This study assessed the integration of Unmanned Aerial Vehicles (UAVs) and satellite images (Sentinel-1 and Sentinel-2) in advanced machine-learning techniques to monitor the ABV of quinoa crops (Jacha Grano variety) across the Bolivian Altiplano. The proposed method follows a two-step procedure. First, UAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs at a 10 m spatial resolution from the crop canopy 3D model and classification, respectively. Secondly, several spectral and polarization/texture indices derived from Sentinel-2 and -1 images were integrated into three decision-tree-based machine-learning models (Random Forest-RF, Gradient Boosting-GB, eXtreme Gradient Boosting-XGB), and one CNN-based machine-learning model to estimate ABV. Additionally, a Stacking Model (STM) build on top of the three decision-tree-based models was considered for comparison. Model evaluation was also performed in a two-step approach. First, a 10-fold cross-validation strategy was used to highlight ABV sensitivity to Sentinel-2 and Sentinel-1 alone and in combination. Secondly, a Leave-One-Plot-Out Cross-Validation (LOPOCV) strategy was used to avoid autocorrelation between the training and evaluation dataset and therefore provided more insight into ABV mapping potential. The results showed that the combination of Sentinel-1 and Sentinel-2 features in the CNN model achieved the best predictive performance with R2 and RMSE values of 0.64 and 0.39 m3 ∙ 100 m−2, respectively. These findings highlight the potential of integrating multi-source information in advanced artificial intelligence algorithms for quinoa ABV monitoring, offering new insights toward the identification of sustainable practices across remote regions with complex socio-economic contexts.
Why it matches plant phenotyping methodsUAV画像・衛星センサー・機械学習を統合し、キノアの地上部バイオマス体積という植物形質を推定する手法を開発・比較検証しており、フェノタイピング手法が中心である。
abstractUAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs
Abstract Understanding the allometric relationships between leaf area and other plant traits is essential for non-destructive growth monitoring and efficient crop management. However, no comprehensive study has yet modeled leaf area in quinoa ( Chenopodium quinoa Willd.) using simple morphological traits across different sowing dates. This study aimed to quantify allometric relationships between leaf area and plant height, leaf dry weight, stem dry weight, panicle dry weight, and total dry matter, and to evaluate whether these relationships are modified by sowing date. A two-year field experiment was conducted with 12 sowing dates under a randomized complete block design with three replications. Leaf area index (LAI) dynamics were described using a logistic model, and allometric relationships were fitted using power-law equations. The results showed that LAI followed a logistic trend across all sowing dates, with maximum values ranging from 2.7 to 7.9. Plant height provided the most reliable prediction of leaf area (R² = 0.83, b = 1.2), followed by leaf dry weight (R² = 0.72, b = 0.97). The allometric coefficients for stem dry weight (b = 1.54, R² = 0.74) and panicle dry weight (b = 1.95, R² = 0.71) showed greater variability. A striking finding was the exceptionally high allometric coefficient (b = 4.95) recorded on May 6 of the second year, indicating a pronounced shift in resource allocation toward leaf area expansion. Total dry matter was a weak predictor (R² = 0.54), likely due to leaf fall during the growing season. The hypothesis that sowing date modifies allometric relationships was confirmed, as evidenced by considerable variation in allometric coefficients across sowing dates. This study provides, for the first time, a comprehensive set of allometric models for quinoa across multiple sowing dates. Plant height and leaf dry weight are recommended as simple, rapid, and non-destructive indicators for leaf area estimation, facilitating improved crop monitoring and management under diverse environmental conditions.
Why it matches plant phenotyping methods草丈や乾物重から葉面積を非破壊推定するアロメトリックモデルを中心に開発・評価しており、植物形質の取得手法が実質的な主題である。
abstractThis study aimed to quantify allometric relationships between leaf area and plant height, leaf dry weight, stem dry weight, panicle dry weight, and total dry matter, and to evaluate whether these relationships are modified by sowing date.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Within the context of climate change, quinoa ( Chenopodium quinoa Willd.) is a climate-resilient crop with high nutritional value. The effects of deficit irrigation on quinoa growth and physiological performance under arid conditions remain insufficiently understood. This study evaluated ten quinoa genotypes (two commercial varieties and eight accessions) under two irrigation regimes to identify traits and spectral indices associated with water-stress tolerance. We combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively. Deficit irrigation reduced plant height (18%), specific leaf area (8%), yield (43%), harvest index (26%), relative water content (7%), and dry matter accumulation (36%), while relative chlorophyll content (SPAD, Soil Plant Analysis Development) and stomatal density increased by 16% and 13%, respectively; accession ACC_23 exhibited the highest water-use efficiency (5.9 g kg -1 ). A univariate analysis of 35 vegetation indices across 13 dates showed that: Health Index(HIV), Normalized Green-Red Difference Index (NGRD), Red-Green Ratio (RG) and Plant Senescence Reflectance Index (PSRI), were the most sensitive, detecting significant differences between irrigation treatments in up to 32 of the 130 possible genotype-by-date comparisons. Integrating remote sensing into crop phenotyping represented a significant methodological improvement by enhancing phenotyping efficiency, improving detection of deficit irrigation effects, and facilitating identification of tolerant quinoa genotypes for arid production systems.
Why it matches plant phenotyping methodsリモートセンシングと多時点の植 phenotyping を統合し、35の植生指数の感度比較によって水ストレス関連形質を抽出する方法適用が、研究の主要な技術的要素として明示されています。
abstractWe combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively.
Above ground crop traits provide an early indication of a plant's capacity to tolerate stress, and are important for breeding programs aimed at improving stress tolerance. In this work, we present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time under control, drought, and saline conditions. We used daily sideview imaging of individual plants, followed by segmentation of the panicle, leaf and stem using the deep learning U-Net++ segmentation model. The resulting segmentations were used in regression models to estimate leaf area, fresh and dry biomass, and leaf dry weight. The regression models showed high predictive accuracy. Using these estimates, we could calculate specific leaf area and leaf weight ratio. In addition, radiation use efficiency for above-ground biomass production was calculated, providing an independent physiological check on the consistency of these predictions. Finally, using automated measurements of plant transpiration we were able to determine daily averages of whole plant stomatal conductance. The results show that image-derived morphological traits can be used to accurately estimate biomass-related traits and to derive physiologically meaningful indicators of plant performance over time. This method provides a framework for non-destructive monitoring of quinoa responses to drought and salinity.
Why it matches plant phenotyping methods画像取得、深層学習セグメンテーション、回帰による植物形質推定を中核とする高スループット表現型解析手法であり、ストレス実験での単なるルーチン測定ではない。
abstractwe present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time
Background High-throughput automated image analysis holds great promise for plant breeding by enabling faster, more accurate assessment of traits relevant to crop improvement. Imaging-based systems, such as the CropReporter, allow automated quantification of photosynthetic parameters like PSII efficiency under ambient light from a top-down 2D perspective. However, standard analysis tools average values across the 2D top view, overrepresenting upper leaves and underrepresenting those in the lower canopy. Upper leaves may occlude lower ones, and due to the pinhole projection of the camera, lower leaves of the same size appear smaller in the image. Consequently, vertical heterogeneity in PSII efficiency within the canopy cannot be resolved using a single 2D image. Results To address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin). Alignment accuracy between MaxiMarvin and CropReporter was high, with R² ≥ 0.98 for the x-axis and R² ≥ 0.99 for the y-axis. The method was tested using Chenopodium quinoa, Glycine max, and Solanum tuberosum, exposed to salinity, waterlogging and drought stress respectively. In Chenopodium quinoa, it allowed precise determination of when senescence began in the lower leaves. In Solanum tuberosum, the reduction in PSII efficiency by drought was the same for all leaf layers, while in Glycine max, waterlogging stress most strongly affected the middle layer of the canopy. Conclusions This framework enables the 3D mapping of PSII efficiency across the vertical plant profile by combining top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). It reveals vertical variation in photosynthetic activity across canopy layers. With standard 2D chlorophyll fluorescence imaging it is difficult to distinguish between non-photosynthetic tissues like flower heads and lower layers of leaves, that might have the same PSII values. Using height-based filtering, taking data from the 3D mapping, such distinction can be made with the method presented in this paper. This allows estimating the PSII efficiencies of leaves only. By capturing layer-specific responses to abiotic stress and developmental changes, the method provides physiologically relevant input for crop growth modelling and highlights the importance of accounting for canopy structure in photosynthetic analyses.
Why it matches plant phenotyping methods2Dクロロフィル蛍光によるPSII効率を3D植物構造へ投影し、群落層別の葉の生理形質を推定する手法の開発・検証が中心である。
abstractTo address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin).
Reproduction assets foundThe authors state that the analysis scripts (2D–3D alignment pipeline) and the phenotyping data used in the study are included with the publication as supplementary material, accessible via the article DOI. This is a paper-specific, publicly available asset containing the authors' analysis code and data.Dataset · publicThe scripts and the data that were used in the current study are available and added to this publication.Open asset ↗lines:143-180Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Quinoa ( Chenopodium quinoa Willd.) is a genetically diverse Andean crop valued for its nutrition and adaptability to varied agro-climatic conditions with potential for cultivation in European and Mediterranean, particularly on marginal lands. Low temperatures during early sowing can impair germination, while delayed sowing increases the risk of poor maturation due to unfavorable autumn weather. To assess the adaptation of quinoa to low temperature conditions, that reflect cold stress, we evaluated germination and phenotypic variation in 60 accessions from highland and coastal ecotypes across three sowing dates in South-Western Germany: late winter (S1), early spring (S2), and spring (S3). Early sowing under low temperature conditions in S1 delayed seedling-emergence and reduced emergence percentages, yet these plants produced the highest average seed yield per plot (64 g) compared to S2 (46 g) and S3 (35 g). Highland accessions showed earlier seedling-emergence and with higher emergence percentages, while coastal types matured earlier and gave higher yields across sowing dates. A complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth. This confirmed the beneficial germination performance of highland accessions under low temperature conditions, with strong agreement between manual and automated scoring. Our findings suggest that quinoa demonstrates resilience to cold stress with highland quinoa exhibiting superior germination traits, and early sowing, despite reduced emergence, can lead to higher yields. We conclude that combining favorable traits such as faster maturity and higher yield of coastal ecotypes with superior germination traits of highland accessions is a promising avenue for breeding improved quinoa varieties for cold climatic regions.
Why it matches plant phenotyping methodsMask R-CNNによる発芽・幼植物成長の画像解析を手動評価と比較し、強い一致を検証しており、植物表現型取得法の技術的検証を含む。
abstractA complementary laboratory experiment assessed germination under cold (4.4 °C) and control (18.3 °C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth.
Reproduction assets foundThe paper states that all phenotypic data and R analysis scripts are available as supplementary material (publicly hosted with the bioRxiv preprint), while raw seed germination images are only available upon request. No separate repository or trained model checkpoint is named.Dataset · publicData availability: All phenotypic data and R scripts used for the analysis are available
as supplementary material.Open asset ↗pdf-page:1 lines:1-52Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 15 Sept 2026
Accurate temporal reconstructions of plant growth are essential for plant phenotyping and breeding, yet remain challenging due to complex geometries, occlusions, and non-rigid deformations of plants. We present a novel framework for building temporal digital twins of plants by combining 3D Gaussian Splatting with a robust sample alignment pipeline. Our method begins by reconstructing Gaussian Splats from multi-view camera data, then leverages a two-stage registration approach: coarse alignment through feature-based matching and Fast Global Registration, followed by fine alignment with Iterative Closest Point. This pipeline yields a consistent 4D model of plant development in discrete time steps. We evaluate the approach on data from the Netherlands Plant Eco-phenotyping Center, demonstrating detailed temporal reconstructions of Sequoia and Quinoa species. Videos and Images can be seen at https://berkeleyautomation.github.io/GrowSplat/
Why it matches plant phenotyping methods植物の多視点画像からGaussian Splattingと位置合わせを用いて、成長の時間的な3D/4Dデジタルツインを構築する手法が研究の中心であり、植物フェノタイピングへの応用も明示されている。
abstractWe present a novel framework for building temporal digital twins of plants by combining 3D Gaussian Splatting with a robust sample alignment pipeline.
Quinoa (Chenopodium quinoa) is a nutritious and resilient crop that displays a high genetic and phenotypic variation. As the popularity of this crop increases, there is a growing need to integrate classic and modern breeding tools to favor its improvement. We tested root restriction as a method to reduce plant size and enable high-throughput phenotypic screening of large sets of quinoa plants under controlled conditions. We verified how increasing root restriction does not affect the prediction of field behavior with respect to other standard greenhouse cultivation procedures. We then combined the phenotypic information obtained with our root restriction system with whole-genome re-sequencing data to characterize a quinoa diversity panel of 100 accessions and showed that phenotypic data obtained from root-restricted plants provide real insights into quinoa genetics. Finally, we carried out a genome-wide association study (GWAS) and identified a previously described locus for betalain biosynthesis, as well as other candidate loci linked to betalain biosynthesis and seed size. Overall, we showed that a phenotyping system based on root restriction can aid the identification of genomic targets in quinoa, which can complement and inform field trials for certain traits. This work supports further breeding and faster improvement of quinoa.
Why it matches plant phenotyping methods根域制限を利用したハイスループット表現型スクリーニング系の構築・検証と、圃場挙動予測との比較が研究の中心であり、単なる生物学的測定ではない。
abstractWe tested root restriction as a method to reduce plant size and enable high-throughput phenotypic screening of large sets of quinoa plants under controlled conditions.
ABSTRACT Quinoa is a grain crop with excellent nutritional properties that has attracted global attention for its potential contribution to future food security in a changing climate. Despite its long history of cultivation, quinoa has been improved little by modern breeding and is a niche crop outside its native cultivation area. Grain yield is strongly affected by panicle traits, whose phenotypic analysis is time consuming and prone to error because of their complex architecture, and automated image analysis is an efficient alternative. We designed a panicle phenotyping pipeline implemented in Python via mask R‐convolutional neural networks for panicle segmentation and classification. After model training, we analysed 5151 images of quinoa panicles collected over three consecutive seasons from a breeding programme in the Peruvian highlands. The pipeline follows a stagewise approach, which first selects the optimal segmentation model and then another model that best classifies panicle shape. The best segmentation model achieved a mean average precision (mAP) score of 83.16 and successfully extracted the panicle length, width, area and RGB values. The classification model achieved 95% prediction accuracy for the amarantiform and glomerulate panicle types. A comparison with manual trait measurements using ImageJ revealed a high correlation for panicle traits (r > 0.94, p < 0.001). We used the pipeline with images from multilocation trials to estimate genetic variance components of an index on the basis of panicle length and width. We further updated the model for images that included metric scales taken in field trials to extract metric measurements of panicle traits. Our pipeline enables accurate and cost‐effective phenotyping of quinoa panicles. Using automated phenotyping based on deep learning, optimal panicle ideotypes can be selected in quinoa breeding and improve the competitiveness of this underutilized crop.
Why it matches plant phenotyping methodsキヌア穂の画像取得・セグメンテーション・形状分類・形質抽出を中核とする高スループット表現型解析パイプラインを開発し、手動測定との相関で検証しているため。
abstractWe designed a panicle phenotyping pipeline implemented in Python via mask R‐convolutional neural networks for panicle segmentation and classification.
Quinoa (Chenopodium quinoa) is a nutritious and resilient crop that displays a high genetic and phenotypic variation. As the popularity of this crop increases, there is a growing need to integrate classic and modern breeding tools to favor its improvement. We tested root restriction as a method to reduce plant size and enable high‐throughput phenotypic screening of large sets of quinoa plants under controlled conditions. We verified how increasing root restriction does not affect the prediction of field behavior with respect to other standard greenhouse cultivation procedures. We then combined the phenotypic information obtained with our root restriction system with whole‐genome re‐sequencing data to characterize a quinoa diversity panel of 100 accessions and showed that phenotypic data obtained from root‐restricted plants provide real insights into quinoa genetics. Finally, we carried out a genome‐wide association study (GWAS) and identified a previously described locus for betalain biosynthesis, as well as other candidate loci linked to betalain biosynthesis and seed size. Overall, we showed that a phenotyping system based on root restriction can aid the identification of genomic targets in quinoa, which can complement and inform field trials for certain traits. This work supports further breeding and faster improvement of quinoa.
Why it matches plant phenotyping methods根域制限を用いた高スループット表現型スクリーニング系の開発・検証が研究の中心であり、圃場での予測性能も検証しているため。
abstractWe tested root restriction as a method to reduce plant size and enable high‐throughput phenotypic screening of large sets of quinoa plants under controlled conditions.
Drought is a principal limiting factor in the production of agricultural crops; however, quinoa possesses certain adaptive and tolerance factors that make it a potentially valuable crop under drought-stress conditions. Within this context, the objective of the present study was to evaluate morphological and physiological changes in ten quinoa genotypes under three irrigation treatments: normal irrigation, drought-stress followed by recovery irrigation, and terminal drought stress. The experiments were conducted at the UNSA Experimental Farm in Majes, Arequipa, Peru. A series of morphological, physiological, and remote measurements were taken, including plant height, dry biomass, leaf area, stomatal density, relative water content, selection indices, chlorophyll content via SPAD, multispectral imaging, and reflectance measurements via spectroradiometry. The results indicated that there were numerous changes under the conditions of terminal drought stress; the yield variables of total dry biomass, leaf area, and plant height were reduced by 69.86%, 62.69%, and 27.16%, respectively; however, under drought stress with recovery irrigation, these changes were less pronounced with a reduction of 21.10%, 27.43%, and 17.87%, respectively, indicating that some genotypes are adapted or tolerant of both water-limiting conditions (Accession 50, Salcedo INIA and Accession 49). Remote sensing tools such as drones and spectroradiometry generated reliable, rapid, and precise data for monitoring stress and phenotyping quinoa and the optimum timing for collecting these data and predicting yield impacts was from 79–89 days after sowing (NDRE and CREDG r Pearson 0.85).
Why it matches plant phenotyping methods乾燥ストレス実験ではあるが、ドローン、マルチスペクトル画像、分光反射を用いたキヌアの表現型取得とストレス・収量影響のモニタリングが明示され、手法の適時性と予測精度も評価されているため。
abstractRemote sensing tools such as drones and spectroradiometry generated reliable, rapid, and precise data for monitoring stress and phenotyping quinoa
In the world, crop diseases are the main cause of reduction in production quality. These diseases affect quinoa crops and a large amount of economic losses occur each year. It is essential to identify these diseases at an early stage to increase production. A visual inspection is the most common method to identify diseases, these errors are common through visual inspection. Time is a key factor in disease detection and requires experience. This study shows how image recognition can be used for disease detection. This work consisted of collecting a data set of images for leaf spot 1,120 images, for bacterial spot 850 images, for downy mildew 896 images and 1,090 healthy images for a total of 3,956 images of quinoa leaves from the K'ayra agronomic center in the Leticia sector, San Jeronimo, Cusco, Peru, of which 70% were considered for training, 20% for validation and 10% for testing. The proposed model worked correctly with an accuracy of 89.498%, which will allow quinoa farmers to detect diseases early, hopefully leading to an increase in quinoa production worldwide.
Why it matches plant phenotyping methodsキヌア葉画像から病害状態を識別するCNNモデルの開発と精度評価が研究の中心であり、植物の病害表現型を直接推定している。
titleModelo de Redes Neuronales Convolucionales para detectar enfermedades en las hojas del cultivo de Quinua
PlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python that has been actively developed since 2014. A new version of PlantCV was recently released. Major goals of the version 4 release were to 1) simplify the process of developing workflows by reducing the amount of coding needed; 2) broadening the set of supported data types; and 3) introducing interactive annotation tools that can be used directly in PlantCV workflow notebooks. Here we highlight the use of point annotations that can be used to quickly collect sets of points for parameterization of functions such as regions of interest or the identification of landmark points. Another application of point annotations this for image annotation, which is a major bottleneck in plant phenomics. For example, we have used point annotations to analyze microscopy images aimed at measurement of quinoa salt bladders, the number and size of stomata, and scoring of pollen germination. These tasks have traditionally been low throughput and have required manual scoring, but our point annotation tools can be used along with traditional segmentation methods to semi-automatically detect and annotate images. The PlantCV point annotation tools also allow users to correct semi-automated detection results before classification (e.g., germinated vs non-germinated pollen) and extraction of size & color traits per object. Once images are annotated, results can be analyzed directly or potentially can be used as labeled data in supervised learning methods.
Why it matches plant phenotyping methodsPlantCVの画像解析ソフトウェアと対話的アノテーション機能を開発・紹介し、植物画像から器官数・サイズ・色などの形質を半自動抽出する方法が中心である。
abstractPlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python
Chenopodium quinoa is an important crop known for its salt tolerance. Salinity is an osmotic stress and ions accumulation in the root zone causes a reduction in soil water availability, affecting the uptake of essential nutrients, changing seed composition, and reducing biomass. Hence, the need for high-yield crops in poor soils. This research examines the effect of salt stress on quinoa photosynthetic efficiency and salt bladder development. Sensitive and tolerant quinoa lines were examined under salt stress conditions when a concentration of 155mM NaCl was applied. Soil conductivity was monitored for salt stress during the experiment. At approximately two months old, CropReporter images were taken and analyzed using PlantCV to estimate photosystem II efficiency, non-photochemical quenching (NPQ), chlorophyll content, and anthocyanin content. The analysis showed that the salt treatment did not negatively affect the plant photosynthetic efficiency (no changes in Fv/Fm, NPQ, Fq’/Fm’) but leaf area and chlorophyll content was statistically negatively affected by the treatment when comparing genotypes. Live tissue was also analyzed using reflection and fluorescence confocal microscopy, where epidermal salt bladders images were acquired, visualized and analyzed in 3-D and the salt tolerant line showed bigger bladder volumes compared with control conditions. A more high-throughput approach using PlantCV, an open-source image analysis software package targeted for plant phenotyping. This software helped count epidermal salt bladders using stereoscope images. A comprehensive understanding of the quinoa salt tolerant mechanisms by employing multidisciplinary approaches is necessary for their effective incorporation into salt-sensitive crops for better crop yields under stressful environments.
Why it matches plant phenotyping methodsPlantCVと画像・蛍光/共焦点 microscopy を用いて、光合成指標、葉面積、色素量、塩腺の体積・数を抽出するワークフローが、塩ストレス評価の主要な技術要素として記述されている。
abstractAt approximately two months old, CropReporter images were taken and analyzed using PlantCV to estimate photosystem II efficiency, non-photochemical quenching (NPQ), chlorophyll content, and anthocyanin content.
Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.
Why it matches plant phenotyping methods植物の栄養状態をハイパースペクトル画像から抽出するCNN手法を開発し、前処理・複数モデルとの比較・異なる生育段階での性能評価を行っており、表現型取得が中心である。
abstractThis study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 is the description of the selected growth stages based on the BBCH system for coding the phenological growth stages of plants ( Meier et al.Open asset ↗lines:339-346Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
PlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python that has been actively developed since 2014. A new version of PlantCV was recently released. Major goals of the version 4 release were to 1) simplify the process of developing workflows by reducing the amount of coding needed; 2) broadening the set of supported data types; and 3) introducing interactive annotation tools that can be used directly in PlantCV workflow notebooks. Here we highlight the use of point annotations that can be used to quickly collect sets of points for parameterization of functions such as regions of interest or the identification of landmark points. Another application of point annotations this for image annotation, which is a major bottleneck in plant phenomics. For example, we have used point annotations to analyze microscopy images aimed at measurement of quinoa salt bladders, the number and size of stomata, and scoring of pollen germination. These tasks have traditionally been low throughput and have required manual scoring, but our point annotation tools can be used along with traditional segmentation methods to semi-automatically detect and annotate images. The PlantCV point annotation tools also allow users to correct semi-automated detection results before classification (e.g., germinated vs non-germinated pollen) and extraction of size & color traits per object. Once images are annotated, results can be analyzed directly or potentially can be used as labeled data in supervised learning methods.
Why it matches plant phenotyping methodsPlantCVの画像解析ソフトウェアと点アノテーション機能の開発・適用を中心に扱い、植物器官の数・サイズ・色などの形質抽出を半自動化する方法論的研究である。
abstractPlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python
Chenopodium quinoa is an important crop known for its salt tolerance. Salinity is an osmotic stress and ions accumulation in the root zone causes a reduction in soil water availability, affecting the uptake of essential nutrients, changing seed composition, and reducing biomass. Hence, the need for high-yield crops in poor soils. This research examines the effect of salt stress on quinoa photosynthetic efficiency and salt bladder development. Sensitive and tolerant quinoa lines were examined under salt stress conditions when a concentration of 155mM NaCl was applied. Soil conductivity was monitored for salt stress during the experiment. At approximately two months old, CropReporter images were taken and analyzed using PlantCV to estimate photosystem II efficiency, non-photochemical quenching (NPQ), chlorophyll content, and anthocyanin content. The analysis showed that the salt treatment did not negatively affect the plant photosynthetic efficiency (no changes in Fv/Fm, NPQ, Fq’/Fm’) but leaf area and chlorophyll content was statistically negatively affected by the treatment when comparing genotypes. Live tissue was also analyzed using reflection and fluorescence confocal microscopy, where epidermal salt bladders images were acquired, visualized and analyzed in 3-D and the salt tolerant line showed bigger bladder volumes compared with control conditions. A more high-throughput approach using PlantCV, an open-source image analysis software package targeted for plant phenotyping. This software helped count epidermal salt bladders using stereoscope images. A comprehensive understanding of the quinoa salt tolerant mechanisms by employing multidisciplinary approaches is necessary for their effective incorporation into salt-sensitive crops for better crop yields under stressful environments.
Why it matches plant phenotyping methods塩ストレス機構の研究だが、CropReporter・PlantCV・顕微鏡画像を用いた植物形質の画像取得・抽出と、塩胞の高スループット計数が明示され、表現型解析ワークフローが実質的に含まれる。
abstractCropReporter images were taken and analyzed using PlantCV to estimate photosystem II efficiency, non-photochemical quenching (NPQ), chlorophyll content, and anthocyanin content.
Chenopodium quinoa is an important crop that is known for its salt tolerance [1, 2]. Salinity is an osmotic stress and the accumulation of ions in the root zone causes a reduction in soil water availability, which negatively affects the uptake of essential nutrients, changes seed composition, and reduces biomass. Therefore, there is a need to develop highly productive crops under poor soils. This research used imaging and other approaches to examine the effect of salt stress on quinoa photosynthetic efficiency, salt bladder changes and chloroplast development. Sensitive and tolerant quinoa lines were examined under salt stress conditions when a concentration of 155 mM NaCl was applied. Electrical soil conductivity was monitored during the experiment to assure salt stress and at approximately two months old, leaf tissue was sampled and chloroplast and grana were characterized using a super-resolution approach called lattice structured illumination microscopy. Chloroplast structure was negatively affected by environmental factors such as the availability of water and minerals [3]. Salt-sensitive lines showed swelling of the chloroplast stroma and starch accumulation (Figure 1). Live tissue was also analyzed using reflection and fluorescence confocal microscopy, where epidermal salt bladders [4] images were acquired, visualized and analyzed in 3-D and the salt tolerant line showed bigger bladder volumes compared with control conditions (Figure 2). A more high-throughput approach was used by using PlantCV [5], an open-source image analysis software package targeted for plant phenotyping. This software helped count epidermal salt bladders using stereoscope images. The salt tolerant line had more salt bladders on the leaf surface than the sensitive line, which could be one of the mechanisms why this line tolerated more stress cause by the salt application (Figure 2). A comprehensive understanding of the quinoa salt tolerant mechanisms by employing multidisciplinary approaches is necessary for their effective incorporation into salt-sensitive crops for better crop yields under stressful environments. Chloroplast structure measured using a super-resolution approach called lattice structured illumination microscopy. Two different genotypes 37TES (salt sensitive) and QQ74 (salt tolerant) under two different treatments salt (+) and no salt conditions (-). A) Epidermal salt bladder images using reflection and fluorescence confocal microscopy. Volume quantification of two different genotypes 37TES (salt sensitive) and QQ74 (salt tolerant) under two different treatments salt (+) and no salt conditions (-). B) Epidermal salt bladder number quantification using PlantCV.
Why it matches plant phenotyping methods塩ストレスの生物学的解析を目的とするが、複数の画像取得・解析法とPlantCVを用いた塩腺の3D体積・個数の定量が主要な構成であり、植物形態形質の画像ベース表現型解析として中心的である。
abstractThis research used imaging and other approaches to examine the effect of salt stress on quinoa photosynthetic efficiency, salt bladder changes and chloroplast development.
Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R² = 0.977–0.980, RMSE = 0.119–0.167) and SPAD-based chlorophyll (R² = 0.983–0.986, RMSE = 2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とランダムフォレストにより、キヌアのLAIおよびSPADクロロフィルという植物形質を推定・検証しており、形質取得ワークフローが研究の中心である。
abstracta UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll
Background Quinoa is an increasingly popular seed crop frequently studied for its tolerance to various abiotic stresses as well as its susceptibility to heat. Estimations of quinoa pollen viability through staining methods have resulted in conflicting results. A more effective alternative to stains is to estimate pollen viability through in vitro germination. Here we report a method for in vitro quinoa pollen germination that could be used to understand the impact of various stresses on quinoa fertility and therefore seed yield or to identify male-sterile lines for breeding. Results A semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community. Pollen collected on day 4 after first anthesis at zeitgeber time 5 was optimum for pollen germination with an average germination of 68% for accession QQ74 (PI 614886). The optimal length of pollen incubation was found to be 48 h, because it maximizes germination rates while minimizing contamination. The pollen germination medium's pH, boric acid, and sucrose concentrations were optimized. The highest germination rates were obtained with 16% sucrose, 0.03% boric acid, 0.007% calcium nitrate, and pH 5.5. This medium was tested on quinoa accessions QQ74, and cherry vanilla with 68%, and 64% germination efficiencies, respectively. Conclusions We provide an in vitro pollen germination method for quinoa with average germination rates of 64 and 68% on the two accessions tested. This method is a valuable tool to estimate pollen viability in quinoa, and to test how stress affects quinoa fertility. We also developed an image analysis tool to semi-automate the process of counting germinating pollen. Quinoa produces many new flowers during most of its panicle development period, leading to significant variation in pollen maturity and viability between different flowers of the same panicle. Therefore, collecting pollen at 4 days after first anthesis is very important to collect more uniformly developed pollen and to obtain high germination rates.
Why it matches plant phenotyping methodsキノア花粉の生存性を測定するin vitro発芽法を開発・最適化し、PlantCVによる発芽花粉の半自動画像計数も開発しており、植物形質取得法が中心である。
abstractA semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThis workflow is available at https://github.com/danforthcenter/quinoa-pollen-germination and a tutorial is availabe at https://github.com/danforthcenter/plantcv-tutorial-interactive-pollent-count .Open asset ↗GitHub · danforthcenter/quinoa-pollen-germinationlines:134-159Code · publicThis workflow is available at https://github.com/danforthcenter/quinoa-pollen-germination and a tutorial is availabe at https://github.com/danforthcenter/plantcv-tutorial-interactive-pollent-count .Open asset ↗GitHub · danforthcenter/plantcv-tutorial-interactive-pollent-countlines:134-159Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves. A total of 1,872 fresh and intact leaves from 13 crops were analyzed using a SPAD-502 meter and scanned using the Nix™ Pro color sensor. The color was assessed via RGB and CIELab systems. The full dataset was divided into calibration (70% of data) and validation (30% of data). For each crop and color pattern, multiple linear regression (MLR) analysis and multivariate modeling [least absolute shrinkage and selection operator (LASSO), and elastic net (ENET) regression] were employed and compared. The obtained MLR equations and multivariate models were then tested using the validation dataset based on r, R 2 , root mean squared error (RMSE), and mean absolute error (MAE). In both RGB and CIELab color systems, the Nix™ Pro color sensor was able to differentiate crops, and the SPAD indices were successfully predicted, mainly for mango, quinoa, peach, pear, and rice crops. Validation results indicated that ENET performed best in most crops (e.g., coffee, corn, mango, pear, rice, and soy) and very close to MLR in bean, grape, peach, and quinoa. The correlation between SPAD and greenness is crop-dependent. Overall, the Nix™ Pro color sensor was a fast, sensible and an easy way to obtain leaf color directly in the field, constituting a reliable alternative to digital camera imagery and associated image processing.
Why it matches plant phenotyping methods植物葉色を定量取得する携帯型センサーを開発・評価し、SPAD指数（葉の緑色状態）の推定モデルを校正・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 pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves.
Abstract Background: Quinoa is an increasingly popular seed crop frequently studied for its tolerance to various abiotic stresses as well as its susceptibility to heat. Estimations of quinoa pollen viability through staining methods have resulted in conflicting results. A more effective alternative to stains is to estimate pollen viability through in vitro germination. Here we report a method for in vitro quinoa pollen germination that could be used to understand the impact of various stresses on quinoa fertility and therefore seed yield or to identify male-sterile lines for breeding. Results: A semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community. Pollen collected on day 4 after first anthesis at ZT5 was optimum for pollen germination with an average germination of 68% for accession QQ74 (PI 614886). The optimal length of pollen incubation was found to be 48 hours, because it maximizes germination rates while minimizing contamination. The pollen germination medium’s pH, boric acid, and sucrose concentrations were optimized. The highest germination rates were obtained with 16% sucrose, 0.03% boric acid, 0.007% calcium nitrate, and pH 5.5. This medium was tested on quinoa accessions QQ74, and cherry vanilla with 68%, and 64% germination efficiencies, respectively. Conclusions: We provide an in vitro pollen germination method for quinoa with average germination rates of 64 and 68% on the two accessions tested. This method is a valuable tool to estimate pollen viability in quinoa, and to test how stress affects quinoa fertility. We also developed an image analysis tool to semi-automate the process of counting germinating pollen. Quinoa produces many new flowers during most of its panicle development period, leading to significant variation in pollen maturity and viability between different flowers of the same panicle. Therefore, collecting pollen at 4 days after first anthesis is very important to collect more uniformly developed pollen and to obtain high germination rates.
Why it matches plant phenotyping methodsキノア花粉の生存性を評価するin vitro発芽法を開発・最適化し、PlantCVによる発芽花粉の画像カウントも半自動化しており、植物形質取得法が研究の中心である。
abstractA semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community.
Reproduction assets foundThe paper's pollen germination microscopy images are deposited on Zenodo and the PlantCV analysis workflow/scripts plus extracted numerical data are on the authors' GitHub, both explicitly stated with URLs.Dataset · publicImages are available here:
https://doi.org/10.5281/zenodo.5909573.Open asset ↗Zenodo · 10.5281/zenodo.5909573pdf-page:9 lines:1-47Code · publicScripts and extracted numerical data are available on
GitHub: https://github.com/danforthcenter/quinoa-pollen-germination.Open asset ↗GitHub · danforthcenter/quinoa-pollen-germinationpdf-page:11 lines:1-46Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
Why it matches plant phenotyping methodsキノアの表現型計測 framework を中心に、計測カード、リモートセンシングによる高スループット計測、種子計測ツール、データベースを提示しており、植物フェノタイピング手法の方法論的研究である。
abstractHere, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement.
The importance of quinoa has been emphasized considerably in the recent decades, as a highly nutritional crop seed that is tolerant to salinity and amenable to arid agronomical conditions. The focus of this paper is to provide raw and a supplemental data of the research article entitled "Agronomic performance of irrigated quinoa in desert areas: comparing different approaches for early assessment of salinity stress" [1], aiming to compare different approaches for early detection, at the genotypic and crop levels, of the effect of salinity caused by irrigation on the agronomic performance of this crop. A set of 20 genotypes was grown under drip irrigation in sandy soil, amended with manure, at the International Center for Biosaline Agriculture (UAE) for two weeks, after which half of the trial was submitted to irrigation with saline water and this was continued until crop maturity. After eight weeks of applying the two irrigation regimes, pigment contents were evaluated in fully expanded leaves. The same leaves were then harvested, dried and the stable carbon and nitrogen isotope compositions (δ 13 C and δ 15 N) and the total nitrogen and carbon contents of the dry matter analyzed, together with ion concentrations. At maturity yield components were assessed and yield harvested. Data analysis demonstrated significant differences in genotypes response under each treatment, within all assessed parameters. The significant level was provided using the Tukey-b test on independent samples. The present dataset highlights the potential use of different approaches to crop phenotyping and monitoring decision making.
Why it matches plant phenotyping methods作物表現型に関する再利用可能なデータセットを提示し、遺伝子型・塩分処理下での生育、色素、同位体、元素、収量など複数の植物形質を比較することが中心である。
abstractThe focus of this paper is to provide raw and a supplemental data of the research article entitled "Agronomic performance of irrigated quinoa in desert areas: comparing different approaches for early assessment of salinity stress" [1], aiming to compare different approaches for early detection, at the genotypic and crop levels, of the effect of salinity caused by irrigation on the agronomic performance of this crop.
Core Ideas Water stress in multiple quinoa varieties were introduced. Remote sensing technologies were evaluated to assess water stress response. Handheld and unmanned aerial system based sensors provided reliable data. Thermal imaging data could capture stress in quinoa varieties. Quinoa ( Chenopodium quinoa Willd.) is a crop known for its tolerance to abiotic stress such as drought and salinity. Quinoa is also a versatile superfood, which is gluten‐free and high in protein, vitamins, nutrients, and beneficial antioxidants. Washington State University's quinoa breeding program efforts focus on identification and development of varieties that are resilient to local environmental conditions. In this study, high‐throughput phenotyping techniques were applied to evaluate the performance of quinoa varieties under different irrigation regimes. Handheld multispectral radiometer (Crop Scan), proximal sensing system using ground platform, and remote sensing with unmanned aerial system (UAS) were used to assess the performance of eight quinoa varieties under two irrigation treatments (non‐irrigated and irrigated). Crop Scan data, along with multispectral and thermal infrared images, were acquired at multiple time points at different stages of crop development during the season. In general, the normalized difference vegetation index, water band index, and green normalized difference vegetation index (GNDVI) data extracted from Crop Scan, and GNDVI and canopy temperature data extracted from UAS were able to detect irrigation treatment effects. Comparing the spectral data acquired at multiple scales indicated that Crop Scan data were highly and significantly correlated with remote sensing data (| r | = 0.57–0.85). Rapid data acquisition and the ability to detect differences among varieties under water stress highlight the application of remote sensing techniques as a high‐throughput phenotyping tool to evaluate quinoa.
Why it matches plant phenotyping methods複数のリモートセンシング手法を用いた植物ストレス形質の高スループット取得・相関評価が研究の中心であり、単なる灌漑試験の routine 測定ではない。
abstractRemote sensing technologies were evaluated to assess water stress response.