Abstract Ascochyta blight is a widely occurring chickpea fungal disease that can cause severe yield loss. Breeding for crop resistance benefits from high‐throughput evaluation of plant–pathogen interactions in genotypes which can serve as sources of resistance. Current practice for the evaluation is human visual scoring of disease symptoms, which is limited in throughput and precision. Here, we developed open‐source sensor‐based phenotyping methods using red, green, blue (RGB) and multispectral imaging to measure resistance components and predict disease severity classes in chickpea and wild relatives grown outdoors over three seasons. Pots were imaged at multiple time points with a ground‐based platform, providing 86,792 RGB and 8199 multispectral images. Lesion count was estimated with YOLOv5 (You Only Look Once version 5) object detection (F1 score = 0.27–0.30), fractional green canopy cover was estimated from RGB images, and vegetation indices were extracted from multispectral images. A model trained on growth rates of fractional green canopy cover normalized to control genotypes could predict disease severity classes with an accuracy of 65% –81 % ( 0.43–0.59) on unseen data from three different seasons. The developed methods provide a pathway to predict visual disease severity scores and support the breeding of crops for disease resistance. They may also be used to characterize disease progression, to find underlying resistance mechanisms, and for early disease detection.
Why it matches plant phenotyping methodsRGB・マルチスペクトル画像と地上センサープラットフォームを開発し、病斑数、緑色キャノピー被覆率、病害重症度を推定・予測する手法が研究の中心であるため。
abstractHere, we developed open‐source sensor‐based phenotyping methods using red, green, blue (RGB) and multispectral imaging to measure resistance components and predict disease severity classes in chickpea and wild relatives grown outdoors over three seasons.
With climate change and global population growth, accelerating the breeding of superior crop varieties is essential for food security. Genomic prediction, which uses genome-wide genetic markers to predict crop traits, plays an important role in intelligent crop breeding. However, existing methods often lack stable and accurate performance across crops and traits. Here, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm. Compared with the best-performing single base learners, GEG2P improves prediction accuracy by 4.02% on average across maize, wheat, rice, chickpea, and soybean. We use SHAP to quantify the contribution of SNPs to phenotype prediction and find that SNPs with large effects captured by different base learners are functionally complementary. This study provides a robust and accurate genomic prediction method for crop breeding.
Why it matches plant phenotyping methods作物形質の遺伝子型から表現型を予測するアンサンブル計算法を開発し、複数作物で精度比較・検証しており、表現型推定手法が研究の中心である。
abstractHere, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm.
Reproduction assets foundThe paper provides public author code (GitHub GEG2P repository and Docker Hub image), a Zenodo deposit of significant SNP interaction pairs generated in this study, and a Figshare link with the wheat genotypic and phenotypic data used in the analyses. These are paper-specific, publicly available, and actionable.Code · publicScripts used in this study are available at GitHub [ https://github.com/Deep-Breeding/GEG2P ] 89 .Open asset ↗GitHub · Deep-Breeding/GEG2Plines:236-266Dataset · publicThe genotypic and phenotypic data of wheat are available at Figshare [ https://figshare.com/s/287c2c7f1623008487a5 ] 68 .Open asset ↗Figsharelines:236-266Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Reliable field phenotyping for terminal heat stress (THS) tolerance in chickpea is constrained by conventional late-sowing approaches that confound reproductive stress with reduced vegetative growth. We developed and validated a deflowering (DF)-based field screening method that selectively imposes heat stress during the reproductive phase while maintaining normal vegetative vigour. Early flowers were removed to synchronize flowering and delay reproduction by 10-15 days, exposing flowering and pod set to high temperatures (>33 °C). Across two seasons and contrasting genotypes, DF maintained vegetative growth but significantly reduced pollen viability, pod set, and yield, with tolerant genotypes showing markedly lower yield penalties than susceptible ones. The method effectively discriminated reproductive thermotolerance and provides a simple, low-cost, and biologically grounded phenotyping tool for chickpea breeding under warming climates.•A DF-based field method selectively imposes reproductive-stage heat stress without compromising vegetative growth.•The approach reliably distinguishes heat-tolerant and susceptible chickpea genotypes under natural field conditions.
Why it matches plant phenotyping methods生殖期の耐暑性を選択的に評価するDFベースの圃場スクリーニング法を開発・検証しており、遺伝子型の識別に用いるフェノタイピング手法が中心である。
abstractWe developed and validated a deflowering (DF)-based field screening method that selectively imposes heat stress during the reproductive phase while maintaining normal vegetative vigour.
Abstract Early detection of soil-borne fungal diseases is essential for sustaining chickpea ( Cicer arietinum L.) productivity. This study evaluated hyperspectral canopy reflectance (350–2500 nm) for early detection of dry root rot (DRR; Macrophomina phaseolina ), Fusarium wilt ( Fusarium oxysporum f. sp. ciceri ), and their combined stress under controlled conditions using resistant and susceptible genotypes. Spectral data were collected at regular intervals from 1 to 76 days after sowing (DAS) and used to derive vegetation indices including NDVI, NDWI, PRI, and DSWI. Visual symptoms appeared at 46 DAS (DRR), 42 DAS (wilt), and 43 DAS (combined stress), whereas spectral indices indicated stress-related changes earlier, typically between 36 and 40 DAS. NDVI reflected early reductions in canopy vigor, PRI captured changes in photosynthetic activity, and NDWI and DSWI indicated alterations in plant water status, with DSWI showing comparatively consistent early sensitivity. Resistant genotypes maintained relatively stable NIR reflectance and water-sensitive spectral responses, while susceptible genotypes exhibited reduced NIR reflectance and increased SWIR absorption. Significant differences (p
Why it matches plant phenotyping methodsハイパースペクトル反射測定とスペクトル指標を用いて、植物体の病害ストレスを症状発現前に推定する方法を評価しており、表現型取得・抽出が研究の中心である。
abstractThis study evaluated hyperspectral canopy reflectance (350–2500 nm) for early detection of dry root rot (DRR; Macrophomina phaseolina ), Fusarium wilt ( Fusarium oxysporum f. sp. ciceri ), and their combined stress under controlled conditions using resistant and susceptible genotypes.
Abstract Fungal diseases such as Ascochyta pose major threats to chickpea production, causing significant losses if not detected early. Conventional diagnostic methods, including visual inspection and molecular assays, are often time-consuming, subjective, and ineffective for early detection of infection. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning for early, non-destructive detection of Ascochyta blight in chickpea leaves, an application that remains underexplored in previous research. Hyperspectral data in the 400–1000 nm range were acquired under controlled laboratory conditions from artificially infected chickpea plants. In this study, we developed a new comprehensive processing pipeline to address critical challenges associated with hyperspectral data, including noise, artifacts, and illumination variations. Subsequently, unsupervised learning approaches, such as K-means clustering, were employed to construct a clean, well-labeled database of mean leaf spectra. Using this refined dataset, we evaluated a classification framework based on supervised learning models, leveraging selected vegetation indices, visible and infrared spectral bands, along with features derived from statistical analyses. The proposed approach achieved an overall classification accuracy exceeding 95% in distinguishing healthy chickpea plants from those infected with Ascochyta blight. Results demonstrate that HSI can capture subtle physiological changes in leaves before visible symptoms appear, offering a reliable and scalable tool for precision agriculture. This study contributes a promising step toward AI-powered early disease detection in chickpea farming, enabling timely interventions, reducing fungicide use, and supporting sustainable crop protection strategies. Future work will focus on real-world deployment and cost-effective integration into existing monitoring systems.
Why it matches plant phenotyping methodsHSIと機械学習による感染葉の生理変化・病害状態の非破壊推定パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigates the use of hyperspectral imaging (HSI) combined with machine learning for early, non-destructive detection of Ascochyta blight in chickpea leaves
Abstract Early and accurate diagnosing of crops that contract diseases is critical in sustaining agricultural production and managing economic losses. Despite the massive success of the deep learning in the automated diagnosis of plant disease, new practices are largely only applicable to specific crops, and also need to be in controlled conditions and not in the field. In response to the aforementioned problems, a new Efficient Attention-based Hybrid Deep Learning (EA-HDL) has been suggested in this paper to perform the classification of multi-crop leaf diseases using real-field images. The architecture is based on an EfficientNetV2 backbone pretrained and has an attention-based pooling mechanism to encourage the use of discriminative features by the effective synthesis of information of the disease-relevant areas and the elimination of background noise. It is a tested, validated and benchmarked framework that was experimented on four of the most crucial crops: cotton, chickpea (chana), Black Gram and wheat in different field conditions. Strong and consistent results have been obtained in experiment work with a 100% record of classification accuracy in the cotton case, 98.64% in the chickpea case, 97.53% in the wheat case and competitive results in the Black Gram case in spite of difficult visual variability. It can be compared to the latest state-of-the-art deep learning models to prove that our approach is more accurate, as it generalizes and works with a variety of crops. The results are evidence that attention-based hybrid deep learning models have a tremendous potential of enhancing accuracy in disease classification in real-life agricultural 1 conditions. The EA-HDL is an effective and scalable platform to real-world crop disease surveillance and precision agriculture system.
Why it matches plant phenotyping methods葉画像から植物の病徴・病害を分類する深層学習手法を開発し、複数作物・圃場条件で検証・ベンチマークしており、植物表現型取得が中心である。
abstracta new Efficient Attention-based Hybrid Deep Learning (EA-HDL) has been suggested in this paper to perform the classification of multi-crop leaf diseases using real-field images.
Chickpea (Cicer arietinum L.), confronts substantial challenges from the emerging pathogenic fungus Macrophomina phaseolina (Tassi) Goid, causing dry root rot (DRR) disease. Chickpea plants severely affected by combined DRR and drought stress. Currently sick plot and sick pot method are utilized for germplasm screening to identify tolerant genotypes. These methods are time-consuming; therefore, we propose a novel methodology for the rapid screening of chickpea under combined DRR and osmotic stress conditions. This chapter introduces an adept high-throughput phenotyping methodology, conducted within controlled laboratory conditions, aiming to investigate the interaction between osmotic stress and DRR disease in chickpea crops. The methodology employs an innovative pouch technique for screening combined stress, providing a streamlined temporal investigation process and precise control over stress parameters. The incorporation of polyethylene glycol (PEG) enables the simultaneous imposition of osmotic stress alongside pathogen infection, making the methodology versatile for studying combined stress scenarios. This approach fills a gap in concurrent stress imposition techniques, enhancing germplasm screening by identifying genotypes with varying susceptibility and resistance levels. Thus, we suggest use of high-throughput phenotyping in combination genome-wide association study (GWAS) can take combined stress resistance breeding in chickpea at next level to combat food security and climate change.
Why it matches plant phenotyping methodsヒヨコマメの乾燥根腐病と浸透圧ストレスに対する耐性を迅速・高スループットに評価する新規ポーチ法を中心に開発しており、表現型スクリーニング手法が研究の中核である。
abstracttherefore, we propose a novel methodology for the rapid screening of chickpea under combined DRR and osmotic stress conditions.
Dry root rot (DRR) disease is a major threat to chickpea production across the world. This disease is caused by a soil-borne necrotrophic fungal pathogen, Macrophomina phaseolina. The use of disease-resistant varieties paves the way to conquer the disease spread. Though chickpea germplasm with rich genetic diversity is available around the world, its response to DRR is still unexplored. In turn, this demands screening and identification of resistant genotypes for crop protection against the disease. Here we describe an improved blotting paper technique for the large-scale screening of chickpea genotypes for DRR resistance. The method is quick, cost-effective, less labour-intensive, and thus optimized for high-throughput screening and can be efficiently used to screen a large number of chickpea genotypes for resistance against DRR.
Why it matches plant phenotyping methodsヒヨコマメの乾燥根腐病抵抗性を評価するための改良ブロッティングペーパー法を開発・最適化しており、植物病害状態の表現型取得が研究の中心です。
abstractHere we describe an improved blotting paper technique for the large-scale screening of chickpea genotypes for DRR resistance.
ChickpeaRootMorphology / geometry measurementRoot system architectureStress response / tolerance
Mechanical impedance in agricultural land is a significant constraint in modern agriculture. It dramatically affects seed germination, plant growth, development, and grain yield. Soil compaction hinders root growth and the ability to access deeper nutrients and water resources, impacting climate resilience, crop productivity, and global food security. Crops display variations in root system architecture (RSA) traits when grown in compacted soils. We can better understand the mechanisms behind soil compaction by examining root-related traits and their associated genes. Our recently published study investigated RSA traits across different soil compaction levels and identified significant genomic associations in chickpeas. We developed reliable methods for creating soils with varying bulk densities (i.e., compaction levels), growing chickpea seedlings, and harvesting the roots. We also conducted high-throughput phenotyping and screening of root-related traits using winRHIZO software. By integrating these phenotypic data with available genotypic data through Genome-Wide Association Studies (GWAS), we could identify genetic loci influencing root penetration in response to increasing soil compaction. These methods will help us identify key architectural traits of roots that can be targeted in crop breeding efforts to enhance resilience and productivity in compacted soils. By improving the root system and understanding the genes involved, we aim to develop plants more responsive to root penetration.
Why it matches plant phenotyping methods根系形態形質のハイスループット取得とwinRHIZOによる解析手法を開発・適用し、土壌圧密下の根系表現型をGWASに利用することが中心である。
abstractWe developed reliable methods for creating soils with varying bulk densities (i.e., compaction levels), growing chickpea seedlings, and harvesting the roots.
Fusarium wilt poses a significant threat to chickpea cultivation, causing substantial yield losses. Developing resistant chickpea varieties is a crucial strategy for managing this devastating disease. Screening a large number of germplasm and breeding lines against the pathogen is necessary to achieve this goal. In this context, the seedling root dip method has emerged as an effective technique to differentiate between resistant and susceptible chickpea genotypes. This method offers the advantages of screening a large number of lines within a short time frame and limited space. Another critical aspect of breeding for disease resistance is the rapid and accurate identification of the pathogen. Traditional pathogen detection methods are labor-intensive and time-consuming. This chapter presents a detailed protocol for the seedling root dip method, enabling the screening of chickpea genotypes against Fusarium oxysporum. Additionally, a rapid approach utilizing ITS primers for identifying the pathogen is discussed, providing a precise and expedient tool for disease resistance breeding efforts.
Why it matches plant phenotyping methods根浸漬法を用いてヒヨコマメ遺伝子型のFusarium萎凋病抵抗性を識別・スクリーニングする詳細プロトコルが主題であり、植物の病害状態を取得する表現型評価法として中心的です。ITSによる病原体同定は分子診断ですが、抵抗性表現型スクリーニング自体が主要な方法的貢献です。
abstractthe seedling root dip method has emerged as an effective technique to differentiate between resistant and susceptible chickpea genotypes
ChickpeaChlorophyll fluorescenceSeed / grainPhysiological trait estimationGrowth / development / phenology
Seed germination is a critical physiological process that transforms a quiescent seed into a metabolically active seedling and is also a crucial factor in determining maximum crop production. This transition is influenced by various intrinsic and extrinsic factors. Interestingly, reactive oxygen species (ROS) plays an important role in breaking seed dormancy by oxidation of biomolecules, weakening of the testa and degradation of endosperm. Similarly, molecular internal oxygen is also considered vital for the transition of dormancy to seed germination. However, it is essential to establish a correlation between the internal oxygen and the generation of ROS during seed germination. This chapter details protocols for imaging internal oxygen concentrations using VisiSens and fluorescent detection of ROS using H 2 DCFDA in chickpea seeds, complemented by qPCR analysis of key ROS-related genes (RBOH, AOX 1, UCP 1, and NADH dehydrogenase). These findings from these methods help advance our understanding of the inverse relationship between molecular oxygen and ROS dynamics during seed germination.
Why it matches plant phenotyping methods発芽研究を背景とするが、種子内部酸素濃度とROSを画像・蛍光で測定するプロトコル自体が章の中心であり、植物の生理状態を取得する方法として適格。
abstractThis chapter details protocols for imaging internal oxygen concentrations using VisiSens and fluorescent detection of ROS using H 2 DCFDA in chickpea seeds
Chickpea (Cicer arietinum L.) is a major grain legume, playing a crucial role in semi-arid systems. Its irrigation management is closely tied to its physiological condition, while environmental factors such as water availability and extreme heat significantly cause yield losses, especially in rainfed systems. The ability to estimate within field chickpea physiological traits may enhance understanding of its responses to environmental conditions and support agricultural management decisions. This study aimed to develop spatial estimation models of Leaf Area Index (LAI) and Leaf Water Potential (LWP), using Sentinel-2 imagery and meteorological data, mimicking practical application. Total of 404 and 361 measurements of LAI and LWP were collected from 14 and 17 fields, respectively (2022–2023). A leave-field-out validation strategy reflecting operational scenarios was employed. Partial least squares regression resulted in the highest accuracy for chickpea LAI, with coefficient of determination (R²) and root mean square error (RMSE) of 0.73 and 1.38 m² m⁻², respectively, while random forest excelled during early growth stages, prior to flowering. For LWP, Ridge Regression (RR) and support vector machine performed comparably overall (R²: 0.15; RMSE: 0.34 and 0.33 MPa); however, RR outperformed during the critical irrigation decision stage (post-flowering). Time-series commercial chickpea trait maps effectively captured LAI and LWP classical seasonal dynamics, demonstrating their relevance for farmers and their potential to elucidate critical relationships between canopy growth and irrigation onset in indeterminate crops such as chickpea. These LAI and LWP models establish foundational models that potentially enable precise and knowledge-based agricultural management tools for chickpea farmers.
Why it matches plant phenotyping methodsSentinel-2画像と気象データからLAIおよび葉水ポテンシャルを推定するモデルを開発し、leave-field-out検証で技術性能を評価しており、植物形質の取得・推定法が中心である。
abstractThis study aimed to develop spatial estimation models of Leaf Area Index (LAI) and Leaf Water Potential (LWP), using Sentinel-2 imagery and meteorological data, mimicking practical application.
Abstract This study uses Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy as a high‐throughput phenotyping tool to quantify total dietary fiber (TDF) in chickpea ( Cicer arietinum L.), dry pea ( Pisum sativum L.), and lentil ( Lens culinaris Medik.) for pulse crop breeding purposes. The standard analytical approach for TDF analysis is based on the Association of Official Analytical Collaboration method 985.29, which requires extensive sample preparation with extended analysis times of up to 30 h. The FT‐MIR approach was developed to enhance rapid and non‐destructive analysis and minimize the traditional workload associated with phenotyping TDF in pulse crops by accomplishing the same task in a shorter time and at minimal cost. Partial least squares regression (PLSR) was applied with chemometric modeling in MIR regions (650–1480 and 2771–3700 cm −1 ), encompassing spectral bands associated with undigested polysaccharides and partially or undigested protein and fatty acid methyl ester fractions that fingerprint TDF. K‐fold cross‐validation was used for PLSR modeling to enhance computational speeds with large‐scale data processing. These PLSR models for chickpea, dry pea, and lentil have coefficients of determination ( R 2 ) as 0.91, 0.96, and 0.94 with root mean square errors of prediction in the range of 0.05–0.5 g/100 g. This technique supports rapid phenotyping of TDF from raw flour in <1 min. The FT‐MIR technique can relieve the phenotyping bottleneck in pulse breeding and pulse‐based food and feed industries, targeting the measurements of TDF and ensuring a rapid and high‐throughput pipeline for plant breeding and cultivar development.
Why it matches plant phenotyping methodsFT-MIR分光法とPLSRモデルを開発・検証し、パルス作物のTDFという植物形質を高速・非破壊測定する手法が研究の中心である。
abstractThis study uses Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy as a high‐throughput phenotyping tool to quantify total dietary fiber (TDF) in chickpea ( Cicer arietinum L.), dry pea ( Pisum sativum L.), and lentil ( Lens culinaris Medik.) for pulse crop breeding purposes.
Drought stress induces a range of physiological changes in plants, including oxidative damage. Ascorbic acid (AsA), commonly known as vitamin C, is a vital non-enzymatic antioxidant capable of scavenging reactive oxygen species and modulating key physiological processes in crops under abiotic stresses like drought. Chickpea (Cicer arietinum L.), predominantly cultivated in drought-prone regions, offers an ideal model for studying drought tolerance. We explored the potential of AsA phenotyping to enhance drought tolerance in chickpea. Using an automated phenomics facility to monitor daily soil moisture levels, we developed a protocol to screen chickpea genotypes for endogenous AsA content. The results showed that AsA accumulation peaked at 30% field capacity (FC)-when measured between 11:30 am and 12:00 noon-coinciding with the maximum solar radiation (32 °C). Using this protocol, we screened 104 diverse chickpea genotypes and two control varieties for genetic variability in AsA accumulation under soil moisture depletion, identifying two groups of genotypes with differing AsA levels. Field trials over two consecutive years revealed that genotypes with higher AsA content, such as BDNG-2018-15 and PG-1201-20, exhibited enhanced drought tolerance and minimal reductions in yield compared to standard cultivars. These AsA-rich genotypes hold promise as valuable genetic resources for breeding programs aimed at improving drought tolerance in chickpea.
Why it matches plant phenotyping methodsチックピーの内生AsA含量を測定・スクリーニングするプロトコルを開発し、自動フェノミクス施設で多数遺伝子型に適用しているため、植物フェノタイピング手法が中心的です。
abstractWe explored the potential of AsA phenotyping to enhance drought tolerance in chickpea.
Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysical parameters of chickpea and developing machine learning (ML) models for early yield prediction. Field experiments were carried out over three years at the Indian Agricultural Research Institute research farm in New Delhi. Thermal and visible images were collected alongside the measurement of crop biophysical parameters, including leaf area index (LAI), photosynthesis, transpiration rate, stomatal conductance, relative leaf water content (RWC), membrane stability index (MSI), and NDVI, for 85 chickpea genotypes with varying levels of wilt resistance. ML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters. The results showed that the canopy temperatures were directly correlated with increasing levels of wilt severity. Crop photosynthesis, stomatal conductance, transpiration, LAI, RWC, MSI, and NDVI dropped significantly with increasing levels of wilt severity. Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. The accuracy of the models increases as we move closer to the harvest. Ranking of the model's performances indicated that XGB is the best model to predict chickpea yield under wilt conditions. NDVI was identified as most important variable for yield prediction. The findings of the study quantified the impacts of wilt on important crop biophysical parameters and highlighted the suitability of ML models in early yield prediction under different levels of disease severity.
Why it matches plant phenotyping methods可視・熱画像と生物物理形質を統合した機械学習による、萎凋病条件下の遺伝子型別早期収量予測を開発・評価しており、形質推定手法が中心である。
abstractML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters.
Identification of Hard to Cook (HTC) chickpeas in a rapid, non-destructive manner is crucial for the pulse processing industry. This study investigated the potential of near infrared (NIR) hyperspectral imaging (HSI) system to classify chickpeas into HTC and Easy to Cook (ETC) (control) categories. Two types of HTC chickpeas were created using eight different varieties of chickpeas: the first type was created by storing under suboptimal conditions, while the second type was created with chemical treatment. A total of eight hundred sixty-four chickpea seeds ({control- 36; physically hardened-36 seeds; chemically hardened-36 seeds} × 8 varieties) were used in this study. The chickpeas were imaged using a NIR-HSI system in the spectral range of 900–2500 nm. The cooking time of individual chickpea seed was measured using an automated Mattson cooker and the spectral data was correlated with the measured reference cooking time of chickpeas to develop the calibration model. Partial Least Square Discriminant Analysis (PLSDA), Support Vector Classifier (SVC) and Convolutional Neural Network-Attention (CNN-ATT) models was used for model development based on full spectrum and significant wavelengths. The optimal models were obtained using the SVC and CNN-ATT which demonstrated 100% accuracy in classifying the chickpeas into HTC and ETC. Besides, the cooking time of control (ETC) and HTC chickpeas were predicted using One Dimensional Convolutional Neural Network (1D-CNN) with Correlation Coefficient of Prediction (R²ₚ) and Root Mean Square Error of Prediction (RMSEP) values of 0.880 and 0.662 respectively indicating the potential of this approach in developing robust model for cooking time prediction in other pulses.
Why it matches plant phenotyping methodsNIRハイパースペクトル画像からヒヨコマメ種子の硬化状態と調理時間を推定するモデルを開発・評価しており、種子形質の取得手法が中心である。
abstractThis study investigated the potential of near infrared (NIR) hyperspectral imaging (HSI) system to classify chickpeas into HTC and Easy to Cook (ETC) (control) categories.
Published1 Aug 2024South African journal of botany : official journal of the South African Association of Botanists = Suid-Afrikaanse tydskrif vir plantkunde : amptelike tydskrif van die Suid-Afrikaanse Genootskap van Plantkundiges
An efficient, low-cost, lightweight, and portable paper microscope can be used to monitor real-time in vivo pollination, which is not feasible with a conventional compound microscope. The advantages of paper microscopes (foldscopes) remain unexplored for in vivo field-oriented assessment of pollination. In this study, experiments were carried out to calibrate and validate the capability of foldscopes to investigate in vitro pollen traits and to understand the feasibility of using in real-time in vivo field-level investigations. Comparison of optical images of pollen morphological traits (color, size, and shape) of diverse plant species (including crops) captured using a foldscope and a compound microscope revealed little to no differences. In addition, foldscopes were calibrated to monitor and estimate in vitro pollen viability in wheat, sorghum, sunflower, chickpea, soybean, and periwinkle and to determine in vitro pollen germination in chickpea, maize, and periwinkle. Pollen tube growth was observed by time-lapse imaging of pollen from periwinkle. The foldscope efficiently captured genotypic variation in in vitro pollen germination of twelve chickpea genotypes under drought revealing the possibility of foldscopes as a tool for field level, real-time in vivo monitoring of pollination under drought.
Why it matches plant phenotyping methods折り紙顕微鏡を用いた花粉形態・生存性・発芽・花粉管成長の画像計測について、校正と従来顕微鏡との比較検証を行っており、植物形質取得法が研究の中心である。
abstractIn this study, experiments were carried out to calibrate and validate the capability of foldscopes to investigate in vitro pollen traits
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract The standard method of estimating in vitro protein digestibility, the protein digestibility corrected amino acid score (PDCAAS) assay, does not support the expected workflow of a pulse breeding program. This is mainly due to its low‐throughput design and long processing time (∼16–24 h) per sample. Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy has been developed as a high‐throughput phenotyping tool to estimate protein digestibility in pulses. The mid‐infrared region representing the amide I band (1756.81–1586.27 cm −1 ) was utilized to perform chemometric modeling with partial least squares regression (PLSR) to estimate in vitro protein digestibility in dry pea ( Pisum sativum L.), lentil ( Lens culinaris Medik.), and chickpea ( Cicer arietinum L.) flours. The root mean square error of predictions of the developed PLSR models for dry pea, lentil, and chickpea were 0.00039, 0.00024, and 0.00017, respectively. Phenotyping with the FT‐MIR approach is more rapid than with the PDCAAS assay and estimates protein digestibility from the flour of a single seed in a shorter time (∼1–2 min). The FT‐MIR approach is robust as the spectroscopic data are consistent and chemically fingerprint this nutritional trait. Accordingly, FT‐MIR can resolve the phenotypic bottleneck of pulse breeding related to in vitro protein digestibility measurements by enabling a high‐throughput phenotyping workflow.
Why it matches plant phenotyping methodsFT-MIR分光とPLSRモデルを用いて、豆類のタンパク質消化性という植物形質を高速推定する方法を開発・評価しており、表現型取得手法が研究の中心である。
abstractFourier‐transform mid‐infrared (FT‐MIR) spectroscopy has been developed as a high‐throughput phenotyping tool to estimate protein digestibility in pulses.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Abstract Non-photochemical quenching (NPQ) is a protective mechanism for dissipating excess energy generated during photosynthesis in the form of heat. The accelerated relaxation of the NPQ in fluctuating light can lead to an increase in the yield and dry matter productivity of crops. Since the measurement of NPQ is time-consuming and requires specific light conditions, theoretical NPQ (NPQ(T)) was introduced for rapid estimation, which could be suitable for High-throughput Phenotyping. We investigated the potential of NPQ(T) to be used for testing plant genetic resources of chickpea under drought stress with non-invasive High-throughput Phenotyping complemented with yield traits. Besides a high correlation between the hundred-seed-weight and the Estimated Biovolume, significant differences were observed between the two types of chickpea desi and kabuli for Estimated Biovolume and NPQ(T). Desi was able to maintain the Estimated Biovolume significantly better under drought stress. One reason could be the effective dissipation of excess excitation energy in photosystem II, which can be efficiently measured as NPQ(T). Screening of plant genetic resources for photosynthetic performance could take pre-breeding to a higher level and can be implemented in a variety of studies, such as here with drought stress or under fluctuating light in a High-throughput Phenotyping manner using NPQ(T).
Why it matches plant phenotyping methodsNPQ(T)による光合成生理形質の迅速推定を高スループット表現型解析へ実装し、その適用可能性を評価しており、表現型取得法が研究の中心です。
abstracttheoretical NPQ (NPQ(T)) was introduced for rapid estimation, which could be suitable for High-throughput Phenotyping.
Abstract Drought stress triggers a cascade of physiological changes in plants, including oxidative damage. Ascorbic acid (AsA), commonly known as vitamin C, is a vital non-enzymatic antioxidant with the potential to scavenge reactive oxygen species and modulate key processes in crop plants under abiotic stresses like drought. Chickpea, is predominantly cultivated in drought-prone regions. We demonstrate the utility of phenotyping for AsA content to enhance drought tolerance in chickpea. Using automated phenomics facility that can monitor daily soil moisture levels, we optimized a protocol for screening endogenous AsA levels in chickpea genotypes. Findings revealed that AsA accumulation peaked at 30% field capacity (FC), when measured between 11:30 am and 12:00 noon, coinciding with the maximum solar radiation during a 24 h cycle. Leveraging this protocol, screened 106 diverse chickpea genotypes for genetic variability in AsA accumulation under soil moisture depletions, identifying two sets of genotypes exhibiting differential AsA levels. Subsequent field evaluations over two consecutive years demonstrated that genotypes with elevated AsA levels like BDNG-2018-15 and PG-1201-20 displayed enhanced drought tolerance with minimum reductions in yield attributes compared to popular cultivars. These AsA-rich genotypes hold promise as valuable genetic resources for breeding programs aimed at improving drought tolerance in chickpea cultivation.
Why it matches plant phenotyping methods自動フェノミクス施設を用いた内生AsA含量のスクリーニングプロトコルを最適化し、遺伝子型評価へ適用しており、表現型取得法が研究の中心である。
abstractWe demonstrate the utility of phenotyping for AsA content to enhance drought tolerance in chickpea.
Chickpea wilt is a widespread agricultural disease that affects production worldwide every year. Rapid and accurate detection of the disease is desirable, but is difficult using traditional methods. Therefore, it is necessary to detect the disease using automatic, rapid, reliable, and simple methods before it completely damages the plant. Herein, we investigate the applicability of machine learning-based texture analysis methods to determine the severity level of Fusarium wilt in chickpea. Various procedures, such as image annotation, augmentation, resizing, and color conversion using different color spaces (RGB, HSV, and Lab*), were performed to develop the model. To perform texture feature extraction, the Gray-Level Run-Length Matrix (GLRLM) and the Gray-Level Occurrence Matrix (GLCM) feature extraction methods were used. To avoid local minima, Bayesian optimization was applied, while to train and test the effectiveness of the proposed model, 15000 images (70–20-10 ratio for training, validation and testing) were used. Finally, multi-class classification models were developed using image classification methods such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Networks. The proposed GLRLM-HSV based KNN model performed well in determining the severity level of fusarium wilt of chickpea among five different severity levels, with an accuracy of 94.5%.
Why it matches plant phenotyping methods画像特徴量と機械学習により、ヒヨコマメのFusarium萎凋病の重症度を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す。
abstractwe investigate the applicability of machine learning-based texture analysis methods to determine the severity level of Fusarium wilt in chickpea.
Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the "Palouse" region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20-78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.
Why it matches plant phenotyping methods育種試験の農業形質とUAVマルチスペクトル画像を含む再利用可能な植物フェノタイピングデータセットであり、画像から植生指数などの形質を抽出できる点が中心です。
abstractThis dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials.
Most chickpea cultivation occurs in rainfed environments, where unpredictable rainfall leads to drought stress, consequently reducing growth and productivity. Fast and robust image-based screening methods would greatly facilitate drought tolerance research. In this study, an experiment was conducted in a climate-controlled environment, using radio frequency-enabled ID (RFID) tagged plant carriers on Lemnatec's high-throughput phenotyping platform. The agro-physiological characteristics of six chickpea genotypes under drought stress conditions imposed at the early podding stage were explored. Using non-destructive techniques, including Red, Green, and, Blue (RGB), Near-Infrared (NIR), Infrared (IR), and chlorophyll fluorescence (Fv/Fm) imaging, data was captured at various stages of drought stress, quantified as the fraction of transpirable soil water, using LemnaGrid software. Traits such as plant phenology, yield, yield components, and physiological parameters (e.g., leaf temperature and photosynthetic characteristics) for both drought-stressed and well-watered plants were recorded manually. Seed yields ranged from 9.9–18.1 g plant−1 under WW, and 2.6–13.7 g plant−1 under DS. DS decreased yield the most in ICC 1882 and RSG 888 (73.7 %) and the least in ICC 4958 (24.3 %) relative to WW. Our findings revealed significant genotype × water treatment interactions for all manually recorded traits. Moreover, strong positive correlations were observed between manually recorded and image-based traits, i.e., between aboveground dry weight and projected area, aboveground dry weight and convex hull area, plant height and caliper length, photosynthetic rate, and chlorophyll fluorescence, stomatal conductance and NIR reflectance, IR thermometer temperature and IR imaging temperature. Notably, the strong positive correlations between NIR reflectance and stomatal conductance, and between chlorophyll fluorescence and photosynthesis, underscore the immense potential of harnessing image-based screening methods in breeding programs to enhance drought tolerance.
Why it matches plant phenotyping methods画像・センサーを用いたハイスループット表現型取得と、手動測定との相関による技術評価が研究の中心であり、干ばつ応答の植物形質を抽出している。
abstractFast and robust image-based screening methods would greatly facilitate drought tolerance research.
Published1 Jan 2024Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Evaluating the protein content of a single chickpea seed in a rapid, non-destructive, and precise manner is crucial for facilitating the breeding of high-protein chickpeas. This study explored the potential of near-infrared (NIR) hyperspectral imaging (HSI) to predict the protein content in a single chickpea seed. Eight varieties of chickpeas with different protein contents were subjected to NIR reflectance hyperspectral imaging in the spectral range of 900–2500 nm at two different positions of chickpea seed (micropyle down and micropyle up). The spectral data was correlated with the measured reference protein content of chickpea seed for building the partial least square regression (PLSR) and support vector machine regression (SVMR) models based on different spectral preprocessing techniques, with full spectrum and effective wavelengths selected using competitive adaptive reweighted sampling (CARS) and iteratively retaining informative variables (IRIV) algorithms. When using the full spectrum, the optimal protein prediction model was obtained using PLSR, which yielded correlation coefficient of prediction (R²ₚ) and root mean square error of prediction (RMSEP) values of 0.935 and 0.987, respectively, with external parameter orthogonalization (EPO)+standard normal variate (SNV) preprocessing for micropyle down position of chickpea seed. The IRIV selected wavelength with PLSR yielded the best model with R²ₚ and RMSEP of 0.947 and 0.861, respectively, at the micropyle down position of chickpea seed. Hence, the optimal prediction models were obtained using PLSR with EPO+SNV at the micropyle down position of chickpea seed.
Why it matches plant phenotyping methods単一ヒヨコマメ種子のタンパク質含量という植物器官形質を、NIRハイパースペクトル画像と回帰モデルで非破壊推定し、複数モデルと前処理を比較・検証しているため、フェノタイピング手法が中心です。
titleApplication of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed
ChickpeaField / plotRootMorphology / geometry measurementRoot system architecture
Through the use of computational systems, it is possible to employ a wide range of statistical techniques, available as open-source code, to perform various assessments in plants. This study aims to demonstrate the application of image analysis in the context of evaluating root nodules in chickpea plants, aiming to standardize a methodology. The research was conducted in the field, where roots were collected, cleaned, and photographed in a studio using a camera with ISO320, SPEED 1/1500 F1.5 M0.6, WB490K. Image analyses were carried out using R software. Parameters related to roots and nodules were obtained, including root area (cm2), nodule area (cm²), the percentage of nodules in relation to roots, and the number of nodules. Comparing the method with conventional approaches showed efficiency, highlighting the effectiveness of this tool for the intended purpose. It is concluded that the use of the developed methodology can be successfully applied to the analysis of nodules and root systems, providing the evaluation of various parameters with precision, reducing labor costs, and saving time.
Why it matches plant phenotyping methodsヒヨコマメの根・根粒形質を画像解析で取得する方法を開発し、従来法と比較して検証しているため、フェノタイピング手法が中心である。
abstractThis study aims to demonstrate the application of image analysis in the context of evaluating root nodules in chickpea plants, aiming to standardize a methodology.
Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the "Palouse" region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20-78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と抽出可能なデジタル形質を含む、育種利用可能な植物表現型データセットとして構築・公開されているため。
abstractThis dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials.
Reproduction assets foundThis Data in Brief article describes its own pulse crop phenotyping dataset (agronomic trait tables and 275 UAV multispectral orthomosaic images), publicly deposited on Zenodo with an explicit DOI listed in the Specification Table under Data accessibility. This is a paper-specific, public, directly actionable dataset.Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.8280431 .Open asset ↗Zenodo · 10.5281/zenodo.8280431lines:1-49Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Phytophthora root rot (PRR) is a major constraint to chickpea production in Australia. Management options for controlling the disease are limited to crop rotation and avoiding high risk paddocks for planting. Current Australian cultivars have partial PRR resistance, and new sources of resistance are needed to breed cultivars with improved resistance. Field- and glasshouse-based PRR resistance phenotyping methods are labour intensive, time consuming, and provide seasonally variable results; hence, these methods limit breeding programs’ abilities to screen large numbers of genotypes. In this study, we developed a new space saving (400 plants/m2), rapid (<12 days), and simplified hydroponics-based PRR phenotyping method, which eliminated seedling transplant requirements following germination and preparation of zoospore inoculum. The method also provided post-phenotyping propagation all the way through to seed production for selected high-resistance lines. A test of 11 diverse chickpea genotypes provided both qualitative (PRR symptoms) and quantitative (amount of pathogen DNA in roots) results demonstrating that the method successfully differentiated between genotypes with differing PRR resistance. Furthermore, PRR resistance hydroponic assessment results for 180 recombinant inbred lines (RILs) were correlated strongly with the field-based phenotyping, indicating the field phenotype relevance of this method. Finally, post-phenotyping high-resistance genotypes were selected. These were successfully transplanted and propagated all the way through to seed production; this demonstrated the utility of the rapid hydroponics method (RHM) for selection of individuals from segregating populations. The RHM will facilitate the rapid identification and propagation of new PRR resistance sources, especially in large breeding populations at early evaluation stages.
Why it matches plant phenotyping methods植物の根腐病抵抗性を評価する高速・高スループット水耕フェノタイピング法を開発し、遺伝子型間識別と圃場評価との相関で検証しているため、方法が研究の中心です。
abstractwe developed a new space saving (400 plants/m2), rapid (<12 days), and simplified hydroponics-based PRR phenotyping method
Reproduction assets foundThe paper's supplementary materials (MDPI S1) contain paper-specific phenotyping images (post-phenotyping propagation, genotype symptom comparisons, hydroponics setup, growth stages) and a workflow flow chart, publicly downloadable. The underlying phenotype datasets are only 'available if requested', so they do not yetSupplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12234069/s1 , Figure S1: Phenotypic differences between (a) plants at the time of transplanting to potting mix post-phenotyping E1 and (b) 5 weeks later showing growth and pod developmentOpen asset ↗lines:235-249Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Nitric oxide (NO) plays a key role in regulating plant growth, enhances nutrient uptake, and activates disease and stress tolerance mechanisms in most plants. NO is marked as a potential tool for improving the yield and quality of horticultural crop species. Research on NO in plant species can provide an abundance of valuable information regarding this. Hence, we have prepared a simple chemosensor (NPO) for the detection of endogenous NO in chickpea saplings. NPO selectively interacts with NO as determined through a chemodosimetric method to clearly show both the colorimetric and fluorometric changes. After the interaction with NO, the colorless NPO turns yellow as observed by the naked eye and shows bright cyan-blue fluorescence under a UV lamp. The 1 : 1 stoichiometric ratio between NPO and NO is determined from Job's plot resulting in a stable diazeniumdiolate product. The interaction mechanism is well established by absorption, fluorescence titration, NMR titration, HRMS, and DFT calculations. This method has successfully been employed in the plant's root and stem systems to label NO. Confocal microscopy images might help us to understand the endogenous NO generation and the mechanism that happens inside plant tissues.
Why it matches plant phenotyping methods植物組織内の内因性NOを蛍光・比色センサーで標識し、根と茎で可視化する手法の開発および適用が中心であり、植物の生理状態を測定するフェノタイピング手法に該当する。
abstractHence, we have prepared a simple chemosensor (NPO) for the detection of endogenous NO in chickpea saplings.
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) imaging following in situ enzymatic digestion is a versatile analytical method for the untargeted investigation of protein distributions, which has rarely been used for plants so far. The present study describes a workflow for in situ tryptic digestion of plant seed tissue for MALDI MS imaging. Substantial modifications to the sample preparation procedure for mammalian tissues were necessary to cater to the specific properties of plant materials. For the first time, distributions of tryptic peptides were successfully visualized in plant tissue using MS imaging with accurate mass detection. Sixteen proteins were visualized and identified in chickpea seeds showing different distribution patterns, e.g., in the cotyledons, radicle, or testa. All tryptic peptides were detected with a mass resolution higher than 60,000 as well as a mass accuracy better than 1.5 ppm root-mean-square error and were matched to results from complementary liquid chromatography-MS/MS (LC-MS/MS) data. The developed method was also applied to crab's eye vine seeds for targeted MS imaging of the toxic protein abrin, showing the presence of abrin-a in all compartments. Abrin (59 kDa), as well as the majority of proteins visualized in chickpeas, was larger than 50 kDa and would thus not be readily accessible by top-down MS imaging. Since antibodies for plant proteins are often not readily available, in situ digestion MS imaging provides unique information, as it makes the distribution and identification of larger proteins in plant tissues accessible in an untargeted manner. This opens up new possibilities in the field of plant science as well as to assess the nutritional quality and/or safety of crops.
Why it matches plant phenotyping methods植物種子組織中のタンパク質分布という植物状態を可視化するMALDI MSイメージング手法を開発し、複数種子で適用・検証しており、測定法が研究の中心である。
abstractThe present study describes a workflow for in situ tryptic digestion of plant seed tissue for MALDI MS imaging.
Fourier-transform mid-infrared (FT-MIR) spectroscopy with an attenuated total reflectance (ATR) sampling interface is a robust technique applicable to high-throughput phenotyping (HTP). This technique is cost-effective in breeding programs compared with wet chemistry techniques (i.e., GC-MS) due to minimal labor and chemical costs. This study aims to probe the applicability of FT-MIR spectroscopy to phenotype total fatty acids in chickpea flour with partial least-squares (PLS) regression as the principal chemometric model. Using two spectral regions (2845.82–3035.92 cm –1 and 1720.17–1763.03 cm –1 ), three PLS models were built to predict total fatty acids (TFA), total unsaturated fatty acids (TUSFA), and total saturated fatty acids (TSFA) in chickpea flour. These regression models had R 2 values of 0.97, 0.97, and 0.91 and root-mean-square error of prediction (RMSEP) values of 12.49, 6.21, and 5.89 mg/100 g, respectively. Predictions using these models support the implementation of a high-throughput workflow to phenotype fatty acids from chickpea flours in an optimized fashion with minimal labor costs, sample preparation, and chemicals, thus eliminating hazardous wastes supporting plant breeding and food processing industries.
Why it matches plant phenotyping methodsFT-MIRとPLS回帰による chickpea 種子由来試料の脂肪酸形質推定を、育種向けハイスループット表現型解析ワークフローとして開発・評価しており、方法が中心的である。
abstractFourier-transform mid-infrared (FT-MIR) spectroscopy with an attenuated total reflectance (ATR) sampling interface is a robust technique applicable to high-throughput phenotyping (HTP).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Precise and high-throughput phenotyping (HTP) of vegetative drought tolerance in chickpea plant genetic resources (PGR) would enable improved screening for genotypes with low relative loss of biomass formation and reliable physiological performance. It could also provide a basis to further decipher the quantitative trait drought tolerance and recovery and gain a better understanding of the underlying mechanisms. In the context of climate change and novel nutritional trends, legumes and chickpea in particular are becoming increasingly important because of their high protein content and adaptation to low-input conditions. The PGR of legumes represent a valuable source of genetic diversity that can be used for breeding. However, the limited use of germplasm is partly due to a lack of available characterization data. The development of HTP systems offers a perspective for the analysis of dynamic plant traits such as abiotic stress tolerance and can support the identification of suitable genetic resources with a potential breeding value. Sixty chickpea accessions were evaluated on an HTP system under contrasting water regimes to precisely evaluate growth, physiological traits, and recovery under optimal conditions in comparison to drought stress at the vegetative stage. In addition to traits such as Estimated Biovolume (EB), Plant Height (PH), and several color-related traits over more than forty days, photosynthesis was examined by chlorophyll fluorescence measurements on relevant days prior to, during, and after drought stress. With high data quality, a wide phenotypic diversity for adaptation, tolerance, and recovery to drought was recorded in the chickpea PGR panel. In addition to a loss of EB between 72% and 82% after 21 days of drought, photosynthetic capacity decreased by 16-28%. Color-related traits can be used as indicators of different drought stress stages, as they show the progression of stress.
Why it matches plant phenotyping methodsHTPシステムを用いた動的な成長・生理・回復形質の取得と、干ばつ耐性評価への実質的な適用が研究の中心であるため。
abstractPrecise and high-throughput phenotyping (HTP) of vegetative drought tolerance in chickpea plant genetic resources (PGR)
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsヒヨコマメの病害重症度という植物状態を、フィールド非対称イオン移動度分光法とハイパースペクトル画像で非侵襲評価する手法が題名上の中心である。
titleNon-invasive evaluation of Ascochyta blight disease severity in chickpea using field asymmetric ion mobility spectrometry and hyperspectral imaging techniques
Chickpea is a drought-tolerant crop and an important source of protein, relevant to its beneficial effects. The aim of this study was to assess the response to agronomic management, including water and nitrogen supply, of crop physiological and agronomic traits in relation to water use efficiency and grain protein composition. Two varieties, Pascià and Sultano, were grown at two different sites in South Italy under rainfed and irrigated conditions, with and without starter nitrogen fertilization. Crop physiological assessment was carried out by hyperspectral phenotyping at flowering and during grain filling. Increases in grain yield and grain size in relation to water supply were observed for water use up to about 400 mm. Water use efficiency increased under starter nitrogen fertilization, and Pascià showed the highest values (4.8 kg mm−1). The highest correlations of the vegetation indexes with the agronomic traits were observed in the later growth stage, especially for the optimized soil-adjusted vegetation index (OSAVI); furthermore, grain filling rate showed a strong relationship with photochemical reflectance index (PRI). Experimental factors mainly influenced protein composition rather than protein content. In particular, the 7s vicilin protein fraction showed a negative correlation with grain yield and water use, while lectin showed an opposite response. Both fractions are of interest for consumer’s health because of their allergenic and antinutritional properties, respectively. Data from spectral phenotyping will be useful for digital farming applications, in order to assess crop physiological status in modern agricultural systems.
Why it matches plant phenotyping methodsハイパースペクトル表現型計測を用いて作物の生理状態を評価し、スペクトル指標と収量・成長特性の関係を検証しており、測定手法の応用が中心的に扱われている。
abstractCrop physiological assessment was carried out by hyperspectral phenotyping at flowering and during grain filling.
Deep learning is a branch of artificial intelligence.With the benefits of autonomous learning and feature extraction, it has received a lot of attention in recent years from both academic and professional circles.The latest improvements in computer vision formulated through deep learning have paved the method for how to detect and diagnose disease in plants by using a camera to capture image as a basis for recognizing several types of plant disease This system provides an efficient solution for detecting multiple disease in several plants The system is designed to recognize several plant leaf diseases in plants like Maize, Mango, Chickpea, Rice, Cotton, Banana, Watermelon etc.
Why it matches plant phenotyping methods植物葉の画像を用いて深層学習で病害を検出・診断する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法に該当します。
abstractcomputer vision formulated through deep learning have paved the method for how to detect and diagnose disease in plants by using a camera to capture image as a basis for recognizing several types of plant disease
Summary The study the effect of contrast on seeds, growth and the associated anatomy and physiology, with upgraded imaging systems. The use of phase information to explore new information at various stage of the growth. This work benefits, the use of Synchrotron-based DEI and DEI-CT systems to enhance the contrast in plant root architecture and contrast mechanisms, visibility of fine structures of root architecture growth and some aspects of physiology at acceptable level. These non-destructive, imaging systems available at the X-15A beamline, at NSLS, BNL, USA, are utilized. Noticed detailed anatomical and physiological observations, contrast mechanisms, with these upgraded systems, compared to other conventional techniques, equipped with tube source of X-rays. Examined the potential of these systems to quantify the plant roots in situ. The acquired images provided good contrast, anatomical structures and physiology of the plant root micro-architecture. We observed some of the complex plant traits, such as growth, development, root architecture and the associated physiology. The interior structure, root architecture, root morphology, growth of laterals and subsequent laterals can be visualized directly by synchrotron-based imaging techniques. Root architecture of the plant grown from seeds provides new information about the structure and enhancement of some desired property, for example, interior micro-structure of the root laterals and the subsequent laterals and the clear visibility of the leaves in detail. This way, it will be possible to differentiate the weakly and strongly attenuation of the signal traversing within the sample, clearly reflects the acceptable visibility in root laterals, subsequent laterals and the associated opaque matrix with enhanced contrast. The sample has a thin layer of hard structure outside and protein inside. Extinction properties of these samples will be characterized by Sy-DEI and Sy-DEI-CT. This way, we may be able to differentiate softly and weakly attenuation within the sample, to know more about the contrast mechanisms. The visibility, contrast and porosity, with finer details, can be noticed, with Sy-DEI-CT systems as distinguished from Sy-DEI. However, limited field of view, may limit the problems associated with Sy-DEI-CT.
Why it matches plant phenotyping methodsシンクロトロンX線DEI/DEI-CTによる植物根の解剖構造・根系形態・成長・生理の非破壊画像化と定量化可能性が研究の中心であり、植物フェノタイピング手法に該当する。
abstractThe use of phase information to explore new information at various stage of the growth.
The aim of this work was to study the applicability of infrared spectroscopy combined with machine learning techniques to evaluate the uptake and distribution of gold nanoparticles (AuNPs) and single-walled carbon nanotubes (CNTs) in Cicer arietinum L. (chickpea). Obtained spectral data revealed that the uptake of AuNPs and CNTs by the C. arietinum seedlings' root resulted in the accumulation of AuNPs and CNTs at stem and leaf parts, which consequently led to the heterogeneous distribution of nanoparticles. principal component analysis and support vector machine classification were applied to assess its usefulness for evaluating the results obtained using the attenuated total reflectance-Fourier transform infrared spectroscopy method of C. arietinum plant grown at different conditions. Specific wavenumbers that could classify the different nanoparticle constituents of C. arietinum plant extracts according to their ATR-FTIR spectra were identified within three specific regions: 450-503 cm -1 , 750-870 cm -1 , and 1022-1218 cm -1 , based on larger PCA loadings of C. arietinum ATR-FTIR spectra with distinct spectral differences between samples of interest. The current work paves a path to the future fabrication strategies for AuNPs and single-walled CNTs via plant-based routes and highlights the diversity of the applications of these materials in bio-nanotechnology. These results indicate the importance of family-plant selection, choice of methods, and pathways for the efficient biomolecule delivery, drug cargo, and optimal conditions in the wide spectrum of bioapplications.
Why it matches plant phenotyping methods赤外分光法と機械学習を用いて、植物体内のナノ粒子の取り込み・分布という植物状態を評価する方法の適用性と分類性能を検討しており、測定・解析手法が中心である。
abstractThe aim of this work was to study the applicability of infrared spectroscopy combined with machine learning techniques to evaluate the uptake and distribution of gold nanoparticles (AuNPs) and single-walled carbon nanotubes (CNTs) in Cicer arietinum L. (chickpea).
Why it matches plant phenotyping methodsヒヨコマメの種子・植物形質を標準化して評価する2つの表現型プロトコルを提示しており、形質取得手法と再利用可能な標準手順が研究の中心である。
abstractWe present two phenotypic protocols within H2O20 Project INCREASE to characterize, develop, and maintain chickpea single-seed-descent (SSD) line collections.
Abstract Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy is a high‐throughput, cost‐effective method to quantify nutritional traits, such as total protein and sulfur‐containing amino acid (SAA) concentrations, in plant matter. This study used the spectroscopic technique FT‐MIR coupled with attenuated total internal reflectance sampling interface to develop multivariate models for total protein concentration in chickpea (Cicer arietinum L.), dry pea (Pisum sativum L.), and lentil (Lens culinaris Medik.), in addition to SAA concentration in lentil. Total nitrogen data from combustion analysis and SAA data from high‐performance liquid chromatography analysis following acid hydrolysis were used for model calibration and validation. Models for the total protein concentration of chickpea (calibration root mean square error [RMSE] = 0.093, R2 = 0.948, prediction RMSE = 0.10), dry pea (calibration RMSE = 0.096, R2 = 0.845, prediction RMSE = 0.093), and lentil (calibration RMSE = 0.13, R2 = 0.845, prediction RMSE = 0.11) utilized infrared regions associated with protein structures, namely amide bands A, I, and II. In sulfur‐related models for lentil total SAA (calibration RMSE = 0.014, R2 = 0.827, prediction RMSE = 0.022) and methionine (calibration RMSE = 0.0075, R2 = 0.815, prediction RMSE = 0.014) models utilized the C‐S and S‐CH3 stretching and bending bands. Study findings support the conclusion that FT‐MIR spectroscopy is a promising high‐throughput and cost‐effective phenotyping technique that will allow quantifying protein traits quickly and easily in pulse crops.
Why it matches plant phenotyping methodsFT-MIR分光法を用いてマメ科作物のタンパク質・アミノ酸形質を定量するモデルを開発・校正・検証しており、表現型取得法が研究の中心である。
titleFourier‐transform infrared spectroscopy (FTIR) as a high‐throughput phenotyping tool for quantifying protein quality in pulse crops
This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with PlantEye (R) laser scanners. Firstly, we segmented the crop canopies from the background in 3D space using the Region Growing Segmentation algorithm. Then, Convolutional Neural Network (CNN) based ML algorithms were fine-tuned for plant counting. Application of the CNN-based (Convolutional Neural Network) processing architecture was possible only after we reduced the dimensionality of the data to 2D. This allowed for the identification of individual plants and their counting with an accuracy of 93.18% and 92.87% for mung bean and chickpea plants, respectively. These steps were connected to the phenotyping pipeline, which can now replace manual counting operations that are inefficient, costly, and error-prone. The use of CNN in this study was innovatively solved with dimensionality reduction, addition of height information as color, and consequent application of a 2D CNN-based approach. We found there to be a wide gap in the use of ML on 3D information. This gap will have to be addressed, especially for more complex plant feature extractions, which we intend to implement through further research.
Why it matches plant phenotyping methods3Dレーザースキャンから個体植物を分離・計数する画像解析手法を開発し、CNNの精度評価とフェノタイピングパイプラインへの統合を行っており、植物表現型取得が研究の中心である。
abstractThis study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features.
Reproduction assets foundThe paper's plant detection/counting pipeline source code is explicitly published on the authors' GitHub repository, stated in both the Conclusions and Data Availability Statement. No public phenotype dataset or trained model deposit is stated.Code · publicSource code of the proposed pipeline and plant detection, including the following updates, has been published in the following Github repositoriy https://github.com/serkankartal/Machine_Learning_Based_Plant_Detection_on_3D_Canopy_scansOpen asset ↗https://github.com/serkankartal/Machine_Learning_Based_Plant_Detection_on_3D_Canopy_scanslines:101-111Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Multidimensional improvement programs of chickpea require screening of a large number of genotypes for straw nutritive value. The ability of near infrared reflectance spectroscopy (NIRS) to determine the nutritive value of chickpea straw was identified in the current study. A total of 480 samples of chickpea straw representing a nation-wide range of environments and genotypic diversity (40 genotypes) were scanned at a spectral range of 1108 to 2492 nm. The samples were reduced to 190 representative samples based on the spectral data then divided into a calibration set (160 samples) and a cross-validation set (30 samples). All 190 samples were analysed for dry matter, ash, crude protein, neutral detergent fibre, acid detergent fibre, acid detergent lignin, Zn, Mn, Ca, Mg, Fe, P, and in vitro gas production metabolizable energy using conventional methods. Multiple regression analysis was used to build the prediction equations. The prediction equation generated by the study accurately predicted the nutritive value of chickpea straw (R 2 of cross validation > 0.68; standard error of prediction < 1%). Breeding programs targeting improving food-feed traits of chickpea could use NIRS as a fast, cheap, and reliable tool to screen genotypes for straw nutritional quality.
Why it matches plant phenotyping methodsヒヨコマメわらの栄養形質をNIRSで推定する予測モデルを構築・交差検証しており、育種での遺伝子型スクリーニングに用いる表現型取得法が中心である。
abstractThe ability of near infrared reflectance spectroscopy (NIRS) to determine the nutritive value of chickpea straw was identified in the current study.
Abstract Background Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. Results In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5–10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R 2 > 0.99 and a mean absolute percentage error Conclusions Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.
Why it matches plant phenotyping methodsヒヨコマメ個体の3D形態を取得する低コスト撮像システムとオープンソース解析パイプラインを開発し、草丈・表面積を基準値で検証しており、フェノタイピング手法が中心である。
abstractIn this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants.
Reproduction assets foundThe authors deposited the paper's 3D point clouds and meshed chickpea models in an open-access Zenodo repository (DOI 10.5281/zenodo.4018242). Processing scripts are only included as article additional files, and the source images are available only on request from the corresponding author.Dataset · publicThe dataset supporting the conclusions of this article (3D point clouds and meshed models) are available in an open-access Zenodo repository, https://doi.org/10.5281/zenodo.4018242 .Open asset ↗Zenodo · 10.5281/zenodo.4018242lines:149-193Plant phenotyping relevance match · UnverifiedCrossref · checked 9 Sept 2026
Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像による植生指数抽出を、太陽天頂角の影響と地上基準形質との関係を用いて評価・検証しており、フェノタイピング手法が中心である。
abstractNormalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications
To meet the needs of a growing world population, we need to increase the global agricultural yields by employing modern, precision, and automated farming methods. In the recent decade, high-throughput plant phenotyping techniques, which combine non-invasive image analysis and machine learning, have been successfully applied to identify and quantify plant health and diseases. However, these image-based machine learning usually do not consider plant stress's progressive or temporal nature. This time-invariant approach also requires images showing severe signs of stress to ensure high confidence detections, thereby reducing this approach's feasibility for early detection and recovery of plants under stress. In order to overcome the problem mentioned above, we propose a temporal analysis of the visual changes induced in the plant due to stress and apply it for the specific case of water stress identification in Chickpea plant shoot images. For this, we have considered an image dataset of two chickpea varieties JG-62 and Pusa-372, under three water stress conditions; control, young seedling, and before flowering, captured over five months. We then develop an LSTM-CNN architecture to learn visual-temporal patterns from this dataset and predict the water stress category with high confidence. To establish a baseline context, we also conduct a comparative analysis of the CNN architecture used in the proposed model with the other CNN techniques used for the time-invariant classification of water stress. The results reveal that our proposed LSTM-CNN model has resulted in the ceiling level classification performance of \textbf{98.52\%} on JG-62 and \textbf{97.78\%} on Pusa-372 and the chickpea plant data. Lastly, we perform an ablation study to determine the LSTM-CNN model's performance on decreasing the amount of temporal session data used for training.
Why it matches plant phenotyping methods植物画像から水ストレス状態を時系列的に推定するLSTM-CNN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 temporal analysis of the visual changes induced in the plant due to stress and apply it for the specific case of water stress identification in Chickpea plant shoot images.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.g., number of locations and frequency of data acquisition) and throughput. In this study, phenomics technologies were applied to evaluate the performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019. The images were analyzed semi-automatically with custom image processing algorithm and features were extracted, such as canopy area and summary statistics associated with vegetation indices. The study demonstrated significant correlations ( P r up to 0.93 and 0.85 for chickpea and dry pea, respectively), days to 50% flowering ( r up to 0.76 and 0.85, respectively), and days to physiological maturity ( r up to 0.58 and 0.84, respectively). Using image-based features as predictors, seed yield was estimated using least absolute shrinkage and selection operator regression models, during which, coefficients of determination as high as 0.91 and 0.80 during model testing for chickpea and dry pea, respectively, were achieved. The study demonstrated the feasibility to monitor agronomic traits and predict seed yield in chickpea and dry pea breeding trials across multiple locations and seasons using phenomics tools. Phenomics technologies can assist plant breeders to evaluate the performance of breeding materials more efficiently and accelerate breeding programs.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と半自動画像処理により、作物形質を抽出・推定し、複数環境で検証するフェノタイピング手法の実質的応用である。
abstractAn unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
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.
In the recent decade, high-throughput plant phenotyping techniques, which combine non-invasive image analysis and machine learning, have been successfully applied to identify and quantify plant health and diseases. However, these techniques usually do not consider the progressive nature of plant stress and often require images showing severe signs of stress to ensure high confidence detection, thereby reducing the feasibility for early detection and recovery of plants under stress. To overcome the problem mentioned above, we propose a deep learning pipeline for the temporal analysis of the visual changes induced in the plant due to stress and apply it to the specific water stress identification case in Chickpea plant shoot images. For this, we have considered an image dataset of two chickpea varieties JG-62 and Pusa-372, under three water stress conditions; control, young seedling, and before flowering, captured over five months. We have employed a variant of Convolutional Neural Network -Long Short Term Memory (CNN-LSTM) network to learn spatiotemporal patterns from the chickpea plant dataset and use them for water stress classification. Our model has achieved ceiling level classification performance of 98.52% on JG-62 and 97.78% on Pusa-372 chickpea plant data and has outperformed the best reported time-invariant technique by at least 14% for both JG-62 and Pusa-372 species, to the best of our knowledge. Furthermore, our CNN-LSTM model has demonstrated robustness to noisy input, with a less than 2.5 % dip in average model accuracy and a small standard deviation about the mean for both species. Lastly, we have performed an ablation study to analyze the performance of the CNN-LSTM model by decreasing the number of temporal session data used for training.
Why it matches plant phenotyping methods植物画像の時系列変化から水分ストレス状態を推定するCNN-LSTM手法を開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractwe propose a deep learning pipeline for the temporal analysis of the visual changes induced in the plant due to stress
The Stress due to water deficiency in plants can significantly lower the agricultural yield. In recent years, computer vision-based plant phenomics has emerged as a promising tool for plant research. Such techniques have the advantage of being non-destructive, non-evasive and fast. Pulses like chickpeas play an important role towards ensuring food security in poor countries owing to their high protein. In present work, we have compared the performance of traditional Machine Learning with that of deep techniques in classifying water stress using chickpea shoot images. From the experimental result, it is concluded that Deep Learning based methods are superior to the conventional Machine Learning methods. They are superior not only in the performance metrics but also in context that they don't need any handcrafted features or sophisticated feature selection methods. Among all Deep Learning methods, ResNet-18 renders better classification performance than the conventional CNN by attaining 86% and 84% accuracy.
Why it matches plant phenotyping methodsヒヨコマメのシュート画像から水ストレス状態を分類する画像ベースの計算手法を、機械学習と深層学習で比較評価しており、植物表現型・状態推定が研究の中心である。
abstractIn present work, we have compared the performance of traditional Machine Learning with that of deep techniques in classifying water stress using chickpea shoot images.
ChickpeaLentilLaboratory / benchtopRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
A much better understanding of root system development and Measuring fine root growth is significant to the understanding of ecosystem structure and function and in predicting how ecosystems react to climate changeability. Their study, however, is hampered by their underground development, and characterizing complex root system architecture, hence, remains a challenge. Nowadays, a number of methods have been utilized to estimate root growth. Specialised, are costly for reproduced pot experiments, and aren’t promptly available for a nondestructive sampling of roots and soil. Here, Employing a Novel Cheap Rhizotron for Root Growth System Analyses on Plants. The root image securing using cheap technology Due to the inaccessibility of root systems, special methods are required to explore the dissemination and dynamics of roots. Utilizing this Rhizotron development, growth of the response the chickpea (Cicer arietinum L.) and Lentil (Lens culinaris Medik) under the hydroponic systems were explored. Significant differences in both architectural and morphological characteristics were observed among tested genotypes, especially for add up to root length, branch number, and particular root length and department thickness. It comes results illustrated that the framework was effective in screening root characteristics, permitting for rapid measurement of dimensional root architecture over time with negligible unsettling influence on plant growth and without destructive root sampling. The setup allows us to simultaneously characterize the root system and shoot development seedling stages. All components of the system are made from commodity components, locally available worldwide to facilitate the adoption of this affordable technology in low-income countries and detailed methods of construction allow researchers to construct and use similar Rhizotrons for experimental research.
Why it matches plant phenotyping methods安価なRhizotronと根画像取得系を開発し、根系形態・構造を非破壊かつ経時的に測定する方法を提示・評価しており、植物フェノタイピングが中心である。
abstractThe root image securing using cheap technology
The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.
Why it matches plant phenotyping methods植物HTPデータから形質を抽出・補正する解析パイプラインを開発し、実データとシミュレーションで検証しているため、方法が中心的です。
abstractwe developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment.
Reproduction assets foundThe paper explicitly provides two public GitHub repositories: the SpaTemHTP R pipeline package and a validation repository containing all data, scripts, and functions needed to reproduce the paper's phenotyping analyses. Raw phenotypic data itself is only available on request.Code · publicThe R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP .Open asset ↗ICRISAT-GEMS/SpaTemHTPlines:316-319Code · publicAll data, scripts, and functions required to reproduce the results can be found at: https://github.com/ICRISAT-GEMS/SpaTemHTP_Validation .Open asset ↗ICRISAT-GEMS/SpaTemHTP_Validationlines:457-479Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 9 Sept 2026
Published6 Nov 2020The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 2 · OpenAlex ↗
Abstract. This work is undertaken considering the significance of functional phenotyping (primarily measured from continuous profiles of plant-water relations) for crop selection purposes. High-Throughput Plant Phenotyping (HTPP) platforms which largely employ state-of-the-art sensor technologies for acquisition of vast amount of field data, often fail to efficiently translate sensor information into knowledge due to the major challenges of data handling and processing. Hence, it is imperative to concurrently find a way for dissociating noise from useful data. Additionally, another important aspect is understanding how frequent should be the data collection, so that information is maximized. This paper presents a novel approach for identifying the optimal frequency for phenotyping evapotranspiration (ET) by assimilating results from both time series forecast as well as classification models. Thus, at the optimal frequency, plant-water relations can not only be desirably predicted but genotypes can also be classified based on the characteristics of their ET profiles. Consequently, this will aid better crop selection, besides minimizing noise, redundancy, cost and effort in HTPP data collection. High frequency (15 min) ET time series data of 48 chickpea varieties (with considerable genotypic diversity) collected at the LeasyScan HTPP platform, ICRISAT is used for this study. Time series forecast and classification is performed by varying frequency up to 180 min. Multiple performance measures of time series forecast and classification are combined, followed by implementation of entropy theory for sampling frequency optimization. The results demonstrate that ET time series with a frequency of 60 min per day potentially yield the optimum information.
Why it matches plant phenotyping methodsETを用いた植物水分関係の表現型取得について、時系列予測・分類・エントロピー理論により最適なフェノタイピング頻度を開発・評価しており、方法が研究の中心である。
abstractThis paper presents a novel approach for identifying the optimal frequency for phenotyping evapotranspiration (ET) by assimilating results from both time series forecast as well as classification models.
BACKGROUND: Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. RESULTS: Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. CONCLUSION: Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data.
Why it matches plant phenotyping methods屋外HTPのロードセルデータから蒸散応答の特徴量とVPDブレークポイントを自動抽出し、遺伝型を識別する解析手法が研究の中心であるため。
abstractoutdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes.
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1件を確認しました。Code · publicInterested readers can find the R scripts on the open-source GitHub platform, https://github.com/KSoumya/EZTr .Open asset ↗KSoumya/EZTrlines:185-203Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · Crossref · bioRxiv · checked 15 Sept 2026
In this work, we developed a low-cost 3D scanner and used an open source data processing pipeline to phenotype the 3D structure of individual chickpea plants. Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5-10 minutes for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R 2 > 0.99 and a mean absolute percentage error < 10%. We demonstrate the ability to assess several important architectural traits, including canopy volume and projected area, and estimate relative growth rate in commercial chickpea cultivars and lines from local and international breeding collections. Detailed analysis of individual reconstructions also allowed us to investigate partitioning of plant surface area, and by proxy plant biomass.
Why it matches plant phenotyping methods低コスト3Dスキャナとオープンソース処理パイプラインを開発し、植物形態形質の地上測定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す。
abstractwe developed a low-cost 3D scanner and used an open source data processing pipeline to phenotype the 3D structure of individual chickpea plants.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
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.
BarleyChickpeaGreenhouseRootGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
SUMMARY The phenotypic analysis of root system growth is important to inform efforts to enhance plant resource acquisition from soils; however, root phenotyping remains challenging because of the opacity of soil, requiring systems that facilitate root system visibility and image acquisition. Previously reported systems require costly or bespoke materials not available in most countries, where breeders need tools to select varieties best adapted to local soils and field conditions. Here, we report an affordable soil‐based growth (rhizobox) and imaging system to phenotype root development in glasshouses or shelters. All components of the system are made from locally available commodity components, facilitating the adoption of this affordable technology in low‐income countries. The rhizobox is large enough (approximately 6000 cm 2 of visible soil) to avoid restricting vertical root system growth for most if not all of the life cycle, yet light enough (approximately 21 kg when filled with soil) for routine handling. Support structures and an imaging station, with five cameras covering the whole soil surface, complement the rhizoboxes. Images are acquired via the Phenotiki sensor interface, collected, stitched and analysed. Root system architecture (RSA) parameters are quantified without intervention. The RSAs of a dicot species ( Cicer arietinum , chickpea) and a monocot species ( Hordeum vulgare , barley), exhibiting contrasting root systems, were analysed. Insights into root system dynamics during vegetative and reproductive stages of the chickpea life cycle were obtained. This affordable system is relevant for efforts in Ethiopia and other low‐ and middle‐income countries to enhance crop yields and climate resilience sustainably.
Why it matches plant phenotyping methods土壌栽培植物の根系構造を画像取得・解析する、低コストのrhizoboxおよび多カメラ撮像システムを開発しており、根系形態の定量化が研究の中心である。
abstractHere, we report an affordable soil‐based growth (rhizobox) and imaging system to phenotype root development in glasshouses or shelters.
Reproduction assets foundThe paper's data availability statement deposits software, test data, and rhizobox CAD files publicly at the Edinburgh DataShare DOI 10.7488/ds/2841, and materials are also linked at chickpearoots.org/resourcesandlinks. The analysis pipeline code itself is only available on request.Dataset · public, TB, CC and IR developed the growth conditions
for chickpea growth in rhizoboxes. TB, CC, VG, IR, ST and
PD wrote the paper.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
Software, test data for its evaluation and CAD files to con-
struct rhizoboxes have been made available at: https://doi.org/10.7488/ds/2841. Data and code implementing the anal-
ysis pipeline is available on request by emailing the senior/
co-corresponding authors.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. Imaging station for imaging of a rhizobox.
Figure S2. Diagram of image capture anOpen asset ↗10.7488/ds/2841pdf-raw-page:13 lines:1-98Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
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
ChickpeaLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
A semi-hydroponic phenotyping platform was constructed using inexpensive and easily obtained materials for characterizing root trait variability in a large set of chickpea (Cicer arietinum) germplasm. The system was designed to accommodate a large number of plants in a small area allowing relatively deeper root development, and thus serves as a high-throughput phenotyping tool for studying root dynamic growth. The root trait quantitative platform could provide accurate phenotyping data for parameterizing root models and for genome-wide association analyses or mapping studies of quantitative trait loci.
Why it matches plant phenotyping methods根系形質を大規模に測定する半水耕フェノタイピングプラットフォームを構築し、高スループット測定系として提示しているため、方法が研究の中心である。
abstractA semi-hydroponic phenotyping platform was constructed using inexpensive and easily obtained materials for characterizing root trait variability in a large set of chickpea (Cicer arietinum) germplasm.
ChickpeaLaboratory / benchtopRootGrowth / development / phenology
Chickpea is a major protein source in low socio-economic classes and cultivated in marginal soil without fertilizer or irrigation. As a result of its root nodule formation capacity chickpea can directly use atmospheric nitrogen. Chickpea is recalcitrant to stable transformation, particularly root regeneration efficiency of chickpea is low. The composite plant-based system with a non-transformed shoot and transformed root is particularly important for root biologist and this approach has already been used successfully for root nodule symbiosis, arbuscular mycorrhizal symbiosis, and other root-related studies. Use of fluorescent marker-based approach can accurately identify the transformed root from its non-transgenic counterpart. RNAi-based gene knockout, overexpression of genes, promoter GUS analysis to understand tissue specific expression and localization of protein can be achieved using the hairy root-based system. We have already published a hairy root-based transformation and composite plant regeneration protocol of chickpea. Here we are describing the recent modification that we have made to increase the transformation frequency and nodule morphology. Further, we have developed a pouch based artificial system, large number of plants can be scored for its nodule developmental phenotype, by using this system.
Why it matches plant phenotyping methods毛状根形質転換の改良に加え、根粒の発達表現型を多数個体で評価する人工パウチ系を開発しており、植物表現型の取得・評価系が方法論の中心的要素である。
abstractHere we are describing the recent modification that we have made to increase the transformation frequency and nodule morphology.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Pulse crops, such as chickpea (Cicer arietinum L.), serve as excellent food sources that provide proteins and minerals to humans and livestock. Additionally, pulses are important sources of soil nitrogen in rotational cropping systems. However, pulse production is limited by several biotic and abiotic stress factors. One such disease is Ascochyta blight (Ascochyta rabiei) in chickpea. To minimize the impact of Ascochyta blight, timely information on disease outbreak and epidemics is essential for implementing disease control methods. Thus, in this study, the feasibility of monitoring Ascochyta blight disease severity in chickpea using remote sensing techniques was evaluated. Disease severity was monitored using an unmanned aircraft system integrated with different types of sensors (3-band multispectral, 5-band multispectral, and thermal cameras). Results indicated that different flight altitudes (60 m and 90 m above ground level) that lead to different image resolutions did not influence disease detection efficiency, especially with the 3-band camera. Selected image features, including canopy area, percentage of canopy area, and vegetation indices (e.g., green normalized difference vegetation index) from multispectral cameras, and mean canopy temperature from the thermal camera, were significantly correlated with yield and visual ratings of disease severity. Moreover, hyperspectral sensing was found to be useful in predicting disease severity. In summary, this study demonstrated that disease severity of Ascochyta blight in chickpea can be monitored using remote sensing methods under active field conditions. With timely and accurate disease severity information from high-throughput phenotyping technologies, the effects of Ascochyta blight on chickpea yield and quality can be minimized with timely application of proper management techniques.
Why it matches plant phenotyping methodsUAV搭載のマルチスペクトル・熱・ハイパースペクトルセンサーを用いて、ヒヨコマメの病害重症度という植物状態を高スループットに推定・検証しており、表現型取得法が研究の中心である。
abstractthe feasibility of monitoring Ascochyta blight disease severity in chickpea using remote sensing techniques was evaluated
Pea (Pisum sativum L) and chickpea (Cicer arietinum L) are important grain legumes grown in the Palouse region of the Pacific Northwest United States. The USDA-ARS grain legume breeding program in this region focuses on developing pea and chickpea varieties with high yield potential, resistance to biotic and abiotic stresses, and superior agronomic characteristics. In this study, aerial high resolution multispectral imaging was evaluated to phenotype yield potential differences among genotypes in green pea, yellow pea and chickpea. Five experiments (three field pea and two chickpea) with 10–25 varieties grown at two locations (Pullman, Washington; Genesee, Idaho) were assessed. Images were acquired approximately 60, 70 and 90 days after planting (DAP) at 110 m above ground level. Normalized difference vegetation index (NDVI), green normalized difference vegetation index, soil adjusted vegetation index (SAVI) and simple ratio (SR) image based features (SUM, MIN, MAX, MEAN) were extracted. In most cases, the MEAN NDVI data was found to be consistently correlated with dry seed yield (p < 0.05), with green pea genotypes showing strongest relationship (r = 0.64–0.93 at about 70 DAP, both during “plot-by-plot” and “by genotype” comparisons). The MEAN SAVI and SR values were also strongly correlated with yield at 61–72 DAP in most of the pea experiments. The data collected during flowering and early pod development phenological growth stages was found to be useful in yield estimation. The developed methods can be used for early generation evaluation in breeding programs, where yield cannot be estimated due to limited seed availability.
Why it matches plant phenotyping methods航空マルチスペクトル画像から植生指数を抽出し、遺伝子型別の収量ポテンシャルを推定する手法を開発・評価しており、表現型取得と推定が研究の中心です。
abstractaerial high resolution multispectral imaging was evaluated to phenotype yield potential differences among genotypes
Background Phytophthora root rot (PRR) caused by P. medicaginis is a major soil borne disease in chickpea growing regions of Australia. Sources of resistance have been identified in both cultivated and wild Cicer species. However, the molecular basis underlying PRR resistance is not known. Current phenotyping methods rely on mycelium slurry or oospore inoculum. Sensitive and reliable methods are desirable to study variation for PRR resistance in chickpea and allow for a controlled inoculation process to better capture early defence responses following PRR infection. Results In this study, a procedure for P. medicaginis zoospore production was standardized and used as the inoculum to develop a hydroponics based in planta infection method to screen chickpea genotypes with established levels of PRR resistance. The efficiency of the system was both qualitatively validated based on observation of characteristic PRR symptom development, and quantitatively validated based on the amount of pathogen DNA in roots. This system was scaled up to screen two biparental mapping populations previously developed for PRR studies. For each of the screenings, plant survival time was measured after inoculation and used to derive Kaplan-Meier estimates of plant survival (KME-survival). KME-survival and canker length were then selected as phenotypic traits associated with PRR resistance. Genetic analysis of these traits was conducted which identified quantitative trait loci (QTL). Additionally, these hydroponic traits and a set of previously published plant survival traits obtained from multiple PRR field experiments were combined in a model-based correlation analysis. The results suggest that the underlying genetic basis for plant survival during PRR infection within hydroponics and field disease environments is linked. The QTL QRBprrkms03 and QRBprrck03 on chromosome 4 identified for the traits KME-survival and canker length, respectively, correspond to the same region reported for PRR resistance in a field disease experiment. Conclusion A hydroponics based screening system will facilitate reliable and rapid screening in both small- and large-scale experiments to study PRR disease in chickpea. It can be applied in chickpea breeding programs to screen for PRR resistance and classify the virulence of new and existing P. medicaginis isolates.
Why it matches plant phenotyping methods植物の根腐病症状と生存を測定する高速スクリーニング法を開発し、定性的・定量的に検証しており、表現型取得法が研究の中心です。
abstractused as the inoculum to develop a hydroponics based in planta infection method to screen chickpea genotypes with established levels of PRR resistance
Background Accurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles. Results We built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R 2 =0.97. Conclusions We used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited.
Why it matches plant phenotyping methods開花時期という植物形質を気候・遺伝要因から推定する非線形モデルを開発し、性能評価と一般化可能な方法論を提示しているため、計算的フェノタイピング手法が中心です。
abstractWe propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data.
The analysis of root system growth, root phenotyping, is important to inform efforts to enhance plant resource acquisition from soils. However, root phenotyping remains challenging due to soil opacity and requires systems that optimize root visibility and image acquisition. Previously reported systems require costly and bespoke materials not available in most countries, where breeders need tools to select varieties best adapted to local soils and field conditions. Here, we present an affordable soil-based growth container (rhizobox) and imaging system to phenotype root development in greenhouses or shelters. All components of the system are made from commodity components, locally available worldwide to facilitate the adoption of this affordable technology in low-income countries. The rhizobox is large enough (~6000 cm 2 visible soil) to not restrict vertical root system growth for at least seven weeks after sowing, yet light enough (~21 kg) to be routinely moved manually. Support structures and an imaging station, with five cameras covering the whole soil surface, complement the rhizoboxes. Images are acquired via the Phenotiki sensor interface, collected, stitched and analysed. Root system architecture (RSA) parameters are quantified without intervention. RSA of a dicot (chickpea, Cicer arietinum L.) and a monocot (barley, Hordeum vulgare L.) species, which exhibit contrasting root systems, were analysed. The affordable system is relevant for efforts in Ethiopia and elsewhere to enhance yields and climate resilience of chickpea and other crops for improved food security. Significance Statement An affordable system to characterize root system architecture of soil-grown plants was developed. Using commodity components, this will enable local efforts world-wide to breed for enhanced root systems.
Why it matches plant phenotyping methods土壌中植物の根系形態を取得・画像解析する低コストな容器、撮像システム、解析ワークフローを開発しており、フェノタイピング手法が研究の中心である。
abstractHere, we present an affordable soil-based growth container (rhizobox) and imaging system to phenotype root development in greenhouses or shelters.
BarleyChickpeaWheatLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
The objective of this study was to develop a flexible and free image processing and analysis solution, based on the Public Domain ImageJ platform, for the segmentation and analysis of complex biological plant root systems in soil from x-ray tomography 3D images. Contrasting root architectures from wheat, barley and chickpea root systems were grown in soil and scanned using a high resolution micro-tomography system. A macro (Root1) was developed that reliably identified with good to high accuracy complex root systems (10% overestimation for chickpea, 1% underestimation for wheat, 8% underestimation for barley) and provided analysis of root length and angle. In-built flexibility allowed the user interaction to (a) amend any aspect of the macro to account for specific user preferences, and (b) take account of computational limitations of the platform. The platform is free, flexible and accurate in analysing root system metrics.
Why it matches plant phenotyping methods植物根系の3D画像から根長・根角度を抽出する画像解析ツールの開発と精度評価が研究の中心であるため。
abstractThe objective of this study was to develop a flexible and free image processing and analysis solution
Reproduction assets foundThe paper's μCT root image data and analysis files (including the Root1 macro workflow) are stated to be publicly deposited in a Harvard Dataverse dataset with an explicit DOI, directly supporting this paper's root phenotyping measurements and analysis.Dataset · publicAll files are available from the database https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DXG4AH .Open asset ↗doi:10.7910/DVN/DXG4AHlines:45-53Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · OpenAlex · checked 10 Sept 2026
Soil salinity results in reduced productivity in chickpea. However, breeding for salinity tolerance is challenging because of limited knowledge of the key traits affecting performance under elevated salt and the difficulty of high-throughput phenotyping for large, diverse germplasm collections. This study utilised image-based phenotyping to study genetic variation in chickpea for salinity tolerance in 245 diverse accessions. On average salinity reduced plant growth rate (obtained from tracking leaf expansion through time) by 20%, plant height by 15% and shoot biomass by 28%. Additionally, salinity induced pod abortion and inhibited pod filling, which consequently reduced seed number and seed yield by 16% and 32%, respectively. Importantly, moderate to strong correlation was observed for different traits measured between glasshouse and two field sites indicating that the glasshouse assays are relevant to field performance. Using image-based phenotyping, we measured plant growth rate under salinity and subsequently elucidated the role of shoot ion independent stress (resulting from hydraulic resistance and osmotic stress) in chickpea. Broad genetic variation for salinity tolerance was observed in the diversity panel with seed number being the major determinant for salinity tolerance measured as yield. This study proposes seed number as a selection trait in breeding salt tolerant chickpea cultivars.
Why it matches plant phenotyping methods画像ベース表現型解析を用いて塩ストレス下の成長速度を高スループットに測定し、圃場性能との関連も検証しており、表現型取得が研究の主要な方法的要素である。
abstractthe difficulty of high-throughput phenotyping for large, diverse germplasm collections
The standard methodology for assessing seed size distribution of pulses is to sieve seeds into size classes, weighing each class and calculating a weighted mean of the sieve sizes (seed size index). A single unit measure of uniformity of size within a sample via sieving is not available, despite being an important trait in terms of appearance, ease of milling, and consistency in processing. This study investigated several different models for estimating the size variability within seed samples by using a variety of desi and kabuli chickpea, faba bean, lupin, lentil, and mungbean samples. Fitting a normal distribution to the frequency distribution and using the estimated variance parameter as a measure for seed size variability was found to be the most suitable method for attaining seed size and a single value of uniformity. Mean seed size (SSₙₒᵣₘ) and within‐sample size variability (SVₙₒᵣₘ) were unrelated variables, thus allowing selection of more uniform samples of any desired size in breeding programs. Sieve selection is discussed and needs to be appropriate for the samples under investigation. Examples of using the R software to calculate these measures and the R functions needed are available as supplementary files to facilitate the use of this proposed method. This method will be useful to pulse breeders and researchers and in the development of image analysis methods characterizing seed sizes of pulse samples and other grains.
Why it matches plant phenotyping methods種子サイズのばらつきと均一性を定量化する統計モデルおよびR実装を提案しており、植物形質の取得・抽出法が研究の中心である。
abstractA single unit measure of uniformity of size within a sample via sieving is not available
Robust associations between yield and crop growth rate in a species-specific critical developmental window have been demonstrated in many crops. In this study we focus on genotype-driven variation in crop growth rate and its association with chickpea yield under drought. We measured crop growth rate using Normalised Difference Vegetative Index (NDVI) in 20 diverse chickpea lines, after calibration of NDVI against biomass accounting for morphological differences between Kabuli and Desi types. Crops were grown in eight environments resulting from the combination of seasons, sowing dates and water supply, returning a yield range from 152 to 366gm−2. For both sources of variation – environment and genotype – yield correlated with crop growth rate in the window 300°Cd before flowering to 200°Cd after flowering. In the range of crop growth rate from 0.07 to 0.91gm−2°Cd−1, the relationship was linear with zero intercept, as with other indeterminate grain legumes. Genotype-driven associations between yield and crop growth rate were stronger under water stress than under favourable conditions. Despite this general trend, lines were identified with high crop growth rate in both favourable and stress conditions. We demonstrate that calibrated NDVI is a rapid, inexpensive screening tool to capture a physiologically meaningful link between yield and crop growth rate in chickpea.
Why it matches plant phenotyping methodsNDVIをバイオマスに対して校正し、形態差を考慮した作物成長速度の迅速な表現型スクリーニング手法として検証・適用しているため、方法が中心的です。
abstractWe measured crop growth rate using Normalised Difference Vegetative Index (NDVI) in 20 diverse chickpea lines, after calibration of NDVI against biomass accounting for morphological differences between Kabuli and Desi types.
Leaf senescence, an indicator of plant age and ill health, is an important phenotypic trait for the assessment of a plant's response to stress. Manual inspection of senescence, however, is time consuming, inaccurate and subjective. In this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline. As high throughput phenotyping platforms are designed to capture whole-of-plant features, camera lenses and camera settings are inappropriate for the capture of fine detail. Specifically, plant colors in images may not represent true plant colors, leading to errors in senescence estimation. Our algorithm features a color distortion correction and image restoration step prior to a senescence analysis. We apply our algorithm to two time series of images of wheat and chickpea plants to quantify the onset and progression of senescence. We compare our results with senescence scores resulting from manual inspection. We demonstrate that our procedure is able to process images in an automated way for an accurate estimation of plant senescence even from color distorted and blurred images obtained under high throughput conditions.
Why it matches plant phenotyping methods植物の老化を画像から自動推定する補正・復元・解析手法を開発し、手動評価と比較検証しており、表現型取得が研究の中心です。
abstractIn this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline.
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1件を確認しました。Code · publicInstructions for users, software and sample image data will be available online at: https://sourceforge.net/projects/plant-senescence-analysis/ .Open asset ↗plant-senescence-analysislines:246-291Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
In chickpeas, the seed size is a critical phenotype that needs to be evaluated carefully during variety development. The sieve analysis used for determining seed size distribution in legumes is labor intensive and time consuming method. An image-based method for sizing chickpeas seeds was developed in this study. Samples from a total of 72 plots from two different locations were harvested and seed size was analyzed. The results show that seed size calculated from image-based method was highly correlated to the ground-truth data, with a correlation coefficient of 0.90. The image processing technique provides rapid evaluation of seed size for phenotyping chickpeas and the method also can be adapted for similar seed types.
Why it matches plant phenotyping methodsヒヨコマメ種子サイズという植物形質を画像処理で迅速に推定する手法を開発し、実測値との相関で検証しており、フェノタイピング手法が中心である。
abstractAn image-based method for sizing chickpeas seeds was developed in this study.