Accurate, non-destructive assessment of watermelon ripeness remains a significant challenge in horticultural production, particularly under field conditions where traditional visual and tactile evaluation methods are subjective and often inconsistent. Although mechanical, acoustic, and spectroscopic techniques have demonstrated promising performance, their reliance on controlled laboratory environments limits their practical applicability in real-world agricultural settings. This study presents a field-deployable, AI-assisted computer vision system designed for objective, real-time classification of watermelon ripeness. The proposed prototype combines controlled illumination with RGB imaging and convolutional neural networks trained on thousands of annotated outdoor images collected over multiple growing seasons. A phased development strategy—encompassing proof-of-concept modelling, field integration, and multi-season validation—supports robustness against variable lighting conditions and environmental influences. The anticipated outcome is a reliable, non-destructive decision-support tool for growers, capable of identifying ripe fruit for manual harvesting while providing a technological foundation for future autonomous harvesting and precision agriculture applications.
Why it matches plant phenotyping methodsスイカ果実の成熟度という植物器官の状態を、RGB画像とCNNで非破壊推定する手法を開発し、圃場統合と複数季節の検証まで行うため、フェノタイピング手法が中心です。
abstractThis study presents a field-deployable, AI-assisted computer vision system designed for objective, real-time classification of watermelon ripeness.
Three-dimensional surface reconstruction is essential for accurately acquiring the external quality parameters of watermelons, such as size, volume, and defect area. Binocular stereo vision provides a low-cost and easily deployable solution for the single-view 3D reconstruction of watermelons. However, watermelons present highly similar surface textures, and as typical spheroid-like objects, the excessive angle between surface normals of edge regions and the camera optical axis leads to insufficient feature representation. Consequently, directly applying existing stereo matching algorithms often introduces matching ambiguities, and lightweight networks struggle to balance real-time performance with matching accuracy. This study focuses on the high-precision single-view point cloud generation of Kirin watermelons. To address these issues, we first construct a cross-modal, high-precision Kirin watermelon stereo matching dataset. Building upon the Fast-ACVNet+ architecture, we then propose MI-ACVNet, a lightweight stereo matching network tailored for high-precision watermelon point cloud acquisition. In the feature extraction stage, a Multi-Scale Stereo Feature Extraction (MSFE) module is adapted. By incorporating the re-parameterized network MobileOne and Epipolar-Enhanced Coordinate Attention (E2CA), MSFE improves the discriminative capability for weak and similar textures without compromising inference speed. For cost computation, a Coarse-to-Fine Cascaded Residual Correction (C2F-CRC) strategy is incorporated to construct a fine-grained cost volume via sub-pixel interpolation, enhancing the network’s ability to capture subtle surface fluctuations. Furthermore, a Semantics-Guided Region-Aware Loss (SGRA-Loss) is formulated, leveraging semantic masks to apply differentiated supervision weights across edge, center, and background regions to significantly improve edge matching accuracy. Ablation studies validate the effectiveness of the MSFE, C2F-CRC, and SGRA-Loss components. Compared to the baseline model, the full MI-ACVNet reduces the End-Point Error (EPE) by 19.5% and the Bad-0.5 error rate by 34.5% in the watermelon region. Furthermore, when compared against five mainstream algorithms (StereoNet, AANet, HSMNet, LightStereo-L, and NMRF-swint), MI-ACVNet achieves state-of-the-art performance: EPE and Bad-0.5 are reduced to 0.091 pixels and 1.159%, respectively, with a single-frame inference time of only 46 ms. The average depth error of the reconstructed point clouds is merely 0.26 mm. By ensuring both real-time efficiency and high-precision depth estimation, this method demonstrates promising potential for deployment in industrial Kirin watermelon sorting lines, driving sorting equipment toward higher precision and intelligence.
Why it matches plant phenotyping methodsスイカのサイズ・体積・欠陥面積などの外部形質を取得するためのステレオ画像再構成手法を開発し、データセット構築、アブレーション、既存手法比較で検証しているため、植物フェノタイピング手法が中心である。
abstractThree-dimensional surface reconstruction is essential for accurately acquiring the external quality parameters of watermelons, such as size, volume, and defect area.
Accurate monitoring of crop nitrogen status is essential to optimize fertilization management and reduce nitrate losses in intensive horticultural systems. This study aimed to calibrate crop monitoring tools based on ion-selective electrodes and remote sensing indices for nitrogen status assessment in horticultural crops.Field experiments were conducted during the 2025 growing season on broccoli and watermelon grown under Mediterranean conditions and subjected to different nitrogen fertilization levels. Crop nitrogen status was assessed using complementary approaches. Multispectral satellite imagery and UAV-based hyperspectral data were used to calculate vegetation indices related to chlorophyll and nitrogen status, including NDRE, GNDVI, TCARI and OSAVI. These indices were calibrated against leaf nitrogen concentration and nitrate content determined by conventional laboratory analyses. In parallel, xylem sap was extracted from leaves and analyzed using ion-selective electrodes to determine nitrate concentration.Strong relationships were observed between nitrogen supply, spectral indices and nitrate concentration in xylem sap, enabling the development of calibration models for real-time crop nitrogen monitoring. The integration of proximal sensing with remote sensing improved the robustness of nitrogen diagnostics across crops and growth stages.These results highlight the potential of combining ion-selective electrodes and remote sensing tools as decision-support systems for optimized nitrogen management.
Why it matches plant phenotyping methods植物の窒素状態を対象に、イオン選択電極・衛星/UAVリモートセンシングの校正モデルを開発・検証しており、表現型取得手法が研究の中心である。
abstractThis study aimed to calibrate crop monitoring tools based on ion-selective electrodes and remote sensing indices for nitrogen status assessment in horticultural crops.
ABSTRACT Three‐dimensional measurement technology based on point clouds can effectively solve the problem of plant occlusion and is a hot research direction for plant phenotyping methods. Rapid and low‐cost 3D reconstruction and accurate 3D point cloud segmentation are two major challenges in 3D phenotyping technology. Taking watermelon seedlings as an example, we proposed a new phenotyping method that uses the Instant‐NGP for 3D reconstruction and the improved PointNet++ for 3D point cloud segmentation. We performed dynamic downsampling and filtering based on the point cloud scale and designed different phenotypic measurement methods for hypocotyl and leaf point clouds. To overcome the difficulty of measuring the hypocotyl caused by slenderness, curvature and inclination, we proposed a segmented stem 3D point cloud skeleton extraction algorithm. The experimental results show that our method achieved satisfactory measurement results for the seedling phenotypes of four growth stages. The detection accuracy of the number of cotyledon leaves and the number of true leaves both exceed 95% and the coefficient of determination ( R 2 ) of leaf area, hypocotyl length and stem diameter phenotypes are all beyond 0.8. The proposed method provides a novel, efficient and precise 3D plant phenotyping solution, with good application and promotion value.
Why it matches plant phenotyping methods3D再構成、点群セグメンテーション、骨格抽出、形質測定を統合した植物フェノタイピング手法の開発が中心であり、精度評価も実施している。
abstractwe proposed a new phenotyping method that uses the Instant‐NGP for 3D reconstruction and the improved PointNet++ for 3D point cloud segmentation.
Sap flow serves as the primary carrier for water, nutrients, and signaling molecules, playing a crucial role in fruit development by delivering these essential constituents to the fruit. While the efflux of sap from fruit to other organs (termed reverse sap flow) has been observed in plants, its underlying mechanisms remain unclear due to a lack of effective methodologies for comprehensive studies. Here, we pioneered the integration of real-time sap flow measurements from novel plant-wearable sensors with synchronized environmental monitoring, establishing a multimodal data framework to systematically decode the endogenous causes and exogenous triggers of reverse sap flow in watermelon plants. Our experimental results reveal that plant water supply-consumption imbalance is the core endogenous cause of reverse sap flow, which is induced by two external triggers in the natural environment: rapid light intensity surges and soil drought. Furthermore, a long-term drought stress experiment illustrates that reverse sap flow from the fruit enhances the drought resistance of plants by adjusting water redistribution within the whole plant. This study challenges the unitary view of fruit solely as a "sink" in the traditional source-sink theory, further refines the understanding of the source-sink paradigm, and provides a novel mechanism and insight for plant drought tolerance strategies.
Why it matches plant phenotyping methods新規の植物ウェアラブルセンサーによるリアルタイム樹液流計測と環境モニタリングの統合が研究の中心で、植物の水輸送状態という生理形質を取得・解析している。
abstractlack of effective methodologies for comprehensive studies
Drought stress is a major constraint on watermelon production worldwide. Conventional phenotyping methods for drought tolerance are often low-throughput and fail to capture dynamic physiological responses. This study validated the high-throughput phenotyping platform (Plantarray 3.0) against conventional methods by dynamically evaluating drought tolerance across 30 genetically diverse watermelon accessions. The Plantarray system quantified key dynamic traits, including transpiration rate (TR), transpiration maintenance ratio (TMR), and transpiration recovery ratios (TRRs), revealing distinct drought-response strategies. Principal component analysis (PCA) of these dynamic traits explained 96.4% of the total variance (PC1: 75.5%, PC2: 20.9%), clearly differentiating genotypes. A highly significant correlation (R = 0.941, p < 0.001) was found between the comprehensive drought tolerance rankings derived from Plantarray and conventional phenotyping. We identified five genotypes as highly tolerant and four as highly sensitive. The elite drought-tolerant germplasm, notably the wild species PI 537300 (Citrullus colocynthis) and the cultivated variety G42 (Citrullus lanatus), exhibited superior physiological performance and recovery capacity. The results demonstrate that the Plantarray system not only efficiently screens for drought tolerance but also provides deep insights into dynamic resistance mechanisms, offering a powerful tool and valuable genetic resources for breeding climate-resilient watermelon cultivars.
Why it matches plant phenotyping methodsPlantarray高スループット表現型解析プラットフォームを従来法と比較検証し、動的な植物生理形質による乾燥耐性評価を中心に扱っているため。
abstractThis study validated the high-throughput phenotyping platform (Plantarray 3.0) against conventional methods by dynamically evaluating drought tolerance across 30 genetically diverse watermelon accessions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
High-throughput plant phenotyping (HTPP) technologies are rapidly transforming plant science by enabling real-time, non-invasive, and large-scale monitoring of complex morphological, physiological, and biochemical traits. However, existing platforms often lack integration across sensing modalities and analytical depth necessary for early and comprehensive phenotypic trait analysis. In this study, we developed a fully automated, multimodal HTPP system combining RGB, shortwave infrared (SWIR) hyperspectral, multispectral fluorescence imaging (MSFI), and thermal imaging to characterize drought-stressed watermelon (Citrullus lanatus) plants. RGB imaging facilitated detailed morphological analysis by extracting color-based traits, quantifying plant height and canopy area, and accurately distinguishing growth stages. SWIR hyperspectral imaging (HSI) enabled non-invasive biochemical assessment by detecting drought-responsive compounds, such as flavonoids, phenolics, and antioxidant activities, while also supporting the classification of stress severity. This spectral profiling revealed key biochemical alterations triggered by water deficit. MSFI liquid crystal tunable filter (LCTF-based) measured chlorophyll a (Chl-a), chlorophyll b (Chl-b), and total chlorophyll (t-Chl) levels, providing critical insights into photosynthetic performance under drought stress. Thermal imaging further enhanced drought assessment by capturing canopy temperature variations, which were used to derive thermal indices for indirect estimation of soil volumetric water content (SVWC). By integrating complementary imaging modalities, the proposed system captured comprehensive phenotypic responses with high predictive accuracy for early detection of drought stress and assessment of plant health. Advanced machine learning (ML) and deep learning (DL) models further enhanced trait extraction and classification, enabling robust analysis of complex, high-dimensional data. This automated, multimodal platform offers scalable, non-invasive crop monitoring, providing precise insights to support drought resilience and precision agriculture.
Why it matches plant phenotyping methods複数の画像・センシングモダリティを統合した自動高スループット植物表現型解析システムを開発し、形態・生理・生化学的形質および乾燥ストレスを抽出することが研究の中心である。
abstractIn this study, we developed a fully automated, multimodal HTPP system combining RGB, shortwave infrared (SWIR) hyperspectral, multispectral fluorescence imaging (MSFI), and thermal imaging to characterize drought-stressed watermelon (Citrullus lanatus) plants.
Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery. To achieve this, a state-of-the-art aerial micrometeorological station (AMMS), equipped with an infrared thermal sensor, temperature–humidity sensor, and advanced multispectral and thermal cameras is mounted on an unmanned aerial system (UAS), thus minimizing crop field intervention and permanently installed equipment maintenance. Additionally, data from satellite systems and ground micrometeorological stations (GMMS) are integrated to enhance and upscale system results from the local field to the regional level. The research was conducted over two years of pilot testing in the municipality of Trifilia (Peloponnese, Greece) on pilot potato and watermelon crops, which are primary cultivations in the region. Results revealed that empirical irrigation applied to the rhizosphere significantly exceeded crop water needs, with over-irrigation exceeding by 390% the maximum requirement in the case of potato. Furthermore, correlations between high-resolution remote and proximal sensors were strong, while associations with coarser Landsat 8 satellite data, to upscale the local pilot field experimental results, were moderate. By applying a comprehensive model for upscaling pilot field results, to the overall Trifilia region, project findings proved adequate for supporting sustainable irrigation planning through simulation scenarios. The results of this study, in the context of the overall services introduced by the project, provide valuable insights for farmers, agricultural scientists, and local/regional authorities and stakeholders, facilitating improved regional water management and sustainable agricultural policies.
Why it matches plant phenotyping methods熱・マルチスペクトル画像と気象センサーを統合し、作物の水ストレス状態（crop water stress index）を推定する取得・推定システムが研究の中心であり、フィールドおよび衛星データとの相関検証も行っている。
abstractaiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract Watermelon cultivation plays an important socioeconomic role in Brazil. Despite all efforts, in genetic improvement programs, selection often involves a limited number of accessions due to complex cultivation requirements for this species, including large evaluation areas. The requirement of large areas results in increased costs and time of evaluations using conventional methodologies. In this context, we assessed the use of imaging to optimize resources and field evaluation time. The study aimed to use image phenotyping with various vegetation indices for watermelon germplasm. In this study, we evaluated 118 watermelon genotypes. In-field manual measurements included the SPAD index of leaves and average Brix and weight of fruits. To determine the potential use of imaging in phenotyping, four vegetation indices —NGRDI, GLI, SAVI, and NDVI — were studied. The germplasm exhibited genetic dissimilarity and the agronomic performance was monitored using images. The NGRDI, NDVI, SAVI and GLI indices remarkably correlated with the BRIX variable and SPAD index, even in germplasms with high dissimilarity. In addition, remote sensing was used to monitor field parameters that were not directly related to the leaf. The NGRDI, NGRDI and SAVI indices were sensitive in capturing this indirect relationship with the BRIX variable.
Why it matches plant phenotyping methodsスイカ遺伝資源の表現型評価を効率化するため、画像および植生指数を用いたフェノタイピングを中心に検討し、手測定値との相関で有用性を評価している。
abstractwe assessed the use of imaging to optimize resources and field evaluation time
SoybeanTomatoWatermelonField / plotFruitPhysiological trait estimationStress / disease detectionGrowth / development / phenologyStomatal traitsWater status / transpiration
The integration of flexible electronics with plant science has generated various plant-wearable sensors, yet challenges persist in their application to real-world agriculture, particularly in high-throughput settings. Overcoming the trade-off between sensing sensitivity and range, adapting sensors to a wide range of crop types, and bridging the gap between sensor measurements and biological understandings remain primary obstacles. Here, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges. PlantRing employs bio-sourced carbonized silk georgette as the strain-sensing material, offering an exceptional detection limit (0.03%–0.17% strain, depending on sensor model), high stretchability (tensile strain up to 100%), and remarkable durability (season-long use). PlantRing effectively monitors plant growth and water status by measuring organ circumference dynamics, performing reliably under harsh conditions, and adapting to a wide range of plant species. Applying PlantRing to study fruit cracking in tomato and watermelon has revealed a novel hydraulic mechanism characterized by genotype-specific excess sap flow within the plant to fruiting branches. Its high-throughput application has enabled large-scale quantification of stomatal sensitivity to soil drought—a long-standing aspiration in plant biology—facilitating the selection of drought-tolerant germplasm. Combining PlantRing with a soybean mutant has led to the discovery of a potential novel function of the circadian clock gene GmLNK2 in stomatal regulation. More practically, integrating PlantRing into feedback irrigation achieves simultaneous water conservation and quality improvement, signifying a paradigm shift from reliance on experience or environmental cues to plant-based feedback control. Collectively, PlantRing represents a groundbreaking tool poised to revolutionize botanical studies, agriculture, and forestry.
Why it matches plant phenotyping methods植物器官周径を測定するウェアラブル高スループットセンサーを開発し、成長・水分状態・気孔感度などの表現型を定量化する方法が研究の中心である。
abstractHere, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges.
The industrial implementation of non-destructive techniques for the classification of watermelons, according to their quality standards and stage of maturity, is highly sought by the handling and processing industry. This study aimed to evaluate the feasibility of near-infrared spectroscopy (NIRS) for the individual internal quality assessment of intact watermelons, simulating industrial sorting lines. Two online near infrared (NIR) sensors, a diode array (DA) and a Fourier-transform (FT) spectrometer, each characterised by distinct optical configurations and technical specifications, were utilised. These sensors operated in reflectance mode, analysing the fruits in both static mode (conveyor belt stopped) and dynamic mode on a moving conveyor belt at two different speeds. Regression and classification models were developed for the prediction of soluble solid content (SSC) and the classification of the maturity stage, respectively, by applying various signal pre-treatment methods to the NIR spectra. The best results for SSC prediction were achieved using the DA instrument in dynamic mode, with no significant differences (P > 0.05) between the two conveyor speeds tested. Specifically, a residual predictive deviation for cross-validation (RPD cv ) of 1.41 was achieved with the DA sensor in dynamic mode and a conveyor speed of 10.5 cm s -1 . Furthermore, for the same instrument, mode, and speed, the proportion of fruits accurately classified as 'mature' and 'immature' in the training set was 76 % and 82 %, respectively, with corresponding values of 90 % and 70 % for the validation set. The findings are promising for the horticultural industry, demonstrating the potential for incorporating NIRS technology into industrial sorting lines for the internal quality assessment of individual watermelons.
Why it matches plant phenotyping methodsNIRSセンサーを用いてスイカ個体の糖度と成熟段階を非破壊推定し、オンライン搬送条件、センサー構成、回帰・分類モデルを評価しており、植物形質取得法が研究の中心である。
abstractThis study aimed to evaluate the feasibility of near-infrared spectroscopy (NIRS) for the individual internal quality assessment of intact watermelons, simulating industrial sorting lines.
SoybeanTomatoWatermelonFruitPhysiological trait estimationGrowth / development / phenologyStomatal traitsWater status / transpiration
The integration of flexible electronics with plant science has generated various plant-wearable sensors, yet challenges persist in their application to real-world agriculture, particularly in high-throughput settings. Overcoming the trade-off between sensing sensitivity and range, adapting sensors to a wide range of crop types, and bridging the gap between sensor measurements and biological understandings remain primary obstacles. Here, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges. PlantRing employs bio-sourced carbonized silk georgette as the strain-sensing material, offering an exceptional detection limit (0.03%-0.17% strain, depending on sensor model), high stretchability (tensile strain up to 100%), and remarkable durability (season-long use). PlantRing effectively monitors plant growth and water status by measuring organ circumference dynamics, performing reliably under harsh conditions, and adapting to a wide range of plant species. Applying PlantRing to study fruit cracking in tomato and watermelon has revealed a novel hydraulic mechanism characterized by genotype-specific excess sap flow within the plant to fruiting branches. Its high-throughput application has enabled large-scale quantification of stomatal sensitivity to soil drought-a long-standing aspiration in plant biology-facilitating the selection of drought-tolerant germplasm. Combining PlantRing with a soybean mutant has led to the discovery of a potential novel function of the circadian clock gene GmLNK2 in stomatal regulation. More practically, integrating PlantRing into feedback irrigation achieves simultaneous water conservation and quality improvement, signifying a paradigm shift from reliance on experience or environmental cues to plant-based feedback control. Collectively, PlantRing represents a groundbreaking tool poised to revolutionize botanical studies, agriculture, and forestry.
Why it matches plant phenotyping methodsPlantRingは植物器官の周径変化を測定し、成長・水分状態・気孔感度などの表現型を高スループットに取得するウェアラブルセンサーシステムであり、センサー開発と実証が研究の中心です。
abstractHere, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges.
Accurate phenotypic analysis is crucial for crop breeding and genetic research. Traditionally, watermelon fruit skin phenotypes have been evaluated manually, which limits precision, particularly for complex traits. This study aims to develop a quantitative approach for analyzing watermelon fruit skin phenotypic traits and to assess their potential in detecting fruit maturity and quality. The study primarily introduces the lacunarity algorithm for quantifying watermelon skin texture characteristics. The effectiveness of the lacunarity algorithm was validated through its application in classifying different texture patterns using various machine learning algorithms. In addition to the lacunarity algorithm, features based on the gray-level co-occurrence matrix (GLCM), color features, and stripe area ratio were extracted as skin phenotypic traits. The temporal dynamics of watermelon skin texture at different fruiting stages were evaluated using the lacunarity algorithm and stripe ratio, while all extracted features were used for fruit maturity and quality detection. Results showed that the support vector machine (SVM) outperformed others in texture pattern classification, achieving an accuracy of 0.89 and an F1 score of 0.88, highlighting the effectiveness of the lacunarity algorithm in quantifying watermelon skin texture. The extreme gradient boosting (XGBoost) model performed best for maturity level classification, with an accuracy of 0.76 and an F1 score of 0.77. Variable importance evaluation revealed that lacunarity values with scale windows of two and one ranked as the most critical features. For quality detection, central sugar content yielded the most accurate predictions among all quality indicators. The long short-term memory (LSTM) model demonstrated the best performance in predicting central sugar content, achieving an R² value of 0.76, an rRMSE of 0.09, and a MAPE of 7.40 %. This study confirms the feasibility of a quantitative approach to watermelon fruit skin phenotypic analysis and provides valuable insights for advancing non–destructive detection techniques and optimizing breeding strategies.
Why it matches plant phenotyping methodsスイカ果皮の表現型形質を画像処理で定量化する手法を開発・検証し、成熟度・品質推定へ応用しており、表現型取得・抽出法が研究の中心である。
abstractThis study aims to develop a quantitative approach for analyzing watermelon fruit skin phenotypic traits
WatermelonGreenhouseNeRF / 3D Gaussian SplattingRGB / grayscaleLeafWhole plant / canopy / plot / fieldObject detection2D/3D reconstructionSegmentationGrowth / development / phenology
Crop phenotype detection is a precise way to understand and predict the growth of horticultural seedlings in the smart agriculture era to increase the cost-effectiveness and energy efficiency of agricultural production. Crop phenotype detection requires the consideration of plant stature and agricultural devices, like robots and autonomous vehicles, in smart greenhouse ecosystems. However, collecting the imaging dataset is a challenge facing the deep learning detection of plant phenotype given the dynamic changes among leaves and the temporospatial limits of camara sampling. To address this issue, digital cousin is an improvement on digital twins that can be used to create virtual entities of plants through the creation of dynamic 3D structures and plant attributes using RGB image datasets in a simulation environment, using the principles of the variations and interactions of plants in the physical world. Thus, this work presents a two-phase method to obtain the phenotype of horticultural seedling growth. In the first phase, 3D Gaussian splatting is selected to reconstruct and store the 3D model of the plant with 7000 and 30,000 training rounds, enabling the capture of RGB images and the detection of the phenotypes of the seedlings, overcoming temporal and spatial limitations. In the second phase, an improved YOLOv8 model is created to segment and measure the seedlings, and it is modified by adding the LADH, SPPELAN, and Focaler-ECIoU modules. Compared with the original YOLOv8, the precision of our model is 91%, and the loss metric is lower by approximately 0.24. Moreover, a case study of watermelon seedings is examined, and the results of the 3D reconstruction of the seedlings show that our model outperforms classical segmentation algorithms on the main metrics, achieving a 91.0% mAP50 (B) and a 91.3% mAP50 (M).
Why it matches plant phenotyping methods植物の3D再構成、画像取得、セグメンテーション、計測を組み合わせた生育形質抽出手法の開発と評価が中心であり、単なる生物学的実験ではない。
abstractThus, this work presents a two-phase method to obtain the phenotype of horticultural seedling growth.
Downy Mildew, caused by Pseudoperonospora cubensis, poses a serious threat to watermelon (Citrullus lanatus) crops, with the potential to drastically reduce yields and cause substantial economic losses in the agricultural sector. Early and accurate detection is essential for mitigating these impacts and maintaining crop productivity. An automatic detection system was developed using the VGG-16 Convolutional Neural Network (CNN) model, selected for its high accuracy. Trained and validated on a dataset of 585 images, split into 80% for training and 20% for validation, the model achieved 100% accuracy in both phases, with minimal losses of 0.0325 and 0.0098 respectively. These results underscore the model's potential for precise Downy Mildew detection, supporting improved disease management in precision agriculture.
Why it matches plant phenotyping methodsスイカ葉の病徴を画像からCNNで自動分類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として採用。
abstractAn automatic detection system was developed using the VGG-16 Convolutional Neural Network (CNN) model
SoybeanTomatoWatermelonField / plotFruitStomata / guard-cell complexPhysiological trait estimationGrowth / development / phenologyStomatal traitsWater status / transpiration
The combination of flexible electronics and plant science has generated various plant-wearable sensors, yet challenges persist in their applications in real-world agriculture, particularly in high-throughput settings. Overcoming the trade-off between sensing sensitivity and range, adapting them to a wide range of crop types, and bridging the gap between sensor measurements and biological understandings remain the primary obstacles. Here we introduce PlantRing, an innovative, nano-flexible sensing system designed to address the aforementioned challenges. PlantRing employs bio-sourced carbonized silk georgette as the strain sensing material, offering exceptional resolution (tensile deformation: < 100 μm), stretchability (tensile strain up to 100 %), and remarkable durability (season long), exceeding existing plant strain sensors. PlantRing effectively monitors plant growth and water status, by measuring organ circumference dynamics, performing reliably under harsh conditions and being adaptable to a wide range of plants. Applying PlantRing to study fruit cracking in tomato and watermelon reveals novel hydraulic mechanism, characterized by genotype-specific excess sap flow within the plant to fruiting branches. Its high-throughput application enabled large-scale quantification of stomatal sensitivity to soil drought, a traditionally difficult-to-phenotype trait, facilitating drought tolerant germplasm selection. Combing PlantRing with soybean mutant led to the discovery of a potential novel function of the GmLNK2 circadian clock gene in stomatal regulation. More practically, integrating PlantRing into feedback irrigation achieves simultaneous water conservation and quality improvement, signifying a paradigm shift from experience- or environment-based to plant-based feedback control. Collectively, PlantRing represents a groundbreaking tool ready to revolutionize botanical studies, agriculture, and forestry.
Why it matches plant phenotyping methodsPlantRingという高スループットの植物装着型センサーを開発し、器官周径、水状態、気孔感度などの植物形質を直接測定することが研究の中心であるため、植物フェノタイピング手法として含める。
abstractHere we introduce PlantRing, an innovative, nano-flexible sensing system designed to address the aforementioned challenges.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
WatermelonGreenhouseNeRF / 3D Gaussian SplattingRGB / grayscaleLeafWhole plant / canopy / plot / fieldObject detection2D/3D reconstructionSegmentationGrowth / development / phenology
Crop phenotype detection is a precision way to understand and predict the growth of horticul-tural Seedling in smart agriculture era, to make the agricultural production more costly and en-ergy efficiency. And it bridges the plant statues and the agricultural devices, like robots and au-tonomous vehicles in smart greenhouse ecosystem, to know each other well. However, the im-aging data set collection is a neckless of deep learning of phenotype detection, as the dynamic coverings among leaves and time-spatial limits of camara sampling. To address this issue, digital cousin is boosting digital twins and virtual entities of plants, and considered to create dynamical 3D structures, attributes and RGB image data sets in a simulation environment, with the princi-ples of varies and interactions in physical world. Thus, this work presents a two-phase method to obtain the phenotype of horticultural seedling growth. In the first phase, 3D Gaussian Splatting is selected to reconstruct and store the 3D model of the plant, enabling to capture RGB images and detect the phenotypes of seedlings transcending temporal and spatial limitations. In the second phase, an improved the YOLOv8 model is created to segment and measure the seedlings, and it is modified by adding modules of the LADH, SPPELAN and Focaler-ECIOU to the original YOLOv8 model. Moreover, a case study of watermelon seeding is explored, and the results show that 3D Gaussian Splatting has good performance in 3D reconstruction of seedlings, and the peak sig-nal-to-noise ratio (PSNR) of the trained models is generally above 24. As for semantic segmenta-tion, compared with the original YOLOv8, the computation of our model decreased by 7.50%, the convergence speed increased by 31.35%.
Why it matches plant phenotyping methods幼 horticultural seedlings の3D再構成、画像取得、セグメンテーション、表現型測定を統合した手法開発が中心であり、植物表現型の抽出方法を直接扱っている。
abstractthis work presents a two-phase method to obtain the phenotype of horticultural seedling growth.
Plant diseases affect the quality and quantity of agricultural products and have an impact on food safety. These effects result in a loss of income in the production sectors which are particularly critical for developing countries. Visual inspection by subject matter experts is time-consuming, expensive and not scalable for large farms. As such, the automation of plant disease detection is a feasible solution to prevent losses in yield. Nowadays, one of the most popular approaches for this automation is to use drones. Though there are several articles published on the use of drones for plant disease detection, a systematic overview of these studies is lacking. To address this problem, a systematic literature review (SLR) on the use of drones for plant disease detection was undertaken and 38 primary studies were selected to answer research questions related to disease types, drone categories, stakeholders, machine learning tasks, data, techniques to support decision-making, agricultural product types and challenges. It was shown that the most common disease is blight; fungus is the most important pathogen and grape and watermelon are the most studied crops. The most used drone type is the quadcopter and the most applied machine learning task is classification. Color-infrared (CIR) images are the most preferred data used and field images are the main focus. The machine learning algorithm applied most is convolutional neural network (CNN). In addition, the challenges to pave the way for further research were provided.
Why it matches plant phenotyping methods植物病害をドローン画像から検出する手法を対象とした系統的レビューであり、植物の病害状態を推定するフェノタイピング手法のレビューが中心である。
abstracta systematic overview of these studies is lacking. To address this problem, a systematic literature review (SLR) on the use of drones for plant disease detection was undertaken
Assessing crop seed phenotypic traits is essential for breeding innovations and germplasm enhancement. However, the tough outer layers of thin-shelled seeds present significant challenges for traditional methods aimed at the rapid assessment of their internal structures and quality attributes. This study explores the potential of combining terahertz (THz) time-domain spectroscopy and imaging with semantic segmentation models for the rapid and non-destructive examination of these traits. A total of 120 watermelon seed samples from three distinct varieties, were curated in this study, facilitating a comprehensive analysis of both their outer layers and inner kernels. Utilizing a transmission imaging modality, THz spectral images were acquired and subsequently reconstructed employing a correlation coefficient method. Deep learning-based SegNet and DeepLab V3+ models were employed for automatic tissue segmentation. Our research revealed that DeepLab V3+ significantly surpassed SegNet in both speed and accuracy. Specifically, DeepLab V3+ achieved a pixel accuracy of 96.69 % and an intersection over the union of 91.3 % for the outer layer, with the inner kernel results closely following. These results underscore the proficiency of DeepLab V3+ in distinguishing between the seed coat and kernel, thereby furnishing precise phenotypic trait analyses for seeds with thin shells. Moreover, this study accentuates the instrumental role of deep learning technologies in advancing agricultural research and practices.
Why it matches plant phenotyping methodsTHz画像とセマンティックセグメンテーションを用いて種皮・胚乳の組織を自動抽出し、種子形質を評価する手法開発が中心である。
abstractThis study explores the potential of combining terahertz (THz) time-domain spectroscopy and imaging with semantic segmentation models for the rapid and non-destructive examination of these traits.
The improper storage of seeds can potentially compromise agricultural productivity, leading to reduced crop yields. Therefore, assessing seed viability before sowing is of paramount importance. Although numerous techniques exist for evaluating seed conditions, this research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method. The study aimed to determine the most effective classification model for watermelon seeds. Initially, purchased watermelon seeds were segregated into two groups: One underwent sterilization in a dehydrator machine at 40°C for 36 h, whereas the other batch was stored under favorable conditions. Watermelon seeds' spectral images were captured using an HSI with a charge-coupled device camera ranging from 400 to 1000 nm, and the segmented regions of all samples were measured. Preprocessing techniques and wavelength selection methods were applied to manage spectral data workload, followed by the implementation of a support vector machine (SVM) model. The initial hybrid-SVM model achieved a predictive accuracy rate of 100%, with a test set accuracy of 92.33%. Subsequently, an artificial bee colony (ABC) optimization was introduced to enhance model precision. The results indicated that, with kernel parameters (c, g) set at 13.17 and 0.01, respectively, and a runtime of 4.19328 s, the training and evaluation of the dataset achieved an accuracy rate of 100%. Hence, it was practical to utilize HSI technology combined with the PCA-ABC-SVM model to detect different watermelon seeds. As a result, these findings introduce a novel technique for accurately forecasting seed viability, intended for use in agricultural industrial multispectral imaging. PRACTICAL APPLICATION: The traditional methods for determining the condition of seeds primarily emphasize aesthetics, rely on subjective assessment, are time-consuming, and require a lot of labor. On the other hand, HSI technology as green technology was employed to alleviate the aforementioned problems. This work significantly contributes to the field of industrial multispectral imaging by enhancing the capacity to discern various types of seeds and agricultural crop products.
Why it matches plant phenotyping methodsスイカ種子の生存性という植物状態を、ハイパースペクトル画像と機械学習で非破壊推定する手法が研究の中心であり、技術開発・評価に該当する。
abstractthis research leveraged hyperspectral imaging (HSI) technology as an innovative, rapid, clean, and precise nondestructive testing method.
WatermelonFruitLeafGrowth / time-series analysisGrowth / development / phenologyWater status / transpirationYield / yield components
This study utilized a plant-wearable sap flow sensor developed by a multidisciplinary team at Zhejiang University to monitor water distribution patterns in watermelon fruit stalks throughout their developmental stages. The dynamic rules of sap flow at different stages of fruit development were discovered: (1) In the first stage, sap flow into the fruit gradually halts after sunrise due to increased leaf transpiration, followed by a rapid increase post-noon until the next morning, correlating with fruit expansion. (2) In the second stage, the time of inflow sap from noon to night is significantly shortened, while the outflow sap from fruit is observed with the enhancement of leaf transpiration after sunrise, which is consistent with the slow fruit growth at this stage. (3) In the third stage, the sap flow maintains the diurnal pattern. However, the sap flow that inputs the fruit at night is basically equal to the sap flow that outputs the fruit during the day; the fruit phenotype does not change anymore. In addition, a strong correlation between the daily mass growth in fruit and the daily sap flow amount in fruit stalk was identified, validating the sensor’s utility for fruit growth monitoring and yield prediction.
Why it matches plant phenotyping methods植物装着型樹液流センサーによる果実成長・収量関連形質のモニタリングが中心で、果実成長との相関によりセンサーの有用性も検証している。
abstractThis study utilized a plant-wearable sap flow sensor developed by a multidisciplinary team at Zhejiang University to monitor water distribution patterns in watermelon fruit stalks throughout their developmental stages.
Seed standardization is crucial for all seed breeders as it enables differentiation of performance among specific seed varieties. Standardized seed lots outperform impure seed lots in terms of yield and plant population uniformity. Watermelon growers frequently encounter the challenge of uncertain ploidy seed naming, stemming from the mixture of seedless (triploid) and seeded (tetraploid and diploid) seeds. This uncertainty adversely affects farmers' incomes and hinders the development of specialized watermelon seed enterprises. Watermelon seed purity has been traditionally determined by human expertise methods based on seed thickness, weight, and specific gravity. In this study, we used machine vision and deep learning technology to distinguish triploid (3×) watermelon seeds from diploid (2×) and tetraploid (4×) seeds in real-time. A YOLOv5n deep learning model with over 95 % discrimination of seeded seeds from seedless seeds, was developed and applied to an industrial gravity-feed online sorting system. The deep learning model took 5.4 ms to predict and eject every frame containing seeds in the online system, allowing the system to operate at up to 166 frames per second. With the seed vibration hopper frequency set at a constant magnitude of 35 %, the gravity-feed online system can classify and sort seeds according to their ploidy class, achieving an impressive rate of 14.8 kg/hr. These findings demonstrate the potential of deep learning in automation for real-time seed discrimination and sorting in online systems.
Why it matches plant phenotyping methods種子の倍数性という植物状態を機械画像と深層学習で推定・選別する手法およびオンラインプラットフォームが研究の中心であり、単なる生物学的測定ではない。
abstractIn this study, we used machine vision and deep learning technology to distinguish triploid (3×) watermelon seeds from diploid (2×) and tetraploid (4×) seeds in real-time.
Plant diseases pose a significant obstacle to global agricultural productivity, impacting crop quality yield and causing substantial economic losses for farmers. Watermelon, a commonly cultivated succulent vine plant, is rich in hydration and essential nutrients. However, it is susceptible to various diseases due to unfavorable environmental conditions and external factors, leading to compromised quality and substantial financial setbacks. Swift identification and management of crop diseases are imperative to minimize losses, enhance yield, reduce costs, and bolster agricultural output. Conventional disease diagnosis methods are often labor-intensive, time-consuming, ineffective, and prone to subjectivity. As a result, there is a critical need to advance research into machine-based models for disease detection in watermelons. This paper presents a large dataset of watermelons that can be used to train a machine vision-based illness detection model. Images of healthy and diseased watermelons from the Mosaic Virus, Anthracnose, and Downy Mildew Disease are included in the dataset's five separate classifications. Images were painstakingly collected on June 25, 2023, in close cooperation with agricultural experts from the highly regarded Regional Horticulture Research Station in Lebukhali, Patuakhali.
Why it matches plant phenotyping methodsスイカの健全・罹病状態を画像で分類する大規模データセットを提供しており、植物病害状態の画像ベース表現型取得が中心である。
abstractThis paper presents a large dataset of watermelons that can be used to train a machine vision-based illness detection model.
Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding, management, and quality testing. The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient, subjective and destroys samples. Therefore, the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning. The Azure Kinect was used to shoot canopy color images, depth images, and RGB-D images of the watermelon plug seedlings. The Mask-RCNN network was used to classify, segment, and count the canopy leaves of the watermelon plug seedlings. To reduce the error of leaf area measurement caused by mutual occlusion of leaves, the leaves were repaired by CycleGAN, and the depth images were restored by image processing. Then, the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud. The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray. Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height. The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages. The average relative error of measurement is 2.33% for the number of true leaves, 4.59% for the number of cotyledons, 8.37% for the leaf area, and 3.27% for the plant height. The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.
Why it matches plant phenotyping methodsスイカ苗の葉数・葉面積・草丈などの表現型を、RGB-D画像と深層学習・画像処理で非破壊測定する手法の開発が中心である。
abstractthe paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.
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
Remote sensing and machine learning (ML) could assist and support growers, stakeholders, and plant pathologists determine plant diseases resulting from viral, bacterial, and fungal infections. Spectral vegetation indices (VIs) have shown to be helpful for the indirect detection of plant diseases. The purpose of this study was to utilize ML models and identify VIs for the detection of downy mildew (DM) disease in watermelon in several disease severity (DS) stages, including low, medium (levels 1 and 2), high, and very high. Hyperspectral images of leaves were collected in the laboratory by a benchtop system (380-1,000 nm) and in the field by a UAV-based imaging system (380-1,000 nm). Two classification methods, multilayer perceptron (MLP) and decision tree (DT), were implemented to distinguish between healthy and DM-affected plants. The best classification rates were recorded by the MLP method; however, only 62.3% accuracy was observed at low disease severity. The classification accuracy increased when the disease severity increased (e.g., 86-90% for the laboratory analysis and 69-91% for the field analysis). The best wavelengths to differentiate between the DS stages were selected in the band of 531 nm, and 700-900 nm. The most significant VIs for DS detection were the chlorophyll green (Cl green), photochemical reflectance index (PRI), normalized phaeophytinization index (NPQI) for laboratory analysis, and the ratio analysis of reflectance spectral chlorophyll-a, b, and c (RARSa, RASRb, and RARSc) and the Cl green in the field analysis. Spectral VIs and ML could enhance disease detection and monitoring for precision agriculture applications.
Why it matches plant phenotyping methodsスイカの病害重症度という植物状態を、航空・地上ハイパースペクトル画像、植生指数、機械学習で推定・分類する手法が研究の中心であり、精度評価も実施しているため。
abstractThe purpose of this study was to utilize ML models and identify VIs for the detection of downy mildew (DM) disease in watermelon in several disease severity (DS) stages
Watermelon cultivators often encounter various challenges of the varietal mixing of triploid, diploid, and tetraploid seeds, thus hindering the watermelon industry due to the uncertainty in the ploidy seed nomenclature. These circumstances indirectly impose negative effects on the income of farmers and the development of companies specializing in watermelon seeds. Therefore, high seed purity is a necessity for all seed breeders and firms, as the performance of a given seed variety can be standardized. In this study, we employed machine vision techniques to classify triploid watermelon seeds from diploid and tetraploid seeds. The major objective of the research was to illustrate the potential of the discrimination of triploid watermelon seeds with multivariate machine learning classification, and, thereafter, deep learning techniques. Watermelon ploidy seed images were acquired by RGB camera, and discrimination models were constructed with multivariate machine learning methods using one-class classification with the DD-SIMCA and SVM quadratic methods. One-class classification with the DD-SIMCA and the SVM-quadratic models yielded triploid discrimination accuracies of 69.5% and 84.3%, respectively. To further improve the ploidy-class discrimination accuracy, deeplabv3 + and Resnet18 deep learning models produced accuracy of 95.5%. The deep learning model results demonstrated a higher discrimination accuracy, and, thus, these results show the potential for automation and application to online systems for real-time ploidy seed discrimination and sorting.
Why it matches plant phenotyping methodsRGB画像と機械学習・深層学習を用いてスイカ種子の倍数性を識別する手法の開発・評価が研究の中心であり、植物材料の状態を画像から推定しているため。
abstractIn this study, we employed machine vision techniques to classify triploid watermelon seeds from diploid and tetraploid seeds.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Watermelon (Citrullus lanatus) is a widely consumed, nutritious fruit, rich in water and sugars. In most crops, abiotic stresses caused by changes in temperature, moisture, etc., are a significant challenge during production. Due to the temperature sensitivity of watermelon plants, temperatures must be closely monitored and controlled when the crop is cultivated in controlled environments. Studies have found direct responses to these stresses include reductions in leaf size, number of leaves, and plant size. Stress diagnosis based on plant morphological features (e.g., shape, color, and texture) is important for phenomics studies. The purpose of this study is to classify watermelon plants exposed to low-temperature stress conditions from the normal ones using features extracted using image analysis. In addition, an attempt was made to develop a model for estimating the number of leaves and plant age (in weeks) using the extracted features. A model was developed that can classify normal and low-temperature stress watermelon plants with 100% accuracy. The R2, RMSE, and mean absolute difference (MAD) of the predictive model for the number of leaves were 0.94, 0.87, and 0.88, respectively, and the R2 and RMSE of the model for estimating the plant age were 0.92 and 0.29 weeks, respectively. The models developed in this study can be utilized in high-throughput phenotyping systems for growth monitoring and analysis of phenotypic traits during watermelon cultivation.
Why it matches plant phenotyping methods画像解析による低温ストレス分類、葉数・植物齢の推定モデル開発が研究の中心であり、植物形質の抽出手法として明確に該当する。
abstractThe purpose of this study is to classify watermelon plants exposed to low-temperature stress conditions from the normal ones using features extracted using image analysis.
High-throughput detection of plant environmental stresses is required for minimizing the reduction in crop yield. Environmental stresses in plants have primarily been validated by the measurements of photosynthesis with gas exchange and chlorophyll fluorescence, which involve complicated procedures. Remote sensing technologies that monitor leaf reflectance in intact plants enable real-time visualization of plant responses to environmental fluctuations. The photochemical reflectance index (PRI), one of the vegetation indices of spectral leaf reflectance, is related to changes in xanthophyll pigment composition. Xanthophyll dynamics are strongly correlated with plant stress because they contribute to the thermal dissipation of excess energy. However, an accurate assessment of plant stress based on PRI requires correction by baseline PRI (PRI o ) in the dark, which is difficult to obtain in the field. In this study, we propose a method to correct the PRI using NPQ T , which can be measured under light. By this method, we evaluated responses of excess light energy stress under drought in wild watermelon (Citrullus lanatus L.), a xerophyte. Demonstration on the farm, the stress behaviors were observed in maize (Zea mays L.). Furthermore, the stress status of plants and their recovery following re-watering were captured as visual information. These results suggest that the PRI is an excellent indicator of environmental stress and recovery in plants and could be used as a high-throughput stress detection tool in agriculture.
Why it matches plant phenotyping methods光合成反射指数（PRI）を用いた植物ストレス検出法の補正手法を提案し、圃場でストレスと回復を可視化・評価しており、植物フェノタイピング手法が中心である。
abstractIn this study, we propose a method to correct the PRI using NPQ T , which can be measured under light.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
The boom of plant phenotype highlights the need to measure the physiological characteristics of an individual plant. However, continuous real-time monitoring of a plant's internal physiological status remains challenging using traditional silicon-based sensor technology, due to the fundamental mismatch between rigid sensors and soft and curved plant surfaces. Here, the first flexible electronic sensing device is reported that can harmlessly cohabitate with the plant and continuously monitor its stem sap flow, a critical plant physiological characteristic for analyzing plant health, water consumption, and nutrient distribution. Due to a special design and the materials chosen, the realized plant-wearable sensor is thin, soft, lightweight, air/water/light-permeable, and shows excellent biocompatibility, therefore enabling the sap flow detection in a continuous and non-destructive manner. The sensor can serve as a noninvasive, high-throughput, low-cost toolbox, and holds excellent potentials in phenotyping. Furthermore, the real-time investigation on stem flow insides watermelon reveals a previously unknown day/night shift pattern of water allocation between fruit and its adjacent branch, which has not been reported before.
Why it matches plant phenotyping methods植物の茎内樹液流を連続・非破壊測定するウェアラブルセンサーを開発し、植物フェノタイピングへの利用可能性と実植物での性能を示した研究であり、表現型取得法が中心である。
abstractHere, the first flexible electronic sensing device is reported that can harmlessly cohabitate with the plant and continuously monitor its stem sap flow, a critical plant physiological characteristic
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Multispectral imaging is increasingly used in specialty crops, but its benefits in assessment of disease severity and improvements in conventional scouting practice are unknown. Multispectral imaging was conducted using an unmanned aerial vehicle (UAV), and data were analyzed for five flights from Florida and Georgia commercial watermelon fields in 2017. The fields were rated for disease incidence and severity by extension agents and plant pathologists at randomized locations (i.e., conventional scouting) followed by ratings at locations that were identified by differences in normalized difference vegetation index (NDVI) and stress index (i.e., UAV-assisted scouting). Diseases identified by the scouts included gummy stem blight, anthracnose, Fusarium wilt, Phytophthora fruit rot, Alternaria leaf spot, and cucurbit leaf crumple disease. Disease incidence and severity ratings were significantly different between conventional and UAV-assisted scouting (P
Why it matches plant phenotyping methodsUAV multispectral画像とNDVI・ストレス指数を用いてスイカの病害発生・重症度を推定し、従来法と比較評価しているため、植物病害表現型の取得手法が中心です。
abstractMultispectral imaging was conducted using an unmanned aerial vehicle (UAV)
Background and aims Determining seed longevity by identifying chemical changes that precede, and may be linked to, seed mortality, is an important but difficult task. The standard assessment, germination proportion, reveals seed longevity by showing that germination proportion declines, but cannot be used to predict when germination will be significantly compromised. Assessment of molecular integrity, such as RNA integrity, may be more informative about changes in seed health that precede viability loss, and has been shown to be useful in soybean. Methods A collection of seeds stored at 5 °C and 35-50 % relative humidity for 1-30 years was used to test how germination proportion and RNA integrity are affected by storage time. Similarly, a collection of seeds stored at temperatures from -12 to +32 °C for 59 years was used to manipulate ageing rate. RNA integrity was calculated using total RNA extracted from one to five seeds per sample, analysed on an Agilent Bioanalyzer. Results Decreased RNA integrity was usually observed before viability loss. Correlation of RNA integrity with storage time or storage temperature was negative and significant for most species tested. Exceptions were watermelon, for which germination proportion and storage time were poorly correlated, and tomato, which showed electropherogram anomalies that affected RNA integrity number calculation. Temperature dependencies of ageing reactions were not significantly different across species or mode of detection. The overall correlation between germination proportion and RNA integrity, across all experiments, was positive and significant. Conclusions Changes in RNA integrity when ageing is asymptomatic can be used to predict onset of viability decline. RNA integrity appears to be a metric of seed ageing that is broadly applicable across species. Time and molecular mobility of the substrate affect both the progress of seed ageing and loss of RNA integrity.
Why it matches plant phenotyping methodsRNA完全性を種子老化・健康状態の指標として評価し、発芽率との比較や保存条件での検証を行っており、単なる生物学的測定ではなく植物状態の測定法の妥当性検証が中心です。
abstractAssessment of molecular integrity, such as RNA integrity, may be more informative about changes in seed health that precede viability loss
Viability analysis of stored seeds before sowing has a great importance as plant seeds lose their viability when they exposed to long term storage. In this study, the potential of Fourier transform near infrared spectroscopy (FT-NIR) was investigated to discriminate between viable and non-viable triploid watermelon seeds of three different varieties stored for four years (natural aging) in controlled conditions. Because of the thick seed-coat of triploid watermelon seeds, penetration depth of FT-NIR light source was first confirmed to ensure seed embryo spectra can be collected effectively. The collected spectral data were divided into viable and nonviable groups after the viability being confirmed by conducting a standard germination test. The obtained results showed that the developed partial least discriminant analysis (PLS-DA) model had high classification accuracy where the dataset was made after mixing three different varieties of watermelon seeds. Finally, developed model was evaluated with an external data set (collected at different time) of hundred samples selected randomly from three varieties. The results yield a good classification accuracy for both viable (87.7%) and nonviable seeds (82%), thus the developed model can be considered as a "general model" since it can be applied to three different varieties of seeds and data collected at different time.
Why it matches plant phenotyping methodsFT-NIRスペクトルから種子の生存性を判別する方法を開発し、異なる品種・時期の外部データで検証しており、植物表現型の取得・推定が中心である。
abstractthe potential of Fourier transform near infrared spectroscopy (FT-NIR) was investigated to discriminate between viable and non-viable triploid watermelon seeds
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Summary Simplifying sample processing, shortening the sample preparation time, and adjusting procedures to suitable for new health and safety regulations, these issues are the current challenges which electron microscopic examinations need to face. In order to resolve these problems, new plant tissue sample processing protocols for transmission electron microscopy should be developed. In the present study, we chose the LR‐White resin‐assisted processing protocol for the ultrastructural observation of different types of plant tissues. Moreover, we explored Oolong tea extract (OTE) as a substitute for UA in staining ultrathin sections of plant samples. The results revealed that there was no significant difference between the OTE double staining method and the traditional double staining method. Furthermore, in some organelles, such as mitochondria in root cells of tomatoes and chloroplast in leaf cells of watermelons, the OTE double staining method achieved little better results than the traditional double staining method. Therefore, OTE demonstrated good potentials in replacing UA as a counterstain on ultrathin sections. In addition, sample preparation time was significantly shortened and simplified using LR‐White resin. This novel protocol reduced the time for preparing plant samples, and hazardous reagents in traditional method (acetone and UA) were also replaced by less toxic ones (ethanol and OTE).
Why it matches plant phenotyping methods植物組織のTEM観察における試料調製・染色プロトコル自体を開発・比較しており、植物の超微細構造を取得する画像計測法の技術的貢献が中心である。
abstractnew plant tissue sample processing protocols for transmission electron microscopy should be developed
The cucurbit diseases caused by cucumber green mottle mosaic virus (CGMMV) have led to a serious problem to growers and seed producers because it is difficult to prevent spreading through pathogen-infected seeds. Conventional detection methods for infected seeds such as biological, serological, and molecular measurements are not practical for measuring entire samples due to their destructive nature, and time, and cost issues. For this reason, it is necessary to develop a rapid and non-destructive novel technique for detecting seeds infestation. A near-infrared (NIR) hyperspectral imaging system was used to discriminate virus-infected seeds from healthy seeds with partial least square discriminant analysis (PLS-DA) and least square support vector machine (LS-SVM). The classification accuracy for virus-infected watermelon seeds were 83.3% with the best model, demonstrating the potential of NIR hyperspectral imaging for detection of virus-infected watermelon seeds.
Why it matches plant phenotyping methodsNIRハイパースペクトル画像と分類モデルを用いて、スイカ種子のウイルス感染状態を非破壊推定する手法が研究の中心であり、植物の病害状態を直接評価している。
abstractA near-infrared (NIR) hyperspectral imaging system was used to discriminate virus-infected seeds from healthy seeds with partial least square discriminant analysis (PLS-DA) and least square support vector machine (LS-SVM).