Accurate identification of crop varieties is essential for plant breeding programs and the protection of Plant Breeders' Rights (PBR), yet traditional morphological assessment methods remain subjective and time-consuming, particularly for species with complex morphological diversity such as Rubus crataegifolius . This study demonstrates that geometric morphometric techniques provide an objective, quantitative complementary approach for distinguishing Korean raspberry varieties, addressing the limitations of subjective visual assessment while remaining compatible with molecular marker analysis. We employed three complementary morphometric approaches: landmark-based analysis (19 anatomical points capturing vein junctions and leaf margins), Elliptic Fourier Descriptors (EFD) for outline contours, and a hybrid landmark-EFD dataset. Using these approaches, we analyzed primocane and floricane leaves from 10 accessions of R. crataