-means algorithm. Thirdly, the images with the new cluster labels were used to train and validate the deep learning convolutional neural network (CNN) algorithm for an automatic CCM estimation. Finally, the estimated CCM was applied to the input image to obtain an image with a calibrated color. The performance of our model for estimating CCM was evaluated using the Euclidean distance between the standard and the estimated color values of the test dataset. The experimental results showed that our deep learning framework can efficiently extract useful low-level features for discriminating images with inconsistent colors and achieved overall training and validation accuracies of 98.00% and 98.53%, respectively. Further, the final CCM provided an average Euclidean distance of 16.23 ΔΕ and outperformed the previously reported methods. This proposed technique can be used in real-time plant phenotyping at multiscale levels.

Why it matches plant phenotyping methods油糧菜キャノピー画像の色校正手法を開発・検証し、植物フェノタイピングへの利用を明示しているため、方法が中心的である。

abstractThis study aims to develop an efficient, robust, and cutting-edge approach for automatic color calibration of three-band (red green blue: RGB) images.

Automatic Measuring Approach and Device for Mature Rapeseed’s Plant Type Parameters →

The plant type parameters, which can be used to track and study the morphological changes of crop organs, are of great importance for breeders and testers. Based on the structural characteristics of mature rapeseed, an automatic measuring system for rapeseed plant type parameters was studied, through which the parameters of primary branches including their quantity, position, and branching angle and the geometric parameters such as plant height, main stem width, main raceme length, and the first branch height were measured. The system hardware includes a manual cutting-short device, an image acquisition darkroom, and a control circuit, used for capturing videos of a stably rotating rapeseed plant in a darkroom with an adjustable light source. The system software is used to connect and control CCD camera and other hardware and run specific image processing algorithms to process and analyze videos to obtain plant type parameters. The human-machine interface provided by the system can be used to set working parameters and display and process measurement results. To evaluate the accuracy of this prototype, batches of rapeseed plants were tested. The results showed that the mean absolute percentage error (MAPE) for the system was about 2.74%, and the automatic measurements had a good agreement with manual measurements. The measuring efficiency was approximately 2.1 times over manual method. In conclusion, the system, which automatically extracts plant type parameters with high throughput and high precision, is of practical value as it can effectively reduce the labor intensity of researchers and provide important basic data for the research of rapeseed breeding and agricultural machinery design.

Why it matches plant phenotyping methods成熟 rapeseedの草型・形態形質を画像取得と画像処理で自動抽出する装置を開発し、手動測定との精度比較で検証しており、フェノタイピング手法が研究の中心です。

abstractan automatic measuring system for rapeseed plant type parameters was studied

Automated image processing framework for analysis of the density of fruiting bodies of Leptosphaeria maculans on oilseed rape stems →

Understanding the transmission of plant pathogen inoculum during the periods when the host plants are not present is crucial for predicting the initiation of epidemics and optimizing mitigation strategies. However, inoculum production at the end of the cropping season, survival during the intercrop period, and the emergence or release of inoculum can be highly variable, difficult to assess, and generally inferred indirectly from symptom data. As a result, a lack of large datasets hampers the study of these epidemiological processes. Here, inoculum production was studied in Leptosphaeria maculans, the cause of phoma stem canker of oilseed rape. The fungus survives on stubble left in the field, from which ascospores are released at the beginning of the next cropping season. An image processing framework was developed to estimate the density of fruiting bodies produced on stem pieces following incubation in field conditions, and a quality assessment of the processing chain was performed. A total of 2540 standardized RGB digital images of stems were then analysed, collected from 27 oilseed rape fields in Brittany over four cropping seasons. Manual post‐processing removed 16% of the pictures, e.g. when moisture‐induced darkening of the oilseed rape stems caused overestimation of the area covered with fruiting bodies. The potential level of inoculum increased with increasing phoma stem canker severity at harvest, and depended on the source field and the cropping season. This work shows how image‐based phenotyping generates high‐throughput disease data, opening up the prospect of substantially increased precision in epidemiological studies.

Why it matches plant phenotyping methods植物茎上の病原菌果実体密度を画像から推定する処理フレームワークを開発し、処理系の品質評価も行っており、植物病害状態の取得手法が研究の中心である。

abstractAn image processing framework was developed to estimate the density of fruiting bodies produced on stem pieces following incubation in field conditions, and a quality assessment of the processing chain was performed.

Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure →

Image processing technology has gained considerable attention in agricultural proximal sensing applications, including plant disease detection, vegetation fraction estimation, monitoring of the crop growth status, and image-based site-specific management. Image segmentation is the first and crucial step to process complex infield images. However, image segmentation by either hand engineered-based or deep learning-based methods that train the entire system from scratch is a daunting task and needs several hundreds of labeled images that may be difficult to obtain in practice. The recent development of transfer learning has shown the potential of transferring the learned feature detectors of a pre-trained convolutional neural network to a new image dataset. This study was thus aimed to evaluate three transfer learning methods using a VGG16-based encoder net for semantic segmentation of oilseed rapes images in a field with high-density weeds. Three different transfer learning approaches using a VGG16-based encoder model were proposed, and their performances were compared to a VGG19-based encoder net. Relying on the intensive use of data augmentation and transfer learning, we showed that such networks could be trained end-to-end using a few annotated training images. The highest accuracy of 96% was obtained by the VGG16-based encoder net in which the fine-tuned model was only used for feature extraction and the segmentation was performed using shallow machine learning classifiers (MLCs). Transfer learning demonstrated to be efficient and presented a robust performance in segmenting plants amongst high-density weeds. The implementation of MLCs is reasonable for real-time applications with the segmentation time less than 0.05 s/image.

Why it matches plant phenotyping methods圃場画像から作物と雑草を分離するセマンティックセグメンテーション手法を開発・比較し、植物画像解析の性能を評価しているため、植物フェノタイピング手法が中心である。

abstractThis study was thus aimed to evaluate three transfer learning methods using a VGG16-based encoder net for semantic segmentation of oilseed rapes images in a field with high-density weeds.

Assessment of Portable Chlorophyll Meters for Measuring Crop Leaf Chlorophyll Concentration →

Accurate measurement of leaf chlorophyll concentration (LChl) in the field using a portable chlorophyll meter (PCM) is crucial to support methodology development for mapping the spatiotemporal variability of crop nitrogen status using remote sensing. Several PCMs have been developed to measure LChl instantaneously and non-destructively in the field, however, their readings are relative quantities that need to be converted into actual LChl values using conversion functions. The aim of this study was to investigate the relationship between actual LChl and PCM readings obtained by three PCMs: SPAD-502, CCM-200, and Dualex-4. Field experiments were conducted in 2016 on four crops: corn (Zea mays L.), soybean (Glycine max L. Merr.), spring wheat (Triticum aestivum L.), and canola (Brassica napus L.), at the Central Experimental Farm of Agriculture and Agri-Food Canada in Ottawa, Ontario, Canada. To evaluate the impact of other factors (leaf internal structure, leaf pigments other than chlorophyll, and the heterogeneity of LChl distribution) on the conversion function, a global sensitivity analysis was conducted using the PROSPECT-D model to simulate PCM readings under different conditions. Results showed that Dualex-4 had a better performance for actual LChl measurement than SPAD-502 and CCM-200, using a general conversion function for all four crops tested. For SPAD-502 and CCM-200, the error in the readings increases with increasing LChl. The sensitivity analysis reveals that deviations from the calibration functions are more induced by non-uniform LChl distribution than leaf architectures. The readings of Dualex-4 can have a better ability to restrict these influences than those of the other two PCMs.

Why it matches plant phenotyping methods携帯型クロロフィルメータによる葉クロロフィル濃度測定法を複数機器で比較・検証し、換算関数と感度分析を評価しているため、植物表現型取得法が研究の中心である。

abstractThe aim of this study was to investigate the relationship between actual LChl and PCM readings obtained by three PCMs: SPAD-502, CCM-200, and Dualex-4.

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. →

Background In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. Results We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. Conclusions We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever.

Why it matches plant phenotyping methods根系画像から根系構造を自動抽出する深層学習手法とツールの開発・評価が研究の中心であり、植物形態形質のフェノタイピングに該当する。

abstractWe present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups.

Identification of genetic variation in Brassica napus seeds for tocopherol content and composition using near‐infrared spectroscopy technique →

Tocopherol is an essential fat‐soluble nutrient for humans. Increasing the tocopherol content in Brassica napus seeds can add value to rapeseed vegetable oil; this has become an important breeding target. However, there is no efficient and non‐destructive method for selecting rapeseed accessions with high tocopherol contents. Here, we report the first near‐infrared reflectance spectroscopy (NIRS)‐based technique for predicting rapeseed tocopherol content. Individual seed tocopherol compositions were estimated from 373 rapeseed genotypes of different origins. This method and chemical methods produced comparable predicted values of the tocopherol constituents in the seeds. Three equations were generated for the prediction of tocopherol content by using a modified partial least squares (MPLS) model. The total tocopherol content for the determination coefficient of cross‐validation (R²cᵥ) (0.74), determination coefficient (RSQ) (0.76) and one minus the ratio of unexplained variance to total variance (1‐VR) (0.65) values indicates a strong correlation between the calibration and validation sets. Overall, our model confirmed the NIRS method as feasible for predicting tocopherol content in rapeseed and as an efficient screening tool for future breeding programs.

Why it matches plant phenotyping methodsBrassica napus種子のトコフェロール含量を非破壊NIRSで推定する手法を開発し、化学分析と比較検証しており、育種選抜のための植物形質取得が中心である。

abstractthere is no efficient and non‐destructive method for selecting rapeseed accessions with high tocopherol contents.

Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images →

Monitoring the above ground biomass (AGB) in winter oilseed rape is important for improving the agronomic management efficiency and predicting yield to ensure edible oil and biofuel supplies. The Yangtze River Basin in China accounts for one-fifth of the rapeseed yield in the world. However, the fragmented farming lands in the Yangtze River Basin make it difficult to accurately monitor winter oilseed rape growth at large scale using the medium-resolution satellite data. The low-altitude unmanned aerial vehicle (UAV) provides a feasible way to accurately and non-destructively estimate AGB in winter oilseed rape at the plot level. In this study, we evaluated the contributions of vegetation indices (VIs) and texture metrics, derived from multispectral images captured by the camera mounted on a UAV, to predict the AGB in winter oilseed rape. The AGB was estimated with (1) multiple VIs and (2) multiple VIs and texture metrics. The partial least square regression (PLSR) and random forest (RF) regression models were trained with datasets of the experiment in 2016-2017 and 2017-2018, and then applied to predict AGB of the 2018-2019 growth season. Results demonstrated that the incorporation of texture metrics to both PLSR and RF models provided more accurate estimations of AGB in winter oilseed rape than the models based solely on VIs. The accuracy of the AGB predicted by the RF regression model using VIs and texture metrics (RMSE=274.18 kg/ha for the validation dataset) was slightly higher than the results of the PLSR model (RMSE=284.09kg/ha for the validation dataset). According to the evaluation of the important variables, the red edge chlorophyll index (CIred edge) and ratio vegetation index (RVI) were selected as the most important input features by PLSR and RF regression models. The normalized difference vegetation index (NDVI) contrast was selected as an important texture metrics for the AGB prediction, indicating that NDVI contrast could be a sensitive indicator of the spatial distribution of shadow caused by the different amount of biomass. This study suggested the great potential of UAV in estimating the plot-level AGB by combining VIs and texture metrics.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数・テクスチャ特徴量を抽出し、作物バイオマスを推定する手法を比較・検証しており、植物形質取得が研究の中心である。

abstractThe low-altitude unmanned aerial vehicle (UAV) provides a feasible way to accurately and non-destructively estimate AGB in winter oilseed rape at the plot level.

Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm →

Knowledge of crop phenology assists in making agricultural decisions such as appropriate irrigation and fertilization applications in order to optimize crop yield. The objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random forest algorithm applied to a multi-temporal RADARSAT-2 dataset, acquired during the Soil Moisture Active Passive (SMAP) Validation Experiment 2016 in Manitoba (SMAPVEX16-MB). The model-based and eigen-based polarimetric parameters are used to separate the vegetation and soil scattering contributions in the total radar signal. As the crop morphological shape and structure vary with phenological growth, our study assumes that the polarimetric parameters related to the volume scattering mechanism have the potential to track the crop phenology. The sensitivity of the polarimetric parameters to the ground identified crop phenology is analyzed for different crop types. For canola, a single polarimetric parameter is sufficient to characterize the crop phenology, due to the high volume scattering power and large temporal dynamic. For corn, soybean and wheat, combinations of multiple polarimetric parameters are required. For each crop type, the Random Forest algorithm trained using 60% of the data is used to retrieve the crop phenology. Performances are compared to Artificial Neural Network, Support Vector Machine Regression and k-Nearest neighborhood algorithms. The Random Forest algorithm provides the best phenology retrieval with significant (p-value < 0.01) spearman correlation coefficients (between the retrieved and ground identified phenology) of 0.93, 0.90, 0.85 and 0.91 for canola, corn, soybean and wheat, respectively. While a single polarimetric parameter demonstrates limited sensitivity to corn phenology, the retrieved phenology from the Random Forest algorithm using multiple polarimetric parameters agrees well with the ground measurements. Furthermore, the importance of different polarimetric parameters for phenology retrieval using the Random Forest algorithm is quantified for different crop types. These findings will be of interest in developing future analytical retrieval models.

Why it matches plant phenotyping methodsSAR偏波分解と機械学習を用いて作物の生育フェノロジーを推定し、地上観測との比較検証や手法間比較を行っており、植物形態・生育状態の取得手法が中心である。

abstractThe objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random forest algorithm applied to a multi-temporal RADARSAT-2 dataset

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures →

We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and first and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat ( Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a final time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist ( http://rootsystemml.github.io/ ), as well as segmentation masks compatible with other automated measurement tools.

Why it matches plant phenotyping methods植物根系形態を自動抽出する画像解析手法とツールの開発・検証が研究の中心であり、複数作物で精度・速度・転移性能を評価している。

abstractWe present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups.

Infield oilseed rape images segmentation via improved unsupervised learning models combined with supreme color features →

The variability of illumination and weather conditions lead to a big challenge for infield image segmentation. Therefore, robust, fast, and automated algorithms are highly required to obtain reliable image segmentation results. This research was aimed to develop efficient unsupervised clustering algorithms for oilseed rape image segmentation in the field. The Naïve Bayes rule was first employed to select a supreme color feature from ten color models. An initialization approach based on the genetic algorithm (GA) was then used to define the initial cluster centroids for subsequent Gaussian mixture model (GMM), self-organizing map (SOM), fuzzy c-mean (FCM), and k-means algorithms. The length of the chromosome was determined using cluster validity indices. Finally, the performances of these algorithms were evaluated based on the image segmentation quality and computation time. After testing the proposed method on the image datasets from two fields, the results revealed that the highest segmentation accuracy of 96% was obtained using the optimized SOM and the lowest computation time was obtained using the k-means. The GA-based initialization speeded up the convergence process and ensured consistent labeling between runs. All clustering algorithms were proved to be robust to varying illumination conditions and can process images with a very complex background in an automated fashion.

Why it matches plant phenotyping methods圃場の油糧ナタネ画像から植物領域を抽出する画像セグメンテーション手法の開発・比較が研究の中心であり、植物表現型取得の基盤手法に該当する。

abstractThis research was aimed to develop efficient unsupervised clustering algorithms for oilseed rape image segmentation in the field.

The power of model-to-crop translation illustrated by reducing seed loss from pod shatter in oilseed rape. →

Key message Elucidation of key regulators in Arabidopsis fruit patterning has facilitated knowledge-translation into crop species to address yield loss caused by premature seed dispersal (pod shatter). In the 1980s, plant scientists descended on a small weed Arabidopsis thaliana (thale cress) and developed it into a powerful model system to study plant biology. The massive advances in genetics and genomics since then have allowed us to obtain incredibly detailed knowledge on specific biological processes of Arabidopsis growth and development, its genome sequence and the function of many of the individual genes. This wealth of information provides immense potential for translation into crops to improve their performance and address issues of global importance such as food security. Here, we describe how fundamental insight into the genetic mechanism by which seed dispersal occurs in members of the Brassicaceae family can be exploited to reduce seed loss in oilseed rape (Brassica napus). We demonstrate that by exploiting data on gene function in model species, it is possible to adjust the pod-opening process in oilseed rape, thereby significantly increasing yield. Specifically, we identified mutations in multiple paralogues of the INDEHISCENT and GA4 genes in B. napus and have overcome genetic redundancy by combining mutant alleles. Finally, we present novel software for the analysis of pod shatter data that is applicable to any crop for which seed dispersal is a serious problem. These findings highlight the tremendous potential of fundamental research in guiding strategies for crop improvement.

Why it matches plant phenotyping methods作物の莢裂開・種子散布という植物形質を解析する新規ソフトウェアを提示しており、単なる生物学的測定ではなく、再利用可能な表現型解析手法が明示されています。

abstractFinally, we present novel software for the analysis of pod shatter data that is applicable to any crop for which seed dispersal is a serious problem.

Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola\n using a DCNN Augmented with Handcrafted Texture Features →

Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate several thousand breeding lines, and therefore, automatic lodging detection and prediction is of great value aid in selection. In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, using transfer learning, we trained 10 lodging detection models using well-established deep convolutional neural network architectures. Our proposed model outperforms the state-of-the-art lodging detection methods in the literature that use only handcrafted features. In comparison to 10 DCNN lodging detection models, our proposed model achieves comparable results while having a substantially lower number of parameters. This makes the proposed model suitable for applications such as real-time classification using inexpensive hardware for high-throughput phenotyping pipelines. The GitHub repository at https://github.com/FarhadMaleki/LodgedNet contains code and models.

Why it matches plant phenotyping methodsUAV画像から作物の倒伏状態を推定するDCNN手法の開発・比較が中心であり、植物表現型の高スループット計測に直接関係する。

abstractwe propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials.

Strong temporal dynamics of QTL action on plant growth progression revealed through high‐throughput phenotyping in canola →

A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non-invasive phenotyping. Time-resolved data for early growth-related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome-wide SNP array data provided the framework for genome-wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker-trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time-resolved analyses to effectively unravel the complex and stage-specific contributions of genes affecting growth processes that operate at different developmental phases.

Why it matches plant phenotyping methods自動化された非侵襲的ハイスループット表現型解析を用いて、成長関連形質を時間分解して取得・解析することが研究の中核であり、単なるルーチン測定を超える実質的なプラットフォーム適用である。

abstractWe took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution.

In-season biomass estimation of oilseed rape (Brassica napus L.) using fully polarimetric SAR imagery →

Accurate estimation of crop biophysical and biochemical parameters during crop growing seasons is essential for improving site-specific management and yield estimation. The potential ability of fully polarimetric synthetic aperture radar (SAR) data in estimating above-ground biomass of oilseed rape was investigated in this study. The temporal profile of different scattering intensity and polarimetric features during the entire growing season was identified with ground measurements. A polarimetric feature, relying on the polarimetric decomposition method, was put forward to estimate the biomass of oilseed rape. Validation results revealed great potential with a determination coefficient (R²) of 0.85, root mean squared error (RMSE) of 41.6 g/m², and relative error (RE) of 28.5% for dry biomass, and an R² of 0.76, RMSE of 527.4 g/m² and RE of 28.6% for fresh biomass. Moreover, the use of full polarization SAR data was compared with single and dual polarization SAR data. The results suggest that when full polarization SAR data is available, a simpler model, higher saturation point and better accuracy can be achieved in biomass estimation of oilseed rape, which highlights the importance and value of polarimetry information in quantitative crop monitoring. This study provides guidelines for in-season monitoring of crop growth parameters with SAR data, which further improves crop monitoring capability in adverse weather conditions.

Why it matches plant phenotyping methodsSAR画像と偏波分解特徴量を用いて油料菜種の地上部バイオマスを推定する手法を提案し、検証・比較しており、植物形質取得が研究の中心です。

abstractA polarimetric feature, relying on the polarimetric decomposition method, was put forward to estimate the biomass of oilseed rape.

Remote prediction of yield based on LAI estimation in oilseed rape under different planting methods and nitrogen fertilizer applications →

The accurate prediction of crop yield at field scale is important for precision agriculture to understand crop production response to agronomic management practice and environmental stress. In this study, we developed a method to predict yield based entirely on remotely sensed data in oilseed rape under different planting methods and nitrogen fertilizer applications. Leaf are index (LAI) measured at four developmental stages were correlated with oilseed rape yield. It is found that LAI at the initiation of stem elongation stage closely related to yield, thus the remote estimation of LAI at this stage can be used to indicate the yield in oilseed rape. The red edge vegetation index (CIred edge), which was derived from canopy reflectance collected at close range as well as by Unmanned Aerial Vehicle (UAV), was able to accurately estimate LAI thus can be used to predict yield in oilseed rape with the estimation error below 15%. Based on remote predictions of field-scale yield in oilseed rape, it is observed that with the same nitrogen fertilizer the oilseed rape plots planted by seed sowing method consistently produced higher yield than plots planted by seedling transplanting method. With the increase of nitrogen fertilizer, the yield of oilseed rape increased but became saturated for the high level of nitrogen applications above 225 kg/ha.

Why it matches plant phenotyping methods油糠菜产量という植物形質を、UAV・近接リモートセンシングによるLAI推定から予測する手法を開発・評価しており、形質取得と予測性能が研究の中心である。

abstractwe developed a method to predict yield based entirely on remotely sensed data in oilseed rape

Nondestructive detection of rape leaf chlorophyll level based on Vis-NIR spectroscopy. →

Chlorophyll is an important factor for measuring the normal growth and development status of plants, and it is also of great significance for the management and utilization of agricultural water and fertilizers. In this study, the chlorophyll content of rapeseed leaves was taken as the research object, and the effect of spectral data pretreatment method on the spectral feature extraction and chlorophyll content prediction model was quantitatively studied. ASD FieldSpec Pro (350-2500 nm) spectrometer was used to measure the spectral reflectance of rape leaf samples, and the spectral reflectance characteristics of different chlorophyll contents were analyzed. The Savitzky-Golay nine-point smoothing of the reflectance spectrum was performed, and the first derivation (FD), second derivation (SD), and reciprocal logarithm (LOG)transformation of the reflectance were performed after MSC and SNV preprocessing respectively. The optimal spectral estimation model for chlorophyll was established by PLSR. The results show that: (1) This study was mainly to monitor the chlorophyll content in rape joints during jointing stage, using the correlation between chlorophyll content and hyperspectral characteristics, using MSC, NOR and SNV to pretreat the reflectance spectra and combining different derivations transformations to extract chlorophyll characteristics. (2) Quantitative model of chlorophyll content was established based PLSR, the best preprocessing was R + SG + SNV + LOG+FD, the calibration results was: LVs = 14, Rc 2 = 0.97, RMSEC = 4.18, SEC = 4.21, Slope = 0.92, Offset = 2.63; the validation results was: Rv 2 = 0.98, RPD = 7.52, RMSEP = 2.94, SEP = 2.98, Slope = 0.98, Offset = 1.43; (3) The optimal estimation model established by different treatment methods has better stability and higher precision, and can rapidly monitor the chlorophyll content of rapeseed in the region.

Why it matches plant phenotyping methodsラップシード葉のクロロフィル量という植物形質を、Vis-NIR分光測定と前処理・PLSRモデルにより非破壊推定する方法が研究の中心であり、検量・検証結果も示されている。

titleNondestructive detection of rape leaf chlorophyll level based on Vis-NIR spectroscopy.

Nondestructive Phenomic Tools for the Prediction of Heat and Drought Tolerance at Anthesis in Brassica Species. →

Oilseed Brassica species are vulnerable to heat and drought stress, especially in the early reproductive stage. We evaluated plant imaging of whole plant and flower tissue, leaf stomatal conductance, leaf and bud temperature, photochemical reflectance index, quantum yield of photosynthesis, and leaf gas exchange for their suitability to detect tolerance to heat (H) and/or drought (D) stress treatments in 12 Brassica genotypes (G). A replicated factorial experiment was set up with 7 d of stress treatment from the beginning of anthesis with various levels of three factors H , D , and G . Most phenomics tools detected plant stress as indicated by significant main effects of H , D , and H×D . Whole plant volume was highly correlated with fresh weight changes, suggesting that whole plant imaging may be a useful surrogate for fresh weight in future studies. Vc max , the maximum carboxylation rate of photosynthesis, increased rapidly on day 1 in H and H+D treatments, and there were significant interactions of G×H and G×D . Vc max of genotypes on day 1 in H and H+D treatments was positively correlated with their harvested seed yield. Vc max on day 1 and day 3 were clustered with seed yield in H and H+D treatments as shown in the heatmaps of genotypic correlations. TPU, the rate of triose phosphate use, also showed significant positive genotypic correlations with seed yield in H+D treatments. Flower volume showed significant interactions of G×H and G×D on day 7, and flower volume of genotypes on day 7 in H was positively correlated with their harvested seed yield. There were few interactions of G×H or G×D for leaf stomatal conductance, leaf and bud temperature, photochemical reflectance index, and quantum yield of photosynthesis. Vc max , TPU, and volume of flowers are potential nondestructive phenomic traits for heat or combined heat and drought stress tolerance screening in Brassica germplasm.

Why it matches plant phenotyping methods熱・乾燥耐性スクリーニングに用いる複数のフェノミクス測定法を比較評価し、画像由来の体積や生理形質の有用性を検証しているため、方法が研究の中心です。

abstractWe evaluated plant imaging of whole plant and flower tissue, leaf stomatal conductance, leaf and bud temperature, photochemical reflectance index, quantum yield of photosynthesis, and leaf gas exchange for their suitability to detect tolerance to heat (H) and/or drought (D) stress treatments

Electrical impedance tomography as a tool for phenotyping plant roots →

Background Plant roots are complex, three-dimensional structures that play a central role in anchorage, water and nutrient acquisition, storage and interaction with rhizosphere microbes. Studying the development of the plant root system architecture is inherently difficult as soil is not a transparent medium. Results This study uses electrical impedance tomography (EIT) to visualise oilseed rape root development in horticultural compost. The development of healthy, control plants and those infected with the gall-forming pathogen, Plasmodiophora brassicae -the causative agent of clubroot disease-were compared. EIT measurements were used to quantify the development of the root system and distinguish between control and infected plants at the onset of gall formation, approximately 20 days after inoculation. Although clear and stark differences between healthy and infected plants were obtained by careful (and hence laborious) packing of the growth medium in layers within the pots; clubroot identification is still possible without a laborious vessel filling protocol. Conclusions These results demonstrate the utility of EIT as a low-cost, non-invasive, non-destructive method for characterising root system architecture and plant-pathogen interactions in opaque growth media. As such it offers advantages over other root characterisation techniques and has the potential to act as a low-cost tool for plant phenotyping.

Why it matches plant phenotyping methodsEITによる根系構造の可視化・定量化手法を開発・実証しており、植物フェノタイピングが中心的な研究です。

abstractThis study uses electrical impedance tomography (EIT) to visualise oilseed rape root development in horticultural compost.

A high-throughput delayed fluorescence method reveals underlying differences in the control of circadian rhythms in Triticum aestivum and Brassica napus →

Background A robust circadian clock has been implicated in plant resilience, resource-use efficiency, competitive growth and yield. A huge number of physiological processes are under circadian control in plants including: responses to biotic and abiotic stresses; flowering time; plant metabolism; and mineral uptake. Understanding how the clock functions in crops such as Triticum aestivum (bread wheat) and Brassica napus (oilseed rape) therefore has great agricultural potential. Delayed fluorescence (DF) imaging has been shown to be applicable to a wide range of plant species and requires no genetic transformation. Although DF has been used to measure period length of both mutants and wild ecotypes of Arabidopsis , this assay has never been systematically optimised for crop plants. The physical size of both B. napus and T. aestivum led us to develop a representative sampling strategy which enables high-throughput imaging of these crops. Results In this study, we describe the plant-specific optimisation of DF imaging to obtain reliable circadian phenotypes with the robustness and reproducibility to detect diverging periods between cultivars of the same species. We find that the age of plant material, light regime and temperature conditions all significantly effect DF rhythms and describe the optimal conditions for measuring robust rhythms in each species. We also show that sections of leaf can be used to obtain period estimates with improved throughput for larger sample size experiments. Conclusions We present an optimized protocol for high-throughput phenotyping of circadian period specific to two economically valuable crop plants. Application of this method revealed significant differences between the periods of several widely grown elite cultivars. This method also identified intriguing differential responses of circadian rhythms in T. aestivum compared to B. napus ; specifically the dramatic change to rhythm robustness when plants were imaged under constant light versus constant darkness. This points towards diverging networks underlying circadian control in these two species.

Why it matches plant phenotyping methods小麦とナタネの概日リズムを測定する遅延蛍光イメージングを作物向けに最適化し、再現性・スループット・測定条件を評価した、中心的なフェノタイピング手法研究である。

abstractwe describe the plant-specific optimisation of DF imaging to obtain reliable circadian phenotypes with the robustness and reproducibility to detect diverging periods between cultivars of the same species.

Temporal genetic patterns of root growth in Brassica napus L. revealed by a low-cost, high-efficiency hydroponic system. →

Key message Application of a low-cost and high-efficiency hydroponic system in a rapeseed population verified two types of genetic factors ("persistent" and "stage-specific") that control root development. The root system is a vital plant component for nutrient and water acquisition and is targeted to enhance plant productivity. Genetic dissection of the root system generally focuses on a single stage, but roots grow continuously during plant development. To reveal the temporal genetic patterns of root development, we measured nine root-related traits in a rapeseed recombinant inbred line population at six continuous stages during vegetative growth, using a modified hydroponic system with low-cost and high-efficiency features that could synchronize plant growth under controlled conditions. Phenotypic correlation and growth dynamic analysis suggested the existence of two types of genetic factors ("persistent" and "stage-specific") that control root development. Dynamic (unconditional and conditional) quantitative trait loci (QTL) mapping detected 28 stage-specific and 23 persistent QTLs related to root growth. Among them, 13 early stage-specific, 19 persistent and 8 later stage-specific QTLs were detected at 7 DAS (days after sowing), 16 DAS and 5 EL (expanding leaf stage), respectively, providing efficient and adaptable stages for QTL identification. The effective prediction of biomass accumulation using root morphological traits (up to 96.6% or 92.64% at a specific stage or the final stage, respectively) verified that root growth allocation with maximum root uptake area facilitated biomass accumulation. Furthermore, marker-assistant selection, which combined the "persistent" and "stage-specific" QTLs, proved their effectiveness for root improvement with an excellent uptake area. Our results highlight the potential of high-throughput and precise phenotyping to assess the dynamic genetics of root growth and provide new insights into ideotype root system-based biomass breeding.

Why it matches plant phenotyping methods低コスト・高効率の改変水耕システムを用いた根系形態の多時点・高スループット計測が研究の主要手法であり、根形質の動的評価と遺伝解析に適用している。

abstractusing a modified hydroponic system with low-cost and high-efficiency features that could synchronize plant growth under controlled conditions

Epoxiconazole exposure affects terpenoid profiles of oilseed rape plantlets based on a targeted metabolomic approach →

Epoxiconazole is a broad-spectrum fungicide described as highly persistent in soil and as such can be considered as an abiotic agent like other problematic agrochemicals. Furthermore, the plant phenotyping tool involving non-invasive monitoring of plant-emitted volatile organic compounds (VOCs) may be useful in the identification of metabolic markers for abiotic stress. We therefore decided to profile the VOCs from secondary metabolism of oilseed rape through a dose-response experiment under several epoxiconazole concentrations (0, 0.01, 0.1 and 1 mg L -1 ). VOC collections of 35-day-old whole plantlets were performed through a dynamic headspace sampling technique under defined and controlled conditions. The plantlets grew freely within a home-made, laboratory and high-throughput glass chamber without any disturbance. Putative metabolic markers were analysed using a targeted metabolomic approach based on TD-GC-MS method coupled with data acquisition in SIM mode in order to focus on terpenes and sulphur-containing volatiles. Chromatograms of emitted terpenes were achieved accurately for the 35-day-old oilseed rape plantlets. We also analysed the presence of sulphur-containing volatiles in samples of shoot and root tissues using an innovative DHS-TD-GC-MS method, but no difference was found between qualitative profiles. Nevertheless, we demonstrated through this experiment that sesquiterpenes such as β-elemene and (E,E)-α-farnesene are involved in epoxiconazole dose-response. In particular, (E,E)-α-farnesene could serve as a metabolic marker of fungicide exposure for oilseed rape plantlets.

Why it matches plant phenotyping methods植物由来VOCを非侵襲的に測定して薬剤ストレスの代謝マーカーを抽出するワークフローが明示され、高スループット測定系として植物状態の評価に用いられているため、単なる代謝測定以上のフェノタイピング応用と判断する。

abstractthe plant phenotyping tool involving non-invasive monitoring of plant-emitted volatile organic compounds (VOCs) may be useful in the identification of metabolic markers for abiotic stress.

Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.) →

The objective of this study was to investigate the utility of hyperspectral images (HSIs) for the detection of oilseed rape waterlogging stress. We assessed two oilseed rape varieties, the non-hybrid NingYou 22 (NY 22) and hybrid NingZa 19 (NZ 19), and HSIs of oilseed rape leaves under different durations of waterlogging stress (0, 3, and 6 days) were collected to build three datasets (NY 22, NZ 19, and both combined). We extracted red–green–blue (RGB) images and visible and near-infrared (VNIR 400–1000 nm) spectra from a region of interest (ROI) in each HSI. Quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), and support vector machine (SVM) classifiers were used to build classification models for comparing images and spectra of samples under different waterlogging levels among the three datasets, and to conduct training and prediction. From each dataset, 70% of the images were used for training, and the remaining 30% were used for testing. In the classification of full-wavelength HSIs (400–1000 nm), QDA and SVM exhibited high multivariate classification accuracy, reaching 77.37% and 95.90% accuracy, respectively. In contrast, KNN displayed low accuracy, but good identification and prediction ability for variety NZ 19. Six optimal wavebands of 529, 641, 698, 749, 856, and 979 nm were used as input for successive projections algorithm (SPA) classification and analysis. The QDA mode had better classification performance, with identification accuracies of 100% and 94.44%, respectively. Overall, the VNIR classification results exceeded those of image classification. These results show that hyperspectral imaging technology is feasible and useful for the detection of oilseed rape waterlogging stress.

Why it matches plant phenotyping methods油菜葉のハイパースペクトル画像から水ストレス状態を分類・検出する手法が研究の中心であり、画像・スペクトル取得、波長選択、分類モデルの比較と精度評価を行っている。

abstractThe objective of this study was to investigate the utility of hyperspectral images (HSIs) for the detection of oilseed rape waterlogging stress.

Crop biomass estimation using multi regression analysis and neural networks from multitemporal L-band polarimetric synthetic aperture radar data →

Biomass has a direct relationship with agricultural production and may help to predict crop yield. Earth observation technology can contribute significantly to monitoring given the availability of temporally frequent and high-resolution radar or optical satellite data. Polarimetric Synthetic Aperture Radar (PolSAR) has several advantages for operational monitoring given that at these longer wavelengths atmospheric and illumination conditions do not affect acquisitions and considering the sensitivity of microwaves to the structural properties of targets. Therefore, SARs are a promising source of data for crop mapping and monitoring. With increasing access to SARs the development of robust methods to monitor crop productivity is timely.In this paper, we examine the use of machine learning and artificial intelligence approaches to analyze a time series of Polarimetric parameters for crop biomass estimation. In total, 14 polarimetric parameters from a time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne L-band data were used for biomass estimation for an intensively cropped site in western Canada. Then, Multiple linear regression (MR) and artificial neural network (ANN) models were developed and evaluated to estimate the biomass for canola, corn, and soybeans. According to the experimental results, the ANN provided more accurate biomass estimates compared to MR.Canola biomass, in general, showed less sensibility to almost all the polarimetric parameters. Nevertheless, Freeman-Double combined with vertical-vertical backscattering (VV) delivered the correlation coefficient (r) of 0.72, and the root mean square error (RMSE) of 56.55 g m−2of canola biomass. For corn, the highest correlation was observed between a pairing of horizontal- horizontal backscattering (HH) with Entropy (H) for biomass estimation yielding an r of 0.92 and RMSE of 196.71 g m−2. Horizontal-vertical backscattering (HV) and Yamaguchi-Surface (OY) delivered the highest sensitivity for soybeans (r of 0.82 and RMSE of 13.48 g m−2). If all crops are pooled, H combined with OY provided the most accurate estimates of biomass (r of 0.89 and RMSE of 135.31 g m−2). These results demonstrated that models which make use of polarimetric parameters that characterize the multiple sources of scattering typical of vegetation canopies can be used to estimate crop biomass accurately. Such results bode well for agricultural monitoring considering the increasing number of satellite SAR sensors with various frequencies, imaging modes and revisit times. As such, the time series analysis and methods proposed in this study could be used to monitor crop development and productivity using SAR space technologies.

Why it matches plant phenotyping methodsUAVSARの時系列偏波データと回帰・ニューラルネットワークにより、作物バイオマスという植物形質を推定する手法を開発・評価しており、形質取得・推定法が研究の中心である。

abstractThen, Multiple linear regression (MR) and artificial neural network (ANN) models were developed and evaluated to estimate the biomass for canola, corn, and soybeans.

A high-throughput delayed fluorescence method reveals underling differences in the control of circadian rhythms in Triticum aestivum and Brassica napus →

Abstract Background A robust circadian clock has been implicated in plant resilience, resource-use efficiency, competitive growth and yield. A huge number of physiological processes are under circadian control in plants including: responses to biotic and abiotic stresses; flowering time; plant metabolism; and mineral uptake. Understanding how the clock functions in crops such as Triticum aestivum (bread wheat) and Brassica napus (oilseed rape) therefore has great agricultural potential. Delayed fluorescence (DF) imaging has been shown to be applicable to a wide range of plant species and requires no genetic transformation. Although DF has been used to measure period length of both mutants and wild ecotypes of Arabidopsis , this assay has never been systematically optimised for crop plants. The physical size of both B. napus and T. aestivum led us to develop a representative sampling strategy which enables high-throughput imaging of these crops. Results In this study, we describe the plant-specific optimisation of DF imaging to obtain reliable circadian phenotypes with the robustness and reproducibility to detect diverging periods between cultivars of the same species. We find that the age of plant material, light regime and temperature conditions all significantly effect DF rhythms and describe the optimal conditions for measuring robust rhythms in each species. We also show that sections of leaf can be used to obtain period estimates with improved throughput for larger sample size experiments. Conclusions We present an optimized protocol for high-throughput phenotyping of circadian period specific to two economically valuable crop plants. Application of this method revealed significant differences between the periods of several widely grown elite cultivars. This method also identified intriguing differential responses of circadian rhythms in T. aestivum compared to B. napus ; specifically the dramatic change to rhythm robustness when plants were imaged under constant light versus constant darkness. This points towards diverging networks underling circadian control in these two species.

Why it matches plant phenotyping methods作物の概日リズムを測定する遅延蛍光イメージング法を最適化し、再現性・頑健性を検証した植物フェノタイピング手法研究である。

abstractwe describe the plant-specific optimisation of DF imaging to obtain reliable circadian phenotypes with the robustness and reproducibility to detect diverging periods between cultivars of the same species.

Assessment of red-edge vegetation indices for crop leaf area index estimation →

This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.

Why it matches plant phenotyping methods作物のLAIという植物形質を対象に、RapidEye画像由来の植生指数を比較し、LAI推定モデルの性能と汎用性を評価しているため、形質取得手法が中心的です。

abstractThis study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs.

LiDARPheno – A Low-Cost LiDAR-Based 3D Scanning System for Leaf Morphological Trait Extraction →

The ever-growing world population brings the challenge for food security in the current world. The gene modification tools have opened a new era for fast-paced research on new crop identification and development. However, the bottleneck in the plant phenotyping technology restricts the alignment in geno-pheno development as phenotyping is the key for the identification of potential crop for improved yield and resistance to the changing environment. Various attempts to making the plant phenotyping a "high-throughput" have been made while utilizing the existing sensors and technology. However, the demand for 'good' phenotypic information for linkage to the genome in understanding the gene-environment interactions is still a bottleneck in the plant phenotyping technologies. Moreover, the available technologies and instruments are inaccessible, expensive, and sometimes bulky. This work attempts to address some of the critical problems, such as exploration and development of a low-cost LiDAR-based platform for phenotyping the plants in-lab and in-field. A low-cost LiDAR-based system design, LiDARPheno, is introduced in this work to assess the feasibility of the inexpensive LiDAR sensor in the leaf trait (length, width, and area) extraction. A detailed design of the LiDARPheno, based on low-cost and off-the-shelf components and modules, is presented. Moreover, the design of the firmware to control the hardware setup of the system and the user-level python-based script for data acquisition is proposed. The software part of the system utilizes the publicly available libraries and Application Programming Interfaces (APIs), making it easy to implement the system by a non-technical user. The LiDAR data analysis methods are presented, and algorithms for processing the data and extracting the leaf traits are developed. The processing includes conversion, cleaning/filtering, segmentation and trait extraction from the LiDAR data. Experiments on indoor plants and canola plants were performed for the development and validation of the methods for estimation of the leaf traits. The results of the LiDARPheno based trait extraction are compared with the SICK LMS400 (a commercial 2D LiDAR) to assess the performance of the developed system.

Why it matches plant phenotyping methods低コストLiDARを用いた植物表現型取得システムを開発し、葉形質の抽出アルゴリズムを提示、実験と商用LiDAR比較で検証しており、方法が研究の中心である。

abstractA low-cost LiDAR-based system design, LiDARPheno, is introduced in this work to assess the feasibility of the inexpensive LiDAR sensor in the leaf trait (length, width, and area) extraction.

Three Dimensional (3D) Reconstruction of Subterranean Clover →

Three dimensional (3D) plant reconstructions, extended to four dimensions with the use of time series and accompanied by visual modelling, is being used for a number of purposes including the estimation of biovolume and as the basis for functional structural plant modelling (FSPM). This has been successfully applied to crop species such as cotton (Paproki et al. 2012). Measuring the growth pattern and arrangement of a pasture sward is a difficult task but can be used as an indirect measure of other variables of interest, such as growth rate, light interception, nutritional quality, herbivore intake, etc. (Laca and Lemaire 2000). Digital representation of individual plants in three dimensions is one way to determine sward structure. The High Resolution Plant Phenomics Centre (HRPPC) has developed PlantScan™ which combines robotics, image analysis and computing advances, to accelerate and automate the measurement of plant growth characteristics and allow discrimination of differences between individual plants within species. Image silhouettes and LiDAR (Light Detection And Ranging) are used and combined to digitise plant architecture in three dimensions with a high level of detail. Colour information, extracted from multispectral sensors, and thermal imaging from infra-red (IR) cameras are then overlaid on these 3D plant representations, thus providing a tool to link plant structure to plant function. Successful reconstructions using data collected by PlantScan™ in controlled conditions, have been conducted for a range of grasses such as wheat (Triticum aestivum), rice (Oryza sativa), corn (Zea mays) and broadleaf species such as canola (Brassica napus), cotton (Gossypium hirsutum) and tobacco (Nicotiana tabacum). This suggests that modelling the sward structure of grass and legume pasture species should be equally achievable. This study explores the use of PlantScanTM to reconstruct 3D images of the important and common pasture legume, subterranean clover (Trifolium subterraneum) with a view to analysing their 3D structure in-silico.

Why it matches plant phenotyping methodsPlantScanを用いたロボット・画像解析・LiDARによる植物体の3D構造再構成と成長特性測定が研究の中心であり、植物表現型取得手法の実質的な適用研究である。

abstractThe High Resolution Plant Phenomics Centre (HRPPC) has developed PlantScan™ which combines robotics, image analysis and computing advances, to accelerate and automate the measurement of plant growth characteristics and allow discrimination of differences between individual plants within species.

Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology. →

Sclerotinia sclerotiorum , one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB images were acquired before and after being artificially inoculated with Sclerotinia sclerotiorum on oilseed rape leaves. New image registration and fusion methods based on scale-invariant feature transform (SIFT) were presented to construct a fused database using multi-model images. The changes of temperature distribution in different sections of infected areas were analyzed by processing thermal images, the maximum temperature difference (MTD) on a single leaf reached 1.7 degrees Celsius 24 h after infection. Four machine learning models were established using thermal images and fused images respectively, including support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and naïve Bayes (NB). The results demonstrated that the classification accuracy was improved by 11.3% after image fusion, and the SVM model obtained a classification accuracy of 90.0% on the task of classifying disease severity. The overall results indicated the UAV low-altitude remote sensing simulation platform equipped with multi-sensors could be used to early detect Sclerotinia sclerotiorum on oilseed rape leaves.

Why it matches plant phenotyping methods油糧ナタネ葉の病害状態・重症度を、UAVマルチセンサー画像、画像融合、機械学習で取得・推定する方法が研究の中心であり、植物病害フェノタイピング手法に該当する。

abstractan indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection

Automated image processing to support the analysis of between-year transmission of Leptosphaeria maculans in field conditions →

Understanding the transmission of inoculum between periods where the host plants are present is central for predicting the development of plant diseases and optimising mitigation strategies. However, the production at the end of the growing period, the survival during the intercrop period, and the emergence or emission of inoculum after sowing or planting can be highly variable, difficult to assess and generally inferred indirectly from symptoms data. As a result, there is a lack of large data sets which is a major brake for the study of these epidemiological processes. Here we focus on Leptosphaeria maculans that causes the black leg of oilseed rape. After having infected leaves, at early stages of the plant, and migrating into the stem, it causes a basal stem canker before harvest. It then survives on stubble left in the field from which ascospores are emitted at the beginning of the next growing period. In this study we first developed an image processing framework to estimate the density of fruiting bodies produced on stubble. Then, we used this framework to analyse automatically a large number of stems collected in oilseed rape fields among a cultivated area. Having performed a quality assessment of the processing chain we used the output data to investigate how the potential level of inoculum may change with the source field, the considered year and the stem canker severity at harvest. Besides the insights gain into the blackleg of oilseed rape, this work shows how image-based phenotyping may support epidemiological studies by increasing substantially the precision of high throughput disease data.

Why it matches plant phenotyping methods油糧ナタネ茎上の子実体密度という植物病害状態を画像処理で推定する枠組みを開発し、品質評価と大規模データへの適用を行っており、表現型取得法が中心です。

abstractwe first developed an image processing framework to estimate the density of fruiting bodies produced on stubble.

Estimating canola phenology using synthetic aperture radar →

Prolonged periods of wet soil conditions, when present during critical crop development stages, can significantly elevate the risk of some crop diseases. Wet soils in fields of flowering canola are a concern with respect to the development of sclerotinia as this pathogen feeds on the petals of the canola flower. As such, determining if canola is in bloom during periods of high moisture is important in deciding whether to take action to mitigate this disease. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization Synthetic Aperture Radar (SAR) data were used with a novel dynamic filtering framework to estimate canola growth stages. In this process, a new crop growth stage indicator was developed and SAR polarimetric parameters sensitive to changes in phenology were identified. Model development used multi-year SAR satellite and field data for one site in Manitoba, Canada. The crop growth estimator was then tested on unseen data from three sites, one in each of Canada's Prairie provinces. This independent validation established that the growth estimator was able to accurately determine canola growth stage and date of flowering with high accuracy. Correlation coefficients (r-values) between observed and estimated phenology ranged from 0.91 to 0.96. Given that this method performed well on test data from other sites and years, this approach could be widely adopted for monitoring the development of canola over extended regions.

Why it matches plant phenotyping methodsSARデータと動的フィルタリングによりカノーラの生育段階・開花日を推定する手法を開発し、複数地点・年のデータで独立検証しており、植物フェノタイピング手法が中心である。

abstracta new crop growth stage indicator was developed

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery. →

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.

Why it matches plant phenotyping methodsUAV画像からセグメンテーションと形状特徴抽出により、ナタネ幼苗の個体数・条列特性を推定する手法を開発し、精度検証しているため、植物表現型取得が中心です。

abstractThe main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings.

Pathotypes and phylogenetic variation determine downy mildew epidemics in Brassica spp. in Australia →

Isolates of Hyaloperonospora brassicae inoculated onto cotyledons of 28 diverse Brassicaceae genotypes, 13 from Brassica napus, two from B. juncea, five from B. oleracea, two from Eruca vesicaria, and one each from B. nigra, B. carinata, B. rapa, Crambe abyssinica, Raphanus sativus and R. raphanistrum, showed significant effects (P ≤ 0.001) of isolate, host and their interaction. Host responses ranged from no visible symptom or a hypersensitive response, to systemic spread and abundant pathogen sporulation. Isolates were generally most virulent on their host of origin. Using an octal classification, six host genotypes were identified as suitable host differentials to characterize pathotypes of H. brassicae and distinguished eight distinct pathotypes. There were fewer, but more virulent, pathotypes in 2015–2016 isolates than 2006–2008 pathogen populations, probably explaining the increase in severity of canola downy mildew over the past decade. Phylogenetic relationships determined across 20 H. brassicae isolates collected in 2006–2008 and 88 isolates collected in 2015–2016 showed seven distinct clades, with 70% of 2006–2008 isolates distributed within clade I (bootstrap value (BVs) of 100%) and the remaining 30% in clade V (BVs 83.3%). This is the first study to define phylogenetic relationships of H. brassicae isolates in Australia, setting a benchmark for understanding current and future genetic shifts within pathogen populations; it is also the first to use octal classification to characterize pathotypes of H. brassicae, providing a novel basis for standardizing phenotypic characterization and monitoring of pathotypes on B. napus and some crucifer species in Australia.

Why it matches plant phenotyping methods宿主基因型上的症状反応を用いた病原菌病型の八進分類法を開発・適用し、病害表現型の標準化とモニタリングを主題としているため、単なる病害実験ではない。

abstractUsing an octal classification, six host genotypes were identified as suitable host differentials to characterize pathotypes of H. brassicae and distinguished eight distinct pathotypes.

Integrating plant ontogeny and structure in Brassica napus L. I. Forward phenomics →

Defining a minimum set of phenotypic traits that can integrate ontogeny and structure of Brassica napus L. is required for breeding and selection of high yielding and adapted genotypes to the short growing season of the upper Midwest, USA. Forward phenomics was instrumental in striking a balance between accuracy, timing and speed of capturing multi-level, spatiotemporal data at different scales of integration. Quantitative and categorical data digitally recorded, measured or scored on whole canopies, single plants, single leaves, and single siliques; and on random mature seed samples of entries in a phenotyping nursery of B. napus were used to identify plant traits that can integrate the effects of time (ontogeny) and space (architecture) on oil%, and to develop a multilevel-multitrait protocol based on field and laboratory characterization of phenotypic and agronomic data while accounting for fixed and random sources of variation when interpreting components of phenotypic variance. Traits conferring tolerance to low temperatures during germination and early seedling growth included fast emergence, early vigor, early flowering combined with short duration of bolting-to-flowering, and early maturity. To approximate rapeseed yield potential in the upper Midwest, USA, genotypes with biomass > 6.0 Mg ha⁻¹, seed > 3.5 Mg ha⁻¹, oil > 1.75 Mg ha⁻¹ and protein yield > 0.75 Mg ha⁻¹ are envisioned. A subset of adaptive traits has been identified that can be combined in a selection index to develop a plant ideotype for B. napus.

Why it matches plant phenotyping methodsB. napusの複数器官・時空間形質を統合するフォワードフェノミクスと、マルチレベル・マルチ形質プロトコルの開発が研究の中心である。

abstractForward phenomics was instrumental in striking a balance between accuracy, timing and speed of capturing multi-level, spatiotemporal data at different scales of integration.

Multi-Focus Fusion Technique on Low-Cost Camera Images for Canola Phenotyping. →

To meet the high demand for supporting and accelerating progress in the breeding of novel traits, plant scientists and breeders have to measure a large number of plants and their characteristics accurately. Imaging methodologies are being deployed to acquire data for quantitative studies of complex traits. Images are not always good quality, in particular, they are obtained from the field. Image fusion techniques can be helpful for plant breeders with more comfortable access plant characteristics by improving the definition and resolution of color images. In this work, the multi-focus images were loaded and then the similarity of visual saliency, gradient, and color distortion were measured to obtain weight maps. The maps were refined by a modified guided filter before the images were reconstructed. Canola images were obtained by a custom built mobile platform for field phenotyping and were used for testing in public databases. The proposed method was also tested against the five common image fusion methods in terms of quality and speed. Experimental results show good re-constructed images subjectively and objectively performed by the proposed technique. The findings contribute to a new multi-focus image fusion that exhibits a competitive performance and outperforms some other state-of-the-art methods based on the visual saliency maps and gradient domain fast guided filter. The proposed fusing technique can be extended to other fields, such as remote sensing and medical image fusion applications.

Why it matches plant phenotyping methodsキャノーラのフィールド表現型画像を対象に、低コストカメラ画像のマルチフォーカス融合手法を開発・比較評価しており、画像取得・品質改善が中心的な方法論的貢献である。

titleMulti-Focus Fusion Technique on Low-Cost Camera Images for Canola Phenotyping.

Detection of Sclerotinia Stem Rot on Oilseed Rape ( Brassica napus L.) Leaves Using Hyperspectral Imaging. →

Hyperspectral imaging was explored to detect Sclerotinia stem rot (SSR) on oilseed rape leaves with chemometric methods, and the influences of variable selection, machine learning, and calibration transfer methods on detection performances were evaluated. Three different sample sets containing healthy and infected oilseed rape leaves were acquired under different imaging acquisition parameters. Four discriminant models were built using full spectra, including partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), soft independent modeling of class analogies (SIMCA), and k-nearest neighbors (KNN). PLS-DA and SVM models were also built with the optimal wavelengths selected by principal component analysis (PCA) loadings, second derivative spectra, competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA). The optimal wavelengths selected for each sample set by different methods were different; however, the optimal wavelengths selected by PCA loadings and second derivative spectra showed similarity between different sample sets. Direct standardization (DS) was successfully applied to reduce spectral differences among different sample sets. Overall, the results demonstrated that using hyperspectral imaging with chemometrics for plant disease detection can be efficient and will also help in the selection of optimal variable selection, machine learning, and calibration transfer methods for fast and accurate plant disease detection.

Why it matches plant phenotyping methods植物葉の病害状態を対象に、ハイパースペクトル画像とケモメトリクスによる検出法、変数選択、機械学習、校正転移を評価しており、病害フェノタイピング手法が中心です。

abstractHyperspectral imaging was explored to detect Sclerotinia stem rot (SSR) on oilseed rape leaves with chemometric methods, and the influences of variable selection, machine learning, and calibration transfer methods on detection performances were evaluated.

Assessing leaf nitrogen concentration of winter oilseed rape with canopy hyperspectral technique considering a non-uniform vertical nitrogen distribution →

Timely estimation of the vertical heterogeneity of leaf nitrogen concentration (LNC) from canopy reflectance using hyperspectral sensing is important for precision N management during winter oilseed rape productivity. However, current research pays little attention to LNC assessments by only taking LNC’s vertical distribution into consideration, leading to limited accuracy and reduced applied value of the results. The main goal of this work was to quantitatively define the contributions of LNC in different layers to winter oilseed rape canopy raw (R) hyperspectra and to its transformation technique (i.e., first derivative reflectance, FDR), and develop a monitoring model considering the vertical LNC gradient using spectral data. Two field experiments were conducted for two consecutive years (2015–2017) with different N rates, cultivars and growth stages. At seedling and budding stage, canopy hyperspectral reflectance and LNC were measured in situ. Canopies of each treatment were divided into three layers of equal vertical (upper, middle, lower). Partial least square (PLS), lambda-lambda r2 (LL r2) and support vector machine (SVM) models were used to analyze the relationships between LNC in different layers and the hyperspectral reflectance measured from above the canopy. Field sampling revealed that a vertical distribution pattern of LNC existed, presenting an evident decline from the upper to lower layer. The FDR-PLS model for LNC prediction in different layers yielded a relatively higher accuracy compared to the R-PLS based on the full range hyperspectra, the coefficient of determination (r2val) was 0.872 for LNC in the upper layer, 0.903 in the middle layer, and 0.837 in the lower layer, with a relative percent deviation (RPD val) of 2.794, 3.052, and 2.328, respectively. Finally, seven (437, 565, 667, 724, 993, 1084 and 1189 nm), six (423, 570, 598, 659, 725 and 877 nm), and five bands (420, 573, 597, 667 and 718 nm) were identified as effective wavelengths for assessing the vertical LNC distribution in the upper, middle and lower layer, respectively. The newly-developed SVM-FDR regression model using the effective wavelengths also performed well for upper (r2val = 0.828, RPD val = 2.358), middle (r2val = 0.844, RPD val = 2.556), and lower (r2val = 0.781, RPD val = 2.029) layer LNC prediction. Our results indicate that estimation of LNC using hyperspectral reflectance data is most effective for the upper and middle layers of oilseed rape canopies. Moreover, the calibration model developed in this study has great potential to assess the N status of the whole oilseed rape canopy.

Why it matches plant phenotyping methodsキャノピー hyperspectral sensing と回帰モデルを開発・評価し、葉窒素濃度という植物生理形質を層別に推定しているため、フェノタイピング手法が研究の中心である。

abstractThe main goal of this work was to quantitatively define the contributions of LNC in different layers to winter oilseed rape canopy raw (R) hyperspectra and to its transformation technique (i.e., first derivative reflectance, FDR), and develop a monitoring model considering the vertical LNC gradient using spectral data.

Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data →

Information on crop phenological development stages such as emergence, flowering, fruiting, maturing and senescence is essential for crop production surveillance and yield prediction. It has long been related to optical spectral signatures such as the Normalized Difference Vegetation Index (NDVI) or spectral shifts in the red-edge range. In recent years, more efforts have been made to explore the sensitivity of Synthetic Aperture Radar (SAR), particularly polarimetric SAR signatures, to crop biophysical parameters or phenological stages. In this study, phenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data. Both crops are very common in north eastern Ontario, Canada, but have very anatomically different development processes. From multi-temporal RADARSAT-2 data acquired in three consecutive years (2012–2014), significant correlations were observed between a number of SAR polarimetric parameters and the growth parameters of both crops. Strong correlation was observed between plant height and the Alpha angle of the Cloude-Pottier decomposition, with the R² of 0.91 and 0.66 for canola and wheat, respectively. The R² increased when the polarimetric parameters were smoothed in the time domain (R² of 0.98 for canola and 0.88 for wheat). Strong correlation was also observed for the two crops between the effective leaf area index (LAIe) and the Beta angle, and between days-after-seeding (DAS) and a combination of the Alpha and the Beta angles. These findings show that multi-temporal C-band polarimetric SAR parameters could be used for tracking crop phenological development stages.

Why it matches plant phenotyping methodsマルチテンポラル偏波SARを用いて作物の生育段階や草高・葉面積指数を推定・追跡し、相関と性能を評価しており、表現型取得手法が中心である。

abstractphenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data.

Mixed-Effects Estimation in Dynamic Models of Plant Growth for the Assessment of Inter-individual Variability →

Modeling inter-individual variability in plant populations is a key issue to understand crop heterogeneity and its variations in response to the environment. Being able to describe the interactions among plants and explain the variability observed in the population could provide useful information on how to control it and improve global plant growth. We propose here a method to model plant variability within a field, by extending the so-called GreenLab functional-structural plant model from the individual to the population scale via nonlinear mixed-effects modeling. Parameter estimation of the population model is achieved using the stochastic approximation expectation maximization algorithm, implemented in the platform for plant growth modeling and analysis PyGMAlion. The method is first applied on a set of simulated data and then on a real dataset from a population of 34 winter oilseed rape plants at the rosette stage. Results show that our method allows for a good characterization of the variability in the population with only a limited number of parameters, which is a key point for plant models. Results on simulated data show that parameters associated with a low sensitivity index are inaccurately estimated by the algorithm when considered as random effects, but a good stability of the results can be obtained by considering them as fixed effects. These results open new ways for the analysis of inter-plant variability within a population and the study of plant–plant competition.Supplementary materials accompanying this paper appear online.

Why it matches plant phenotyping methods植物集団の成長・個体間変異を推定する非線形混合効果モデルを開発し、シミュレーションおよび実データで検証・適用している。植物成長モデルに基づく形質推定が研究の中心であり、単なる生物学的実験の routine 測定ではない。

abstractWe propose here a method to model plant variability within a field, by extending the so-called GreenLab functional-structural plant model from the individual to the population scale via nonlinear mixed-effects modeling.

A bioassay for prosulfocarb, pyroxasulfone and trifluralin detection and quantification in soil and crop residues →

Three experiments were conducted to develop a bioassay method for assessing the bioavailability of prosulfocarb, pyroxasulfone and trifluralin in both crop residue and soil. In preliminary experiments, Italian ryegrass (Lolium multiflorum Lam.), cucumber (Cucumis sativus L.) and beetroot (Beta vulgaris L.) were tested as bioassay plant species for the three pre-emergent herbicides. Four growth parameters (shoot length, root length, fresh weight and dry weight) were measured for all plant species. Shoot-length inhibition was identified as the most responsive to the herbicide application rates. Italian ryegrass was the most sensitive species to all tested herbicides, whereas beetroot and cucumber had lower and similar sensitivity to shoot inhibition for the three herbicides. The bioassay species performed similarly in wheat and canola residues collected a few days after harvest. In bioassay calibration experiments, dose–response curves were developed for prosulfocarb, pyroxasulfone and trifluralin in a sandy loam soil typical of the grain belt of Western Australia and with wheat residue. The developed bioassay uses ryegrass shoot inhibition for relatively low suspected concentrations of herbicide, and cucumber shoot inhibition for higher rates. The bioassay was validated by spraying the three herbicides separately onto wheat residue and soil and comparing the concentrations derived from chemical analysis with those from the bioassay. All of the linear correlations between concentrations derived from chemical analyses and the bioassays were highly significant. These results indicate that the bioassay calibration curves are suitable for estimating herbicide concentrations in crop residue collected soon after harvest and a sandy-loam soil, low in organic matter.

Why it matches plant phenotyping methods除草剤濃度を推定するため、植物の生育阻害（特にシュート長）を測定するバイオアッセイを開発・較正・検証しており、表現型取得が研究の中心である。

abstractThree experiments were conducted to develop a bioassay method for assessing the bioavailability of prosulfocarb, pyroxasulfone and trifluralin in both crop residue and soil.

3D Reconstruction of Lipid Droplets in the Seed of Brassica napus →

Rapeseed is one of the most important and widely cultured oilseed crops for food and nonfood purposes worldwide. Neutral lipids are stored in lipid droplets (LDs) as fuel for germination and subsequent seedling growth. Most of the LD detection in seeds was still in 2D levels, and some of the details might have been lost in previous studies. In the present work, the configuration of LDs in seeds was obtained by confocal imaging combined with 3D reconstruction technology in Brassica napus. The size and shape of LDs, LD numbers, cell interval spaces and cell size were observed and compared at 3D levels in the seeds of different materials with high and low oil content. It was also revealed that different cells located in the same tissue exhibited various oil contents according to the construction at the 3D level, which was not previously reported in B. napus. The present work provides a new way to understand the differential in cell populations and enhance the seed oil content at the single cell level within seeds.

Why it matches plant phenotyping methods種子内の脂質滴を対象に、共焦点イメージングと3D再構築を組み合わせた形態・数量計測手法を中心的に適用しており、植物器官の表現型取得が研究の主要目的である。

abstractthe configuration of LDs in seeds was obtained by confocal imaging combined with 3D reconstruction technology in Brassica napus.

Rapid Phenotyping of Seed Oil Content →

Source: Adobe Stock. While there are great advances in crop genotyping, many research programs still depend upon the selection of plants or seeds based on the phenotype. This can involve sorting through thousands of samples by hand. Finding ways to automate sorting based on phenotype can increase the productivity of plant breeders, freeing up time to do other tasks. Albrecht Melchinger, ASA and CSSA member and Professor of Applied Genetics and Plant Breeding at the University of Hohenheim in Germany, uses seed phenotyping in his work. One example is the use of a color marker, “where you can see from the embryo coloration whether it's a haploid seed or a diploid seed. The haploid seeds are, in this case, white and the other ones are purple.” However, some germplasm is naturally purple and could not be used in the breeding program. To solve this problem, Melchinger and colleagues developed an inducer with high oil content. Haploid seeds would have normal oil content while the diploid seed had higher oil content. While color was no longer limiting the germplasm that could be used, researchers still had to perform the time-consuming task of analyzing individual seeds to determine oil content. To speed this process, these researchers have created a platform for determining oil content. Although they were working with maize, they realized this automated, high-throughput system had the potential to benefit breeders working with other oil crops. Oil crop breeders are often trying to increase oil content, “and it would be very nice if you had measurements of individual seeds in a nondestructive manner,” Melchinger says. An article recently published in Crop Science (http://bit.ly/2FLCqAX) describes this phenotyping platform for measuring the oil content of seeds and tests accuracy across a range of oil crops. Researchers used the platform to measure the oil content of canola, castor bean, cotton, jatropha, maize, soy, and sunflower. The platform has four modules (Fig. 1). The first separates individual seeds from a larger sample using suction. Depending upon the size and shape of the seed, the pneumatic pressure required to select a single seed needs to be adjusted. The second module determines seed mass. Mass is measured on a balance, and it is key to keep this clean and free from debris. Oil mass is measured in the third module using commercial TD-NMR (time domain nuclear magnetic resonance) equipment. A computer then calculates oil content from oil mass and seed mass data. This step is also one that needs to be adjusted based on seed size. Flow chart of the seeds through the modules of the high-throughput platform. NMR, nuclear magnetic resonance. The final module sorts seeds based on oil content, which can be done in two different ways. One approach is to set categories. For example, when sorting based on oil content to separate haploid and diploid seeds, a user can sort seeds into two categories. Alternatively, the module will measure each seed and set them on a tray in a grid pattern. The computer tracks the placement of each seed. A researcher can then query the dataset, for example identifying the top 10% of seeds based on oil content. Seeds meeting the selected criteria are identified by LED lights, which are located under each seed. In testing the system with these seven oil crops, the researchers report that their high-throughput phenotyping platform has high accuracy. Because the process is fully automated, a user can load seeds for analysis and walk away. “We do it very often overnight,” Melchinger says. He explains the pneumatic system that moves seeds through the modules is the key development, and the researchers have applied for a patent on this technology. They are also developing a manual that will outline how settings should be adjusted based on seed size and shape when using this platform for different crops. Melchinger sees this platform being useful beyond measuring oil content. “Our system makes use of NMR, but it is not confined to NMR,” he says. As technology is developed to analyze other traits, there is potential to switch the components while maintaining the high-throughput functionality of the platform. Slide 1: Module 1: A feeder (or hopper) is filled with seeds and the separator uses pneumatic pressure to select a single seed at a time for processing. Slide 2: Module 2: Seeds are weighed on a mass balance. Slide 3: Module 3: The NMR machine, where oil mass is determined. Seeds are transported into and out of the NMR machine using pneumatic pressure. Slide 4: Module 4a: Seed sorting based on pre-established categories. Seeds can be sorted into as many as six defined categories. Slide 5: Module 4b: Seed sorting onto a tray. Data for each seed are stored in the computer, and a user can define criteria for selection. LED lights under tray identify seeds that meet the criteria. Slide 6: LED selection grid in Module 4b: Seeds with light shining below meet user-defined selection criteria. Check out the Crop Science article, “High-Throughput Precision Phenotyping of the Oil Content of Single Seeds of Various Oilseed Crops” at: http://bit.ly/2FLCqAX.

Why it matches plant phenotyping methods種子油含量を個別・非破壊・高スループットで測定および選別するプラットフォームの開発、精度評価、複数作物への適用が中心であり、植物フェノタイピング手法に該当する。

abstractTo speed this process, these researchers have created a platform for determining oil content.

Near-infrared reflectance spectroscopy calibrations for assessment of oil, phenols, glucosinolates and fatty acid content in the intact seeds of oilseed Brassica species. →

Background Very few near-infrared reflectance spectroscopy (NIRS) calibration models are available for non-destructive estimation of seed quality traits in Brassica juncea. Those that are available also fail to adequately discern variation for oleic acid (C 18:1 ) , linolenic (C 18:3 ) fatty acids, meal glucosinolates and phenols. We report the development of a new NIRS calibration equation that is expected to fill the gaps in the existing NIRS equations. Results Calibrations were based on the reference values of important quality traits estimated from a purposely selected germplasm set comprising 240 genotypes of B. juncea and 193 of B. napus. We were able to develop optimal NIRS-based calibration models for oil, phenols, glucosinolates, oleic acid, linoleic acid and erucic acid for B. juncea and B. napus. Correlation coefficients (RSQ) of the external validations appeared greater than 0.7 for the majority of traits, such as oil (0.766, 0.865), phenols (0.821, 0.915), glucosinolates (0.951, 0.986), oleic acid (0.814. 0.810), linoleic acid (0.974, 0.781) and erucic acid (0.963, 0.943) for B. juncea and B. napus, respectively. Conclusion The results demonstrate the robust predictive power of the developed calibration models for rapid estimation of many quality traits in intact rapeseed-mustard seeds which will assist plant breeders in effective screening and selection of lines in quality improvement breeding programmes. © 2018 Society of Chemical Industry.

Why it matches plant phenotyping methods近赤外分光法による種子品質形質推定の校正モデルを開発し、外部検証しており、植物形質の取得・推定法が研究の中心である。

titleNear-infrared reflectance spectroscopy calibrations for assessment of oil, phenols, glucosinolates and fatty acid content in the intact seeds of oilseed Brassica species.

High‐Throughput Precision Phenotyping of the Oil Content of Single Seeds of Various Oilseed Crops →

High‐throughput (HT) precision phenotyping of agronomic traits is important for well‐founded, rapid selection decisions in plant breeding. This applies especially to nondestructive measurement of single‐seed oil content, for which an HT platform has recently become available. The objectives of this study were (i) to evaluate the suitability of this HT platform for measuring seed mass, oil mass, and oil content in various oil crops, (ii) to determine the accuracy and repeatability of the measurements, and (iii) to discuss technical adjustments required for specific crops. Seeds of canola ( Brassica napus L.), castor bean ( Ricinus communis L.), cotton ( Gossypium hirsutum L), jatropha ( Jatropha curcas L.), maize ( Zea mays L.), soybean [ Glycine max (L.) Merr.], and sunflower ( Helianthus annuus L.) were measured repeatedly using a randomized complete block design. Additionally, the oil content of bulks of seeds from two crops was determined by wet chemistry analysis. Repeatability of all three traits recorded via the HT platform generally exceeded 98% in all seven crops. Oil content of bulks determined by wet chemistry analysis was almost perfectly correlated ( R 2 > 99.9%) with the mean of nuclear magnetic resonance (NMR) measurements of single seeds from these bulks. To warrant precise results and smooth operation, yielding an average throughput of ∼600 seeds h −1 , technical modifications in certain modules of the HT platform are required to accommodate the size, geometry, and oil content of seeds from different crops. In conclusion, the HT platform demonstrated high repeatability and accuracy of measurements, which opens up several fields of application in plant breeding.

Why it matches plant phenotyping methods単一種子の油含量・質量を測定する高スループット表現型解析プラットフォームについて、精度・反復性の評価と作物別の技術改良を中心に検証しているため。

abstractHigh‐throughput (HT) precision phenotyping of agronomic traits is important for well‐founded, rapid selection decisions in plant breeding.

A laboratory high-throughput glass chamber using dynamic headspace TD-GC/MS method for the analysis of whole Brassica napus L. plantlet volatiles under cadmium-related abiotic stress. →

Introduction The dynamic headspace sampling technique using thermal desorption, gas chromatography-mass spectrometry (TD-GC/MS) is a powerful method for analysing plant emissions of volatile organic compounds (VOCs), and experiments performed in sterile and controlled conditions can be useful for VOC metabolism investigations. Objective The main purpose of this study was to set up a laboratory high-throughput glass chamber for whole plant volatiles analysis. Brassica napus L. plantlets were tested with the developed system to better understand the relationship between low emission of induced terpene and cadmium (Cd)-related abiotic stress. Methodology VOCs emitted by 28-day-old Brassica napus L. plantlets cultivated in vitro were trapped with our device using adsorbent cartridges that were desorbed with a thermal desorption unit before cryofocusing with a cooled injection system and programmable temperature vaporising inlet into an HP-5 ms GC column. Terpene detection and quantitation from chromatogram profiles were acquired using selected ion monitoring (SIM) mode during full scan analysis and mass spectra were obtained with a quadrupole-type mass spectrometer. Results The new trapping method produced reliable qualitative profiles of oilseed rape VOCs. Typical emissions of monoterpenes (myrcene, limonene) and sesquiterpenes (β-elemene, (E,E)-α-farnesene) were found for the different concentrations tested. One-way analysis of variance for quantitative results of (E,E)-α-farnesene emission rates showed a Cd concentration effect. Conclusion This inexpensive glass chamber has potential for wide application in laboratory sterile approach and replicated research. Moreover, the non-invasive dynamic sampling technique could also be used to analyse volatiles under both abiotic and biotic stresses.

Why it matches plant phenotyping methods植物体の揮発性有機化合物（VOC）をストレス関連の生理状態として取得する高スループット測定系を開発・検証しており、測定手法が研究の中心である。

abstractThe main purpose of this study was to set up a laboratory high-throughput glass chamber for whole plant volatiles analysis.

Phenotyping roots in darkness: disturbance-free root imaging with near infrared illumination. →

Root systems architecture (RSA) and size properties are essential determinants of plant performance and need to be assessed in high-throughput plant phenotyping platforms. Thus, we tested a concept that involves near-infrared (NIR) imaging of roots growing along surfaces of transparent culture vessels using special long pass filters to block their exposure to visible light. Two setups were used to monitor growth of Arabidopsis, rapeseed, barley and maize roots upon exposure to white light, filter-transmitted radiation or darkness: root growth direction was analysed (1) through short-term cultivation on agar plates, and (2) using soil-filled transparent pots to monitor long-term responses. White light-triggered phototropic responses were detected for Arabidopsis in setup 1, and for rapeseed, barley and maize roots in setups 1 and 2, whereas light effects could be avoided by use of the NIR filter thus confirming its suitability to mimic darkness. NIR image-derived 'root volume' values correlated well with root dry weight. The root system fractions visible at the different pot sides and in different zones revealed species- and genotype-dependent variation of spatial root distribution and other RSA traits. Following this validated concept, root imaging setups may be integrated into shoot phenotyping facilities in order to enable root system analysis in the context of whole-plant performance investigations.

Why it matches plant phenotyping methodsNIR画像を用いた根系形態（RSA）取得法を開発・検証し、根容積と乾物重の相関や既存施設への統合可能性を評価しており、表現型取得法が研究の中心である。

abstractwe tested a concept that involves near-infrared (NIR) imaging of roots growing along surfaces of transparent culture vessels

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems. →

Hyperspectral imaging covering the spectral range of 384-1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems.

Why it matches plant phenotyping methods植物体の病害状態をハイパースペクトル画像と化学計量モデルで非破壊検出する手法の開発・比較が中心であり、植物フェノタイピング方法論に該当する。

abstractHyperspectral imaging covering the spectral range of 384-1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems

Remote estimation of crop canopy parameters by statistical regression algorithms for winter rapeseed using Sentinel-2 multispectral images →

Еstimation of crop canopy parameters is important task for remote sensing monitoring of agriculture and constructing strategies for within-field management. The main objective of this study is to evaluate the retrieval from Sentinel-2 images by parametric and non-parametric statistical models several crop canopy parameters for monitoring before winter and after winter rapeseed crop in real farming conditions of North East Bulgaria. For the calibration of the models in-situ data from three field campaigns is used. For most of the studied parameters models with good accuracy were identified, except for aboveground fresh biomass. The best identified model for vegetation fraction (RMSEcv=0.14%) and plant density (RMSEcv=9 nb/m2) were parametric models with three band vegetation index (3BSI-Tian) and linear fitting function for the first, three band vegetation index (3BSI-Verreslt) and polynomial for the second parameter. For aboveground dry biomass (RMSEcv=52 g/m²), mean plant height (RMSEcv=4cm) and nitrogen concentration in fresh biomass (RMSEcv=2%) the best models were non-parametric, Gaussian Processes Regression for the first parameter and Variational Heteroscedastic variant of the Gaussian Processes Regression for the other two.

Why it matches plant phenotyping methodsSentinel-2画像と統計回帰モデルを用いて、菜種の植生被覆、密度、バイオマス、草丈、窒素濃度などの作物形質を推定し、現地データでモデル精度を評価しているため、形質取得手法が研究の中心である。

titleRemote estimation of crop canopy parameters by statistical regression algorithms for winter rapeseed using Sentinel-2 multispectral images

Ability of models with effective wavelengths to monitor nitrogen and phosphorus status of winter oilseed rape leaves using in situ canopy spectroscopy →

Till date, studies using canopy hyperspectral data to monitor crop nutrient status have focused mainly on biomass, water and nitrogen (N) prediction, and only a few have attempted to monitor phosphorus (P). This study aimed to evaluate the potential of the canopy raw spectra (R) in combination with a partial least square (PLS) regression model for estimating the leaf N and P concentration (LNC and LPC), compared to the potential of other hyperspectral transformation techniques such as log-transformed spectra (Log(1/R)), the continuum removal (CR) method and first derivative reflectance (FDR) for winter oilseed rape. Field experiments were conducted over three consecutive growing seasons (2013–2016) at different sites (Wuxue, Wuhan and Shayang) in Hubei, China, using different N and P application rates, planting patterns, cultivars and ecological sites. Data from the conventionally managed fields of 25 farmers in 2015–2016 were also collected to test the transferability of the established optimal monitoring model for LNC and LPC prediction. Canopy hyperspectral reflectance data were acquired over a wavelength range from 400 to1300nm (the visible and near-infrared region, VNIR), and quantitative correlations between LNC and LPC and their spectra were determined. The results showed that the FDR-PLS model yielded the highest retrieval accuracy for LNC and LPC predictions. The coefficient of determination of the validation dataset (r2val) between the observations and predictions was 0.89 for LNC and 0.82 for LPC, with a relative percent deviation (RPDval) of 2.41 and 2.22, respectively. The variable importance in projection (VIP) values of the FDR-PLS model with full spectral range were applied to identify the effective wavelengths and to decrease the high dimensionality of the canopy hyperspectral reflectance dataset. Seven wavelengths centred at 445, 556, 657, 764, 985, 1082, and 1194nm and six wavelengths at 755, 832, 891, 999, 1196, and 1267nm were identified as effective wavelengths for predicting the LNC and LPC values. The newly-developed FDR-PLS models for LNC (r2val=0.85, RPDval=2.10) and LPC (r2val=0.78, RPDval=1.94) provided accurate estimations based on field experiment validations using the effective wavelengths. The validation in the farmers’ fields also indicated an excellent accuracy between the observed and predicted values for LNC (r2val=0.82, RPDval=2.09) and LPC (r2val=0.75, RPDval=2.01). The overall results demonstrated the applicability and feasibility of the FDR-PLS model for estimating the N and P status of winter oilseed rape using in situ canopy hyperspectral reflectance data.

Why it matches plant phenotyping methodsキャノピー分光とFDR-PLSモデルを開発・検証し、冬ナタネ葉の窒素・リン濃度という植物状態を推定する手法が研究の中心である。

abstractThis study aimed to evaluate the potential of the canopy raw spectra (R) in combination with a partial least square (PLS) regression model for estimating the leaf N and P concentration (LNC and LPC)

Detection of Water Content in Rapeseed Leaves Using Terahertz Spectroscopy. →

The terahertz (THz) spectra of rapeseed leaves with different water content (WC) were investigated. The transmission and absorption spectra in the range of 0.3-2 THz were measured by using THz time-domain spectroscopy. The mean transmittance and absorption coefficients were applied to analyze the change regulation of WC. In addition, the Savitzky-Golay method was performed to preprocess the spectra. Then, the partial least squares (PLS), kernel PLS (KPLS), and Boosting-PLS were conducted to establish models for predicting WC based on the processed transmission and absorption spectra. Reliable results were obtained by these three methods. KPLS generated the best prediction accuracy of WC. The prediction coefficient correlation (Rval) and root mean square error (RMSEP) of KPLS based on transmission were Rval = 0.8508, RMSEP = 0.1015, and that based on absorption were Rval = 0.8574, RMSEP = 0.1009. Results demonstrated that THz spectroscopy combined with modeling methods provided an efficient and feasible technique for detecting plant physiological information.

Why it matches plant phenotyping methodsTHz分光と回帰モデリングにより、 rapeseed葉の含水量という植物生理形質を推定する測定手法を開発・比較検証しており、表現型取得が中心である。

abstractThe terahertz (THz) spectra of rapeseed leaves with different water content (WC) were investigated.

Chlorophyll fluorescence as a tool for nutrient status identification in rapeseed plants. →

In natural conditions, plants growth and development depends on environmental conditions, including the availability of micro- and macroelements in the soil. Nutrient status should thus be examined not by establishing the effects of single nutrient deficiencies on the physiological state of the plant but by combinations of them. Differences in the nutrient content significantly affect the photochemical process of photosynthesis therefore playing a crucial role in plants growth and development. In this work, an attempt was made to find a connection between element content in (i) different soils, (ii) plant leaves, grown on these soils and (iii) changes in selected chlorophyll a fluorescence parameters, in order to find a method for early detection of plant stress resulting from the combination of nutrient status in natural conditions. To achieve this goal, a mathematical procedure was used which combines principal component analysis (a tool for the reduction of data complexity), hierarchical k-means (a classification method) and a machine-learning method-super-organising maps. Differences in the mineral content of soil and plant leaves resulted in functional changes in the photosynthetic machinery that can be measured by chlorophyll a fluorescent signals. Five groups of patterns in the chlorophyll fluorescent parameters were established: the 'no deficiency', Fe-specific deficiency, slight, moderate and strong deficiency. Unfavourable development in groups with nutrient deficiency of any kind was reflected by a strong increase in F o and ΔV/Δt 0 and decline in φ Po , φ Eo δ Ro and φ Ro . The strong deficiency group showed the suboptimal development of the photosynthetic machinery, which affects both PSII and PSI. The nutrient-deficient groups also differed in antenna complex organisation. Thus, our work suggests that the chlorophyll fluorescent method combined with machine-learning methods can be highly informative and in some cases, it can replace much more expensive and time-consuming procedures such as chemometric analyses.

Why it matches plant phenotyping methodsクロロフィル蛍光と機械学習を組み合わせ、植物の栄養欠乏・ストレス状態を早期検出する方法を中心に開発・評価しているため、植物フェノタイピング手法として採用する。

abstractin order to find a method for early detection of plant stress resulting from the combination of nutrient status in natural conditions

Identification of isolates of the plant pathogen Leptosphaeria maculans with resistance to the triazole fungicide fluquinconazole using a novel In Planta assay. →

Leptosphaeria maculans is the major pathogen of canola (oilseed rape, Brassica napus) worldwide. In Australia, the use of azole fungicides has contributed to the 50-fold increase in canola production in the last 25 years. However, extensive application of fungicides sets the stage for the selection of fungal populations with resistance. A high-throughput in planta assay was developed to allow screening of thousands of isolates from multiple populations. Using this screen, isolates were identified with decreased sensitivity to the fungicide fluquinconazole when applied at field rates as a protective seed dressing: these isolates cause significantly larger lesions on cotyledons and true leaves and increased disease severity at plant maturity. This increased in planta resistance was specific to fluquinconazole, with no cross resistance to flutriafol or tebuconazole/prothioconazole. In a limited set of 22 progeny from a cross between resistant and susceptible parents, resistance segregated in a 1:1 ratio, suggesting a single gene is responsible. A survey of 200 populations from across canola growing regions of Australia revealed fungicide resistance was present in 15% of the populations. Although in vitro analysis of the fungicide resistant isolates showed a significant shift in the average EC50 compared to the sensitive isolates, this was not as evident as the in planta assays. The development of this novel, high-throughput in planta assay has led to the identification of the first fungicide resistant L. maculans isolates, which may pose a threat to the productivity of the Australian canola industry.

Why it matches plant phenotyping methods植物体上の病斑・病害重症度を測定する新規ハイスループット表現型アッセイの開発が中心であり、病原体分子検出だけでなく感染植物の症状を評価しているため。

abstractA high-throughput in planta assay was developed to allow screening of thousands of isolates from multiple populations.

Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves →

Automatic detection and monitoring of freezing injury in crops is of vital importance for assessing plant physiological status and yield losses. This study investigates the potential of hyperspectral techniques for detecting leaves at the stages of freezing and post-thawing injury, and for quantifying the impacts of freezing injury on leaf water and pigment contents. Four experiments were carried out to acquire hyperspectral reflectance and biochemical parameters for oilseed rape plants subjected to freezing treatment. Principal component analysis and support vector machines were applied to raw reflectance, first and second derivatives (SDR), and inverse logarithmic reflectance to differentiate freezing and the different stages of post-thawing from the normal leaf state. The impacts on biochemical retrieval using particular spectral domains were also assessed using a multivariate analysis. Results showed that SDR generated the highest classification accuracy (>95.6%) in the detection of post-thawed leaves. The optimal ratio vegetation index (RVI) generated the highest predictive accuracy for changes in leaf water content, with a cross validated coefficient of determination (R²cv) of 0.85 and a cross validated root mean square error (RMSEcv) of 2.4161mg/cm². Derivative spectral indices outperformed multivariate statistical methods for the estimation of changes in pigment contents. The highest accuracy was found between the optimal RVI and the change in carotenoids content (R²CV=0.70 and RMSECV=0.0015mg/cm²). The spectral domain 400–900nm outperformed the full spectrum in the estimation of individual pigment contents, and hence this domain can be used to reduce redundancy and increase computational efficiency in future operational scenarios. Our findings indicate that hyperspectral remote sensing has considerable potential for characterizing freezing injury in oilseed rape, and this could form a basis for developing satellite remote sensing products for crop monitoring.

Why it matches plant phenotyping methods凍害状態のハイパースペクトル検出と葉の水分・色素含量推定手法を開発・評価しており、植物表現型の取得・抽出が研究の中心である。

abstractThis study investigates the potential of hyperspectral techniques for detecting leaves at the stages of freezing and post-thawing injury, and for quantifying the impacts of freezing injury on leaf water and pigment contents.

Development of high-throughput methods to screen disease caused by Rhizoctonia solani AG 2-1 in oilseed rape. →

Background Rhizoctonia solani (Kühn) is a soil-borne, necrotrophic fungus causing damping off, root rot and stem canker in many cultivated plants worldwide. Oilseed rape (OSR, Brassica napus ) is the primary host for anastomosis group (AG) 2-1 of R. solani causing pre- and post-emergence damping-off resulting in death of seedlings and impaired crop establishment. Presently, there are no known resistant OSR genotypes and the main methods for disease control are fungicide seed treatments and cultural practices. The identification of sources of resistance for crop breeding is essential for sustainable management of the disease. However, a high-throughput, reliable screening method for resistance traits is required. The aim of this work was to develop a low cost, rapid screening method for disease phenotyping and identification of resistance traits. Results Four growth systems were developed and tested: (1) nutrient media plates, (2) compost trays, (3) light expanded clay aggregate (LECA) trays, and (4) a hydroponic pouch and wick system. Seedlings were inoculated with virulent AG 2-1 to cause damping-off disease and grown for a period of 4-10 days. Visual disease assessments were carried out or disease was estimated through image analysis using ImageJ. Conclusion Inoculation of LECA was the most suitable method for phenotyping disease caused by R. solani AG 2-1 as it enabled the detection of differences in disease severity among OSR genotypes within a short time period whilst allowing measurements to be conducted on whole plants. This system is expected to facilitate identification of resistant germplasm.

Why it matches plant phenotyping methodsOSRの病害抵抗性を評価するための高スループットな病害フェノタイピング法を開発・比較検証しており、画像解析を含む表現型取得が研究の中心である。

abstractThe aim of this work was to develop a low cost, rapid screening method for disease phenotyping and identification of resistance traits.

Mid-infrared spectroscopy combined with chemometrics to detect Sclerotinia stem rot on oilseed rape ( Brassica napus L.) leaves. →

Background Detection of plant diseases in a fast and simple way is crucial for timely disease control. Conventionally, plant diseases are accurately identified by DNA, RNA or serology based methods which are time consuming, complex and expensive. Mid-infrared spectroscopy is a promising technique that simplifies the detection procedure for the disease. Mid-infrared spectroscopy was used to identify the spectral differences between healthy and infected oilseed rape leaves. Two different sample sets from two experiments were used to explore and validate the feasibility of using mid-infrared spectroscopy in detecting Sclerotinia stem rot (SSR) on oilseed rape leaves. Results The average mid-infrared spectra showed differences between healthy and infected leaves, and the differences varied among different sample sets. Optimal wavenumbers for the 2 sample sets selected by the second derivative spectra were similar, indicating the efficacy of selecting optimal wavenumbers. Chemometric methods were further used to quantitatively detect the oilseed rape leaves infected by SSR, including the partial least squares-discriminant analysis, support vector machine and extreme learning machine. The discriminant models using the full spectra and the optimal wavenumbers of the 2 sample sets were effective for classification accuracies over 80%. The discriminant results for the 2 sample sets varied due to variations in the samples. Conclusion The use of two sample sets proved and validated the feasibility of using mid-infrared spectroscopy and chemometric methods for detecting SSR on oilseed rape leaves. The similarities among the selected optimal wavenumbers in different sample sets made it feasible to simplify the models and build practical models. Mid-infrared spectroscopy is a reliable and promising technique for SSR control. This study helps in developing practical application of using mid-infrared spectroscopy combined with chemometrics to detect plant disease.

Why it matches plant phenotyping methods油糧ナタネ葉の感染状態という植物病害表現型を、中赤外分光とケモメトリクスで検出する方法を開発・検証しており、方法論が研究の中心である。

abstractMid-infrared spectroscopy was used to identify the spectral differences between healthy and infected oilseed rape leaves.

Developing and testing an algorithm for site-specific N fertilization of winter oilseed rape →

Winter oilseed rape (WOSR) is a major crop in Germany, combining economic benefits with a high value in crop rotation, but it still lacks agronomically sound concepts for site-specific nitrogen (N) fertilization. Since ecological challenges resulting from high optimal N rates and a low N harvest index are approaching on WOSR cropping systems, optimizing N fertilization becomes crucial. Recent studies showed the importance of taking autumnal N uptake into account when estimating optimal N rates for WOSR, thus autumnal N is pivotal in the algorithm that is introduced in this study. The algorithm was parameterized by using data from site-specific N fertilization trials and optimized to reduce N fertilizer amounts. Afterwards it was tested on different commercial farms in northern Germany. The autumnal N uptake was estimated using hyperspectral reflection measurements gained from tractor-mounted devices, and the data was processed to N application maps used for the N application in spring. In addition, a uniform optimal fertilization and a uniform application of average N rates calculated by the algorithm were applied to provide control treatments. Yield, N balance and economic net-revenue were evaluated for each treatment. Yields from site-specific fertilization were slightly lower (0.06t/ha) than from uniform optimal treatment but 0.22t/ha higher than from the uniform application of the site-specific N amount (not significant in both cases). The N balance was significantly lower when fertilizing site-specifically instead of applying uniform optimal N rate, while the net-revenues were slightly higher.

Why it matches plant phenotyping methods冬季ナタネの秋季N吸収量という植物状態を、トラクター搭載ハイパースペクトル計測で推定し、施肥アルゴリズムへ組み込んで圃場で検証している。施肥最適化が目的だが、植物状態の取得・推定手法が技術的に中心的である。

abstractThe algorithm was parameterized by using data from site-specific N fertilization trials and optimized to reduce N fertilizer amounts. Afterwards it was tested on different commercial farms in northern Germany.

Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS) →

Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this study, we developed a calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds. We used a six-year daily Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series and 211 ground-observation records from four major crop species (winter wheat/barley, oilseed rape, and sugar beet) in central Germany. Results showed the superiority of the Savitzky–Golay algorithm in combination with BISE. The satellite-derived senescence dates matched ripeness stages of winter crops and the dates with maximum NDVI were closely related to the field-observed heading stage of winter cereals. We showed that the emergence of winter crops corresponded to the dates extracted with a threshold of 0.1, which translated into 8.89 days of root-mean-square error (RMSE) improvement compared to the standard threshold of 0.5. The method with optimised settings and thresholds can be easily transferred and applied to areas with similar growing conditions. Altogether, the results improve our understanding of how satellite-derived phenometrics can explain in situ phenological observations.

Why it matches plant phenotyping methods衛星NDVIから作物の生育フェノロジーを抽出する手順を開発・最適化し、地上観測で校正・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 calibration procedure to make phenometrics more comparable to ground-based phenological stages by optimising the settings of Best Index Slope Extraction (BISE) and smoothing algorithms together with thresholds.

Potential biases in screening for plant resistance to insect pests: an illustration with oilseed rape →

Breeding to increase crop resistance is a common strategy to decrease damage caused by insect pests, especially in the current context where insecticides are becoming at the same time less accepted by society and less efficient because of widespread pest resistance. The main bottleneck of this strategy is phenotyping. Although simple, high‐throughput methods have been proposed which could be highly useful, they may raise conceptual issues. Using field and laboratory experiments on oilseed rape (Brassica napus) and the pollen beetle (Brassicogethes aeneus syn. Meligethes aeneus), we illustrated possible difficulties with this approach: (i) field screenings might not represent the real attractiveness of the tested genotypes; (ii) plant phenology or spatial organization of the genotypes might bias field screening results; (iii) experiments based on detached plant parts (here, single flower buds or anthers) might not allow to infer the plant–insect relationship of the whole plant. We propose ways to better take these risks into account.

Why it matches plant phenotyping methods害虫抵抗性スクリーニングにおける表現型取得法の代表性・バイアスを実験的に検証し、改善策を提案しており、測定法の評価が中心です。

abstractThe main bottleneck of this strategy is phenotyping.

A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage. →

Background The fitness of the rape leaf is closely related to its biomass and photosynthesis. The study of leaf traits is significant for improving rape leaf production and optimizing crop management. Canopy structure and individual leaf traits are the major indicators of quality during the rape seedling stage. Differences in canopy structure reflect the influence of environmental factors such as water, sunlight and nutrient supply. The traits of individual rape leaves traits indicate the growth period of the rape as well as its canopy shape. Results We established a high-throughput stereo-imaging system for the reconstruction of the three-dimensional canopy structure of rape seedlings from which leaf area and plant height can be extracted. To evaluate the measurement accuracy of leaf area and plant height, 66 rape seedlings were randomly selected for automatic and destructive measurements. Compared with the manual measurements, the mean absolute percentage error of automatic leaf area and plant height measurements was 3.68 and 6.18%, respectively, and the squares of the correlation coefficients (R 2 ) were 0.984 and 0.845, respectively. Compared with the two-dimensional projective imaging method, the leaf area extracted using stereo-imaging was more accurate. In addition, a semi-automatic image analysis pipeline was developed to extract 19 individual leaf shape traits, including 11 scale-invariant traits, 3 inner cavity related traits, and 5 margin-related traits, from the images acquired by the stereo-imaging system. We used these quantified traits to classify rapes according to three different leaf shapes: mosaic-leaf, semi-mosaic-leaf, and round-leaf. Based on testing of 801 seedling rape samples, we found that the leave-one-out cross validation classification accuracy was 94.4, 95.6, and 94.8% for stepwise discriminant analysis, the support vector machine method and the random forest method, respectively. Conclusions In this study, a nondestructive and high-throughput stereo-imaging system was developed to quantify canopy three-dimensional structure and individual leaf shape traits with improved accuracy, with implications for rape phenotyping, functional genomics, and breeding.

Why it matches plant phenotyping methodsステレオ画像による3次元再構成と画像解析パイプラインを開発し、葉面積・草丈・葉形質を検証しており、植物フェノタイピング手法が研究の中心である。

abstractWe established a high-throughput stereo-imaging system for the reconstruction of the three-dimensional canopy structure of rape seedlings from which leaf area and plant height can be extracted.

High throughput phenotyping of root and shoot traits in Brassica to identify novel genetic loci for improved crop nutrition →

Despite the success of breeding for high-yielding varieties during and since the ‘Green Revolution’, there are still an ever increasing number of people who suffer from malnutrition, due to both inadequate calorie intake and ‘hidden hunger’ from insufficient essential nutrients. There are also adverse impacts of such high-input, intensive agriculture on the wider environment. It is necessary therefore to focus breeding efforts on improving nutrient uptake and composition of crops, as well as improved yield. Roots have been an under-utilised focus of crop breeding, because of difficulty in observation and accurate measurement. Furthermore, genetic diversity in crop roots may have been lost in commercial varieties because of the focus on above-ground traits and the use of fertilisers. Techniques which can accurately measure phenotypic variation in roots, of a diverse range of germplasm at a high throughput, would increase the potential for identifying novel genetic loci related to improved nutrient uptake and composition. The aim of this PhD was to screen at high throughput in a controlled-environment, the roots of an array of Brassica napus germplasm. The validity of the system to predict field performance, ... (continues)

Why it matches plant phenotyping methodsBrassicaの根・シュート形質を高スループットに取得するシステムの開発と、圃場性能予測に向けた妥当性評価が研究の中心である。

abstractTechniques which can accurately measure phenotypic variation in roots, of a diverse range of germplasm at a high throughput, would increase the potential for identifying novel genetic loci related to improved nutrient uptake and composition.

Discriminating power of selected physical properties of seeds of various rapeseed (Brassica napus L.) cultivars →

In this study, the seeds of open-pollinated winter rapeseed cultivars, hybrid winter rapeseed cultivars, open-pollinated spring rapeseed cultivars and hybrid spring rapeseed cultivars were investigated. The physical, optical, mechanical, geometric and image texture properties of rapeseeds were compared. Statistical models were developed based on the analyzed parameters to discriminate between seed groups. Most parameters effectively discriminated between cultivars of winter and spring rapeseed, including true density, porosity, L*, a*, b*, and spectral values at 400 nm, 470 nm, 500–530 nm, 560–620 nm, 640–650 nm and 690 nm. Four homogeneous groups were identified based on linear dimensions: F (surface area), S (width) and shape factors W6 (circularity ratio), Rb (Blair-Bliss coefficient) and W13 (roundness). No statistically significant differences in the mean values of hardness or area under the force-displacement graph were observed between seed groups. The model developed based on image texture variables from channel Y (luminance) was characterized by the highest discrimination accuracy of 82–87%. The experimental groups were classified with 89–92% accuracy in the model combining the best variables from each group of physical parameters. Total classification accuracy in neural networks reached 75% for a validation set comprising geometric properties and 91–92% for a validation set containing physical characteristics.

Why it matches plant phenotyping methods種子の物理・光学・幾何・画像テクスチャ特性を取得し、統計モデルやニューラルネットワークで品種群を識別する方法が研究の中心であり、植物器官の観測可能な形態・物性を用いた表現型解析に該当する。

abstractThe physical, optical, mechanical, geometric and image texture properties of rapeseeds were compared.

Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging. →

Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.

Why it matches plant phenotyping methodsアブラナの花弁における真菌感染状態を、ハイパースペクトル画像と化学計量学で直接推定する方法が研究の中心であり、感染植物の状態を測定するフェノタイピング手法に該当する。

abstractThis research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics.

Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola →

There is growing evidence that potassium deficiency in crop plants increases their susceptibility to herbivorous arthropods. The ability to remotely detect potassium deficiency in plants would be advantageous in targeting arthropod sampling and spatially optimizing potassium fertilizer to reduce yield loss due to the arthropod infestations. Four potassium fertilizer regimes were established in field plots of canola, with soil and plant nutrient concentrations tested on three occasions: 69 (seedling), 96 (stem elongation), and 113 (early flowering) days after sowing (DAS). On these dates, unmanned aerial vehicle (UAV) multi-spectral images of each plot were acquired at 15 and 120 m above ground achieving spatial (pixel) resolutions of 8.1 and 65 mm, respectively. At 69 and 96 DAS, field plants were transported to a laboratory with controlled lighting and imaged with a 240-band (390–890 nm) hyperspectral camera. At 113 DAS, all plots had become naturally infested with green peach aphids (Hemiptera: Aphididae), and intensive aphid counts were conducted. Potassium deficiency caused significant: (1) increase in concentrations of nitrogen in youngest mature leaves, (2) increase in green peach aphid density, (3) decrease in vegetation cover, (4) decrease in normalized difference vegetation indices (NDVI) and decrease in canola seed yield. UAV imagery with 65 mm spatial resolution showed higher classification accuracy (72–100 %) than airborne imagery with 8 mm resolution (69–94 %), and bench top hyperspectral imagery acquired from field plants in laboratory conditions (78–88 %). When non-leaf pixels were removed from the UAV data, classification accuracies increased for 8 mm and 65 mm resolution images acquired 96 and 113 DAS. The study supports findings that UAV-acquired imagery has potential to identify regions containing nutrient deficiency and likely increased arthropod performance.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と実験室ハイパースペクトル画像を用いて、カノーラのカリウム欠乏を分類・検出し、解像度や画素選別による精度を比較しているため、植物状態の取得手法が中心である。

abstractThe ability to remotely detect potassium deficiency in plants would be advantageous

Methods for estimating leaf nitrogen concentration of winter oilseed rape (Brassica napus L.) using in situ leaf spectroscopy →

Accurate and nondestructive assessment of leaf nitrogen (N) nutritional status is important for site-specific N management in winter oilseed rape production. To develop a method for determining leaf N concentration (LNC) in oilseed rape, a field experiment with different N fertilizer levels was conducted in two successive years by measuring leaf spectral reflectance (400⿿1300nm) and LNC at varying developmental stages. A partial least square (PLS) regression analysis was performed with four spectral methods: (i) the raw spectral reflectance (R), (ii) inverse-log reflectance data (log(1/R)), (iii) continuum removal (CR) method and (iv) first derivative reflectance (FDR). The results indicated that LNC and leaf reflectance significantly varied with the levels of N fertilization, and a good correlation was observed for all the spectral methods. Using a calibration dataset, the best results were obtained with the FDR-PLS method, which yielded the highest coefficient of determination (r2cal) of 0.963, the ratio prediction to deviation (RPD cal) of 5.207, and the lowest root mean square error (RMSE cal) of 0.294. Tests with the independent validation dataset also showed that the FDR-PLS method could well predict LNC in oilseed rape, with the values of r2val, RPD val, and RMSE val being 0.966, 5.488 and 0.276, respectively. The variable importance in projection (VIP) scores resulting from this PLS regression analysis were used to determine the effective wavelengths and reduce the dimensionality of the spectral reflectance data. The newly-developed FDR-PLS model using the effective wavelengths (432, 467, 519, 614, 772, 912 and 1072nm) performed well in LNC prediction with r2val=0.884, RPD val=2.971 and RMSE val=0.508. The overall results indicate that the LNC of winter oilseed rape could be reliably estimated with the in situ developed FDR-PLS method in this study.

Why it matches plant phenotyping methods葉の分光反射から植物形質である葉窒素濃度を推定する手法を開発し、独立データセットで検証しており、表現型取得・推定法が研究の中心である。

abstractTo develop a method for determining leaf N concentration (LNC) in oilseed rape, a field experiment with different N fertilizer levels was conducted in two successive years by measuring leaf spectral reflectance (400⿿1300nm) and LNC at varying developmental stages.

Non-destructive determination of Malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging. →

The feasibility of hyperspectral imaging with 400-1000 nm was investigated to detect malondialdehyde (MDA) content in oilseed rape leaves under herbicide stress. After comparing the performance of different preprocessing methods, linear and nonlinear calibration models, the optimal prediction performance was achieved by extreme learning machine (ELM) model with only 23 wavelengths selected by competitive adaptive reweighted sampling (CARS), and the result was R P = 0.929 and RMSEP = 2.951. Furthermore, MDA distribution map was successfully achieved by partial least squares (PLS) model with CARS. This study indicated that hyperspectral imaging technology provided a fast and nondestructive solution for MDA content detection in plant leaves.

Why it matches plant phenotyping methods油糧菜葉のMDA含量・分布を非破壊的に推定するハイパースペクトル画像法の検討が研究の中心であり、前処理・波長選択・校正モデル比較と分布マッピングを行っているため、植物フェノタイピング手法に該当する。

titleNon-destructive determination of Malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging.

Development of near-infrared spectroscopy calibrations to measure quality characteristics in intact Brassicaceae germplasm →

Determining seed quality parameters is an integral part of cultivar improvement and germplasm screening. However, quality tests are often time consuming, seed destructive, and can require large seed samples. This study describes the development of near-infrared spectroscopy (NIRS) calibrations to measure moisture, oil, fatty acid profile, nitrogen, glucosinolate, and chlorophyll content in six species from the Brassicaceae family. Rapeseed and similar oilseeds are potential feedstocks for producing hydrotreated renewable jet fuel. Screening samples with NIRS would allow cultivars with desirable characteristics to be quickly identified. A total of 367 samples of six species (Brassica napus, Brassica carinata, Brassica juncea, Brassica rapa, Sinapis alba, and Camelina sativa) were scanned with NIRS. Global calibrations for all six species were developed using modified partial least squares regression with reference values obtained through wet chemistry techniques. Comparing predicted values to reference data, the coefficients of determination (r2) and ratios of performance to deviation (RPD) varied, with some calibrations performing better than others. The calibration equations for seed oil content (r2=0.98, RPD=7.3) and nitrogen (r2=0.98, RPD=5.3) performed very well while the equations for seed moisture (r2=0.93, RPD=3.8) and total glucosinolate content (r2=0.92, RPD=2.3) were more qualitative. Large variation was observed for chlorophyll content (0–390mg/kg) so two calibration equations were developed, one for the higher and one for the lower range of values. When combined, these calibrations also showed very good performance (r2=0.99, RPD=14). The performance of the calibrations for the fatty acids was more varied, with some performing very well, such as the calibration for C18:3 (r2=0.99, RPD=9.9), and others, such as C22:0 (r2=0.69, RPD=1.9), showing poor correlation.

Why it matches plant phenotyping methodsBrassicaceae種子の品質形質をNIRSで非破壊推定する校正モデルを開発し、湿式化学分析との比較で性能検証しており、形質取得法が研究の中心である。

abstractThis study describes the development of near-infrared spectroscopy (NIRS) calibrations to measure moisture, oil, fatty acid profile, nitrogen, glucosinolate, and chlorophyll content in six species from the Brassicaceae family.

phenoSeeder - A Robot System for Automated Handling and Phenotyping of Individual Seeds. →

The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.

Why it matches plant phenotyping methods個々の種子の形態・体積・質量・密度を取得するロボット型フェノタイピング装置を開発し、複数作物で測定特性を検証しているため、方法が研究の中心です。

abstractHere, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes.

Virtual modeling based on deep phenotyping provides complementary data to field experiments to predict plant emergence in oilseed rape genotypes →

Breeding oilseed rape for oil and protein contents may have led to differences in seedling emergence in genotypes. New opportunities for deep automated phenotyping of germination and seedling growth are being developed on phenotyping platforms. Our aim was to demonstrate that using these data to parameterize a crop emergence model complements field experiments for the evaluation of differences among genotypes. Five genotypes, chosen in a diverse set of winter oilseed rape for their different germination speeds, were phenotyped for germination at different temperatures and water potentials as well as for radicle and hypocotyl growth. These data were used as parameters to run the SIMPLE crop emergence model over a period of 27 years (1985–2012), at two locations, one in France and one in Germany, and at four sowing dates. Field experiments were performed in 2012, 2013 and 2014, and the emergence of the five genotypes was measured at early and late sowing dates. First, model predictions were compared with observed field emergence in the French sowing trials in 2014. The model proved to be rather good at predicting the emergence of the genotypes. Then, for the simulation study, the model extended the observed differences between locations and sowing dates over a greater number of years. The model also identified the main reasons for non-emerging seedlings and their frequencies in the simulated sowings. Differences between the five genotypes were on average very small, but complex interactions appeared that led to bigger differences under certain sowing conditions. This study demonstrates that combining deep phenotyping with crop models in simulation studies paves the way for more precise and detailed evaluation of genotypes.

Why it matches plant phenotyping methods深層自動フェノタイピングによる発芽・幼植物成長の取得を中心に、そのデータを作物モデルへ組み込み、圃場発芽との比較検証まで行っているため、方法の実質的な適用研究に該当する。

abstractNew opportunities for deep automated phenotyping of germination and seedling growth are being developed on phenotyping platforms.

Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters →