Accurate, field-scale mapping of crop growth stages is critical for supply-sensitive vegetable production, where timely harvests require detailed phenological information. Consecutive growth stages often involve rapid and subtle morphological changes and are influenced by challenging open-field conditions, which frequently result in misclassification when stages are treated as independent, discrete categories. To address this issue, CropMap is proposed as a growth-stage mapping framework that integrates Hierarchical Semantic Segmentation Networks (HSSN) with multispectral unmanned aerial vehicles (UAVs) imagery. CropMap incorporates the structured biological progression of crop development into the learning objective through tree-based label constraints, allowing the model to recognize phenological continuity and reduce confusion between adjacent stages. The framework is evaluated on the publicly available National Information Society Agency of Korea (NIA) field crop growth-stage dataset, a large-scale, multi-institutional UAV dataset containing 337,665 multispectral patches across six hierarchically related growth stages of Chinese cabbage and radish, curated by the NIA. CropMap achieves a test-set mean Intersection over Union (mIoU) of 0.5382, representing a 5% relative improvement over the best-performing transformer baseline (SegFormer; mIoU = 0.5124). Performance varies across classes: background separation is strong (IoU = 0.9128) and the rosette stage is well distinguished (IoU = 0.6354), while the leaf expansion stage remains the primary challenge (IoU = 0.3541), reflecting the inherent difficulty of mapping this spectrally and morphologically transitional class. These findings indicate that hierarchy-aware learning reduces inter-stage confusion for most phenological classes, but transitional growth stages remain a significant limitation for field-scale deployment. The framework provides a foundation for stage-resolved crop monitoring to support harvest timing and supply forecasting in high-value vegetable systems.
Why it matches plant phenotyping methods作物の生育段階という植物状態を、マルチスペクトルUAV画像と階層型セマンティックセグメンテーションで推定する手法が研究の中心であり、公開データセット上で性能評価も行っている。
abstractCropMap is proposed as a growth-stage mapping framework that integrates Hierarchical Semantic Segmentation Networks (HSSN) with multispectral unmanned aerial vehicles (UAVs) imagery.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Rising temperatures and changing weather conditions are accelerating the spread of plant diseases and increasing the threat to global food security. Reliable detection of leaf diseases is therefore essential to protect crop yields and ensure food quality. Deep learning has proven to be a powerful tool for classifying leaf diseases across various crops. Due to the natural variability of plants, plant diseases often appear in irregular structures. Surface unevenness, folds, or dirt particles are common in field images and can be mistakenly identified as important features by convolutional neural networks (CNNs). This is a challenge that has not been sufficiently addressed in previous studies. This study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure, such as surface irregularities or prominent leaf veins, which may mislead the model. Using stratified five-fold cross-validation on a peer-reviewed dataset, which comprises 2,801 images of radish leaves across five classes (healthy, three disease classes: mosaic virus, black leaf spot, and downy mildew, and one pest-affected class: flea beetle), the proposed method achieved an average and balanced accuracy of 99.86%, establishing a new dataset-level benchmark in the field and demonstrating its effectiveness. The results indicate that the proposed approach may provide a promising basis for future applications in agricultural field monitoring, automated sorting and post-harvest quality control, offering potential to reduce both food waste and associated costs.
Why it matches plant phenotyping methods植物の葉画像から病害状態を分類する深層学習手法の開発・交差検証が研究の中心であり、植物表現型（病害状態）の取得・推定に該当する。
abstractThis study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure
Brassica vegetablesLettuceRadishMicroscopyMultimodalRootVisualization / data management
Microfibers (MFs), primarily originating from sewage sludge and laundry effluents, are the most prevalent form of microplastics (MPs) in agricultural soils. While their ecological effects have been explored, the visualization, crop-level accumulation, and potential transport mechanisms of MFs within soil-plant systems remain poorly understood. This study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions. Three edible vegetables-lettuce, Chinese cabbage, and cherry radish-were used to evaluate species-specific response patterns. The results revealed clear differences in MF interactions across species: lettuce exhibited strong MF adsorption on root surfaces and subsequent penetration via crack-entry and apoplastic pathways without entering cells. In contrast, Chinese cabbage and cherry radish showed limited MF adsorption and no uptake. These patterns were associated with root permeability and antioxidative capacities, indicating that plant functional traits play a critical role in determining the transport capacity of MPs. Beyond introducing a novel method for MF visualization in complex terrestrial matrices, this study provides new insights into the risks posed by MFs to soil-plant systems. The findings also highlight potential threats to food safety and underscore the need to establish plant-specific thresholds and pollution mitigation strategies to support sustainable agriculture and protect public health.
Why it matches plant phenotyping methods植物体内のマイクロファイバー分布・吸着・蓄積・取り込みを可視化する新規蛍光染色・マルチモーダル顕微鏡ワークフローが研究の中心であり、植物状態の測定法として該当する。
abstractThis study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions.
In this study, we investigated the application of three convolutional neural network models YOLOv5, YOLOR, and YOLOv7 for precisely detecting individual radish plants, radish rows, and weeds. A comprehensive dataset was created, capturing diverse conditions and annotated for three target classes: radish, radish-line, and weed. Through extensive experimentation involving 39 combinations of model types, batch sizes (2, 4, 8), and learning rates (0.1, 0.01, 0.001), we determined that the YOLOv5-x model with a batch size of 4 and a learning rate of 0.01 offers superior performance. This configuration achieved a remarkable 99% accuracy for the radish class, 98% for radish-line, and 91% for weed, as confirmed by confusion matrices. Further analysis using the F1-score, Precision-Recall (PR) curves, and training progress plots underscored the model's robustness, particularly its high mAP_0.5:0.95 score. Despite the Weed class posing greater detection challenges, likely due to its lower representation in the dataset, the YOLOv5-x outperformed YOLOR-D6 and YOLOv7-D6 in critical metrics after 300 epochs. This research not only highlights the efficacy of YOLOv5-x in agricultural applications but also suggests potential enhancements in data annotation and model training strategies to further improve weed detection. Our findings provide significant insights for developing automated, high-precision plant-weed detection systems, contributing to more efficient and sustainable agricultural practices.
Why it matches plant phenotyping methodsYOLO画像認識モデルを比較評価し、個体のラディッシュと畝、雑草を検出する手法の性能を検証しており、植物状態の画像ベース取得が研究の中心です。
abstractwe investigated the application of three convolutional neural network models YOLOv5, YOLOR, and YOLOv7 for precisely detecting individual radish plants, radish rows, and weeds.
Understanding plant growth dynamics is essential for applications in agriculture and plant phenotyping. We present the Growth Modelling (GroMo) challenge, which is designed for two primary tasks: (1) plant age prediction and (2) leaf count estimation, both essential for crop monitoring and precision agriculture. For this challenge, we introduce GroMo25, a dataset with images of four crops: radish, okra, wheat, and mustard. Each crop consists of multiple plants (p1, p2, ..., pn) captured over different days (d1, d2, ..., dm) and categorized into five levels (L1, L2, L3, L4, L5). Each plant is captured from 24 different angles with a 15-degree gap between images. Participants are required to perform both tasks for all four crops with these multiview images. We proposed a Multiview Vision Transformer (MVVT) model for the GroMo challenge and evaluated the crop-wise performance on GroMo25. MVVT reports an average MAE of 7.74 for age prediction and an MAE of 5.52 for leaf count. The GroMo Challenge aims to advance plant phenotyping research by encouraging innovative solutions for tracking and predicting plant growth. The GitHub repository is publicly available at https://github.com/mriglab/GroMo-Plant-Growth-Modeling-with-Multiview-Images.
Why it matches plant phenotyping methods植物のマルチビュー画像から葉数と植物齢を推定するデータセット・ベンチマークおよびモデルを提示しており、植物表現型取得・推定が中心である。
abstractWe present the Growth Modelling (GroMo) challenge, which is designed for two primary tasks: (1) plant age prediction and (2) leaf count estimation
The results of the application of the chlorophyll fluorescence and proline content (PC) methods for assessing the drought tolerance of oilseed radish varieties are presented. The interval of Relative humidity of soil (RHS) 60.8% - 35.6% with raising air temperature (0.8°C day-1) and decrease leaf RWC (6.7% day-1) was investigated. An increase F0 and Fst of 1.11 and 0.42 relative fluorescence units (RFU) per 1% decrease in RHS, 2.33 and 0.88 RFU per 1°C increase in air temperature, and 0.35 and 0.13 RFU per 1% decrease in leaf RWC was showed. A decrease Fpl, Fm at 6.57 and 50.1 RFU per 1% decrease RHS, 13.8 and 105.2 RFU per 1°C increase temperature and 2.1 and 15.7 RFU per 1% decrease leaf RWC was determined. An increase PC of 0.20 and 0.07 nmol gDW-1 per RFU increase F0 and Fst and 1.14 and 8.73 nmol gDW-1 per RFU decrease Fpl and Fm was noted. An increase PC of 5.74 nmol gDW-1 per 1% decrease in RHS and 12.05 nmol gDW-1 per 1°C increase in air temperature was proved.
Why it matches plant phenotyping methods乾燥耐性評価を目的に、クロロフィル蛍光とプロリン含量の測定法を適用し、環境条件・葉のRWCとの定量関係を示しているため、植物生理状態のフェノタイピング手法が中心です。
titlePOTENTIAL OF THE PROLINE-FLUORESCENT METHOD FOR ASSESSING PLANT DROUGHT TOLERANCE ON THE TEST OBJECT OF OILSEED RADISH
Radishes, which are common root vegetables, are rich in vitamins and minerals, and contain low calories. This vegetable is known for its rapid growth. Nevertheless, the variety of leaf diseases where leaves get affected by various bacterial and fungal diseases can hinder the healthy growth of radish. Furthermore, there is a high risk of inaccurate identification of diseases if the farmers try to use traditional methods in recognizing these diseases. With the purpose of precise identification of radish leaf diseases for the finest growth of this vegetable, total of 2801 images of the radish leaves are collected from vegetable field in Bangladesh. The collected dataset includes comprehensive images of healthy leaves as well as four types of leaf affected by various diseases such as Black Leaf Spot, Downey Mildew, Flea Beetle and Mosaic. Utilizing this robust dataset, deep learning models can be trained to identify the leaf diseases which helps to detect the diseases in order to reduce the harm of the cultivation of radish. By identifying the diseases on radish leaves accurat-ely and maintaining healthy production of radish, this dataset contributes to the broader sustainability in the agricultural sector.
Why it matches plant phenotyping methodsダイコン葉の病害状態を画像で取得したデータセットの構築が中心であり、植物病害フェノタイピング用の再利用可能な資源に該当する。
abstracttotal of 2801 images of the radish leaves are collected from vegetable field in Bangladesh
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository of 2801 smartphone images of radish leaves (healthy plus four disease classes) collected in Bangladesh, which is the paper's own phenotyping image dataset and is directly accessible.Dataset · publicortant role for classifying the radish plant healthy and unhealthy leaves.
Data source location
1. Vegetable field of Kathalkandi, Nasirnagar, Brahmanbaria, Bangladesh (latitude: 24.1915°, longitude: 91.1826°)
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/s973cz2jcd.1
Direct URL to data: https://data.mendeley.com/datasets/s973cz2jcd/1
1
Value of the Data
•
The dataset containing several classes of radish leaves where each class clearly representing the unhealthy leaf as well as healthy leaf. All the images are captured with high resolution that ensuing the high-quality of leaves images, helps to recognize the pattens of diseases.
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The dataset presentOpen asset ↗Mendeley Data · 10.17632/s973cz2jcd.1lines:1-50Code / dataset availability confirmedCrossref · OpenAlex · checked 15 Sept 2026
Cover crops (CC) immobilize mineral soil N in their biomass, preventing N losses during crop rotation intervals. As the CC biomass is incorporated into the soil and decomposes, N is released for the following main crop. The efficiency of CC N uptake and release depends on CC quantity and quality, which can be enhanced in mixtures. Traditional N uptake measurements are labour-intensive and limited in capturing spatial variability. We calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures. This innovative approach combined vegetation indices, textural features, and a photogrammetry-derived canopy surface model to predict CC traits. Linear models were trained for biomass, N uptake, and C:N predictions, while a K-Nearest-Neighbour model was trained for N concentration. When evaluated on the test set, the calibrated remote sensing models accurately predicted CC aboveground biomass (R 2 : 0.71, RMSE: 287.1 kg/ha, NRMSE: 11.74 %), N concentration (R 2 : 0.80, RMSE: 1.77 gN /kg, NRMSE: 6.96 %), N uptake (R 2 : 0.56, RMSE: 9.38 kgN /ha, NRMSE: 15.08 %), and C:N ratio (R 2 : 0.62, RMSE: 1.86, NRMSE: 10.98 %). The field experiment included monocultures, bi-, and tri-species mixtures of common vetch ( Vicia sativa ), black oat ( Avena strigosa ), and fodder radish ( Raphanus sativus ). N uptake was similar between treatments, yet the CC species differed in strategies, producing high biomass with low N concentration or vice versa. This study provides a basis for spatially predicting key CC traits using UAV optical data.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、テクスチャ特徴、フォトグラメトリ由来モデルを用いて、植物のバイオマス、窒素濃度、窒素吸収量、C:N比を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractWe calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures.
Reproduction assets foundThe paper's Data availability statement explicitly states the authors' R code for image processing, model training, and figure production is publicly available on the authors' WUR GitLab repository (uav4covercroptraits). No phenotype dataset or image deposit is stated separately.Code · publictal for the
UAV data acquisition.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.atech.2024.100608.
Data availability
The R code generated during this study to process the images, train
the models and produce the figures, is publicly available at https://git.wur.nl/dall002/uav4covercroptraits.References
[1] C. Aita, S.J. Giacomini, Crop residue decomposition and nitrogen release in singleOpen asset ↗git.wur.nl/dall002/uav4covercroptraitspdf-raw-page:10 lines:1-89Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
RadishRiceX-ray / CTRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系の発達を対象に、分布型光ファイバーセンサー、信号処理、根の可視化モデルを開発し、X線CTとの比較検証まで行う、植物フェノタイピング手法が中心の研究です。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe authors publicly provide MATLAB code for virtual root reconstruction and the sample datasets used in the study via their GitHub repository Fiber-RADGET, with explicit availability statements in the Methods and Data availability sections.Code · publicThe custom code for the virtual root reconstruction in MATLAB (MathWorks, Massachusetts, USA) is available at https://github.com/mtei1/Fiber-RADGET.git .Open asset ↗mtei1/Fiber-RADGETlines:126-223Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Introduction The future of human space missions relies on the ability to provide adequate food resources for astronauts and also to reduce stress due to the environment (microgravity and cosmic radiation). In this context, microgreens have been proposed for the astronaut diet because of their fast-growing time and their high levels of bioactive compounds and nutrients (vitamins, antioxidants, minerals, etc.), which are even higher than mature plants, and are usually consumed as ready-to-eat vegetables. Methods Our study aimed to identify the best light recipe for the soilless cultivation of two cultivars of radish microgreens (Raphanus sativus, green daikon, and rioja improved) harvested eight days after sowing that could be used for space farming. The effects on plant metabolism of three different light emitting diodes (LED) light recipes (L1-20% red, 20% green, 60% blue; L2-40% red, 20% green, 40% blue; L3-60% red, 20% green, 20% blue) were tested on radish microgreens hydroponically grown. A fluorimetric-based technique was used for a real-time non-destructive screening to characterize plant methabolism. The adopted sensors allowed us to quantitatively estimate the fluorescence of flavonols, anthocyanins, and chlorophyll via specific indices verified by standardized spectrophotometric methods. To assess plant growth, morphometric parameters (fresh and dry weight, cotyledon area and weight, hypocotyl length) were analyzed. Results We observed a statistically significant positive effect on biomass accumulation and productivity for both cultivars grown under the same light recipe (40% blue, 20% green, 40% red). We further investigated how the addition of UV and/or far-red LED lights could have a positive effect on plant metabolite accumulation (anthocyanins and flavonols). Discussion These results can help design plant-based bioregenerative life-support systems for long-duration human space exploration, by integrating fluorescence-based non-destructive techniques to monitor the accumulation of metabolites with nutraceutical properties in soilless cultivated microgreens.
Why it matches plant phenotyping methods蛍光センサーによる非破壊・リアルタイムな植物代謝状態の定量推定が研究の明示的な中心で、分光法による検証も行っているため、植物フェノタイピング手法の応用・検証として採用。
abstractA fluorimetric-based technique was used for a real-time non-destructive screening to characterize plant methabolism.
Thanks to the unique features of wide-field, label-free and depth-resolved imaging, spatial-frequency domain imaging (SFDI) technique has witnessed rapid and considerable progress in biomedical, agricultural and food engineering. This paper reports on the design, characterization and calibration of a multipurpose, multispectral SFDI system (550, 600, 630, 675, 710 and 730 nm) for property and quality assessment of fruits and vegetables. The system was mainly composed of illumination unit, imaging unit and sample stage. Customized software was developed to synchronously control pattern generation and shift, light illuminating, image acquisition and saving. Multiple parameters, including field of view, spatial resolution, uniformity, linearity, repeatability and stability, were used to characterize or calibrate the multispectral SFDI system from a systematic view, based on a set of experiments using optical phantoms. The results indicate that the constructed multispectral SFDI system can be used to collect optical property parameters of common fruits and vegetables after calibration. Then the multispectral SFDI system was applied to measure the optical properties of six types of fruits and vegetables (apple, pear, cucumber, tomato, white and green radish), and the measured optical property mappings were utilized for detecting early-stage bruise, which cannot be observed by naked eyes or even conventional uniform lighting imaging. The results demonstrated that the multispectral SFDI system was capable of measuring optical properties, and the reduced scattering coefficient mapping was superior to the absorption coefficient mapping in detecting early-stage bruise. Selection of effective characteristic wavelength could further enhance the bruise detection.
Why it matches plant phenotyping methods果実・野菜の光学特性と初期損傷を測定するマルチスペクトルSFDIシステムの設計、校正、性能評価が研究の中心であり、植物器官の状態を取得する実質的なフェノタイピング手法である。
abstractThis paper reports on the design, characterization and calibration of a multipurpose, multispectral SFDI system
RadishRiceX-ray / CTRootObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent direct visualization. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We demonstrate that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. We also developed computational models to visualize the roots of root crops and monocotyledons, and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device’s groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系を対象とする分布型光ファイバーセンサーと計算モデルを開発し、根系フェノタイピングへの適用・比較検証まで行うことが中心であるため。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe paper's custom MATLAB code for virtual root reconstruction from fiber-optic strain data is explicitly stated to be publicly available on the authors' GitHub repository (Fiber-RADGET). No separate public phenotype dataset deposit is mentioned; the supplementary movie is not a qualifying dataset URL.Code · publicThe
custom code for the virtual root reconstruction in MATLAB
(MathWorks, Massachusetts, USA) is available at
https://github.com/mtei1/Fiber-RADGET.git.Open asset ↗mtei1/Fiber-RADGETpdf-page:12 lines:1-24Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The pollen germination rate decreases under various abiotic stresses, such as high-temperature stress, and it is one of the causes of inhibition of plant reproduction. Thus, measuring pollen germination rate is vital for understanding the reproductive ability of plants. However, measuring the pollen germination rate requires much labor when counting pollen. Therefore, we used the Yolov5 machine learning package in order to perform transfer learning and constructed a model that can detect germinated and non-germinated pollen separately. Pollen images of the chili pepper, Capsicum annuum, were used to create this model. Using images with a width of 640 pixels for training constructed a more accurate model than using images with a width of 320 pixels. This model could estimate the pollen germination rate of the F 2 population of C. chinense previously studied with high accuracy. In addition, significantly associated gene regions previously detected in genome-wide association studies in this F 2 population could again be detected using the pollen germination rate predicted by this model as a trait. Moreover, the model detected rose, tomato, radish, and strawberry pollen grains with similar accuracy to chili pepper. The pollen germination rate could be estimated even for plants other than chili pepper, probably because pollen images were similar among different plant species. We obtained a model that can identify genes related to pollen germination rate through genetic analyses in many plants.
Why it matches plant phenotyping methods機械学習画像解析により、植物花粉の発芽・未発芽を識別し、花粉発芽率という植物生殖形質を自動推定する手法を開発・適用しており、表現型取得が研究の中心である。
abstractwe used the Yolov5 machine learning package in order to perform transfer learning and constructed a model that can detect germinated and non-germinated pollen separately.
CassavaRadishField / plotRootYield / biomass estimationRoot system architecture
Abstract Background: Root phenotyping methods are of increasing importance as researchers seek to understand belowground productivity and breeders work to select for root traits. Effective non-destructive root phenotyping methods do not exist for bulked-root and tuber crops such as potato and cassava. Cassava is a tropical crop widely grown by subsistence farmers throughout the tropics and is the fourth most important staple food crop in the world, yet lags in research. It has an extensive growth period sometimes exceeding 12 months. Early maturity is a major goal for breeders, but the ability to select for it is hampered by the lack of non-destructive yield estimation methods. GPR is a tool with potential to aid in bulked root selection, but standard methods have yet to be developed. In this study, we demonstrate good practice in GPR estimation of root mass, which was used as a proxy for cassava root mass, and investigate the effect of soil water content on measurement. Results: Significant correlation between GPR data and daikon root mass was found for three of the five irrigation treatments. Correlation strength improved with increased soil water content and decreased variation of soil water content between plots. Pearson correlation coefficient varied from 0.53 – 0.79. Conclusions: GPR can be used to estimate bulked root mass. Wet soil can improve the predictive quality of GPR data, but water content needs to be homogeneous throughout the study site and period. Determining the optimal soil water content will require further research.
Why it matches plant phenotyping methodsGPRによる非破壊的なバルク根量推定法を開発・検証し、土壌水分が測定性能に与える影響も評価しているため、植物フェノタイピング手法が中心である。
abstractwe demonstrate good practice in GPR estimation of root mass, which was used as a proxy for cassava root mass, and investigate the effect of soil water content on measurement.
Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.
Why it matches plant phenotyping methodsUAS画像とOBIAを用いて作物の面積・体積を推定し、乾物バイオマスとの相関で検証した非破壊フェノタイピング手法の開発・適用が中心である。
abstractDevelopment of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales.
Zinc as a micronutrient and cadmium as a nonessential toxic element share similar pathways for entering plant tissues and thus may be antagonistic. In nutrient solution culture, 17-day-old radish (Raphanus sativus L) plants were exposed to short-term (24 h) equimolar metal contamination (2.2 µM of each 70 Zn and total Cd) to investigate the in situ Zn/Cd distribution in the apical root tissues using high-resolution secondary ion mass spectrometry (NanoSIMS) imaging. Inductively-coupled plasma mass spectrometry analysis of bulk root tissue confirmed large root uptake of both metal elements. After 24-h exposure the total root concentration (in µg/g DW) of 70 Zn was 180 ± 24 (mean±SE) and of total Cd 352 ± 11. NanoSIMS mapping was performed on the cross sections of the radish root apex as a crucial component in root growth and uptake of water and nutrients from soil. Elemental maps of 70 Zn and 114 Cd isotopes revealed greater enrichment of both metals in the outer epidermal root layer than in cortical tissues and especially stele, confirming the epidermal root cells as preferential sites of metal uptake, and indicating relatively slow and less-intensive metal transport into other parts (edible hypocotyl, shoot) of metal-sensitive radish. NanoSIMS has been confirmed as a powerful tool for spatial detection and visualisation of some ultra-trace metal isotopes (e.g. 70 Zn) in the fast-growing root tips. However, precise (sub)cellular mapping of diffusible metallic ions (Cd, Zn) remains a technically-challenging task in plant specimens given an unavoidable compromise between optimising methodology for structural preservation vs. authentic in vivo ion localisation.
Why it matches plant phenotyping methodsNanoSIMSによる根端内の金属イオン空間分布の画像取得が研究の中心であり、植物の元素局在状態を測定するとともに、空間マッピングの技術的有効性と限界を評価している。
abstractinvestigate the in situ Zn/Cd distribution in the apical root tissues using high-resolution secondary ion mass spectrometry (NanoSIMS) imaging
Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.
Why it matches plant phenotyping methodsUAV-RGB画像、Otsu/ExGセグメンテーション、SfMによる草高推定と回帰モデルを開発・検証し、作物の生体形質を定量化しているため、フェノタイピング手法が中心である。
abstractThis study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties.
OatRadishLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
Plant roots growing through soil typically encounter considerable structural heterogeneity, and local variations in soil dry bulk density. The way the in situ architecture of root systems of different species respond to such heterogeneity is poorly understood due to challenges in visualising roots growing in soil. The objective of this study was to visualise and quantify the impact of abrupt changes in soil bulk density on the roots of three cover crop species with contrasting inherent root morphologies, viz. tillage radish (Raphanus sativus), vetch (Vicia sativa) and black oat (Avena strigosa). The species were grown in soil columns containing a two-layer compaction treatment featuring a 1.2 g cm-3 (uncompacted) zone overlaying a 1.4 g cm-3 (compacted) zone. Three-dimensional visualisations of the root architecture were generated via X-ray computed tomography, and an automated root-segmentation imaging algorithm. Three classes of behaviour were manifest as a result of roots encountering the compacted interface, directly related to the species. For radish, there was switch from a single tap-root to multiple perpendicular roots which penetrated the compacted zone, whilst for vetch primary roots were diverted more horizontally with limited lateral growth at less acute angles. Black oat roots penetrated the compacted zone with no apparent deviation. Smaller root volume, surface area and lateral growth were consistently observed in the compacted zone in comparison to the uncompacted zone across all species. The rapid transition in soil bulk density had a large effect on root morphology that differed greatly between species, with major implications for how these cover crops will modify and interact with soil structure.
Why it matches plant phenotyping methodsX線CTと自動根セグメンテーションを用いて根系形態を3次元可視化・定量化しており、植物表現型の取得・抽出が研究の中心である。
abstractThe objective of this study was to visualise and quantify the impact of abrupt changes in soil bulk density on the roots of three cover crop species with contrasting inherent root morphologies
Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.
Why it matches plant phenotyping methodsUAV搭載光学・ハイパースペクトルデータから植物形質を推定する回帰モデルを構築し、校正・独立検証まで実施しており、植物形質取得法の適用と技術検証が中心である。
abstractHere we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely.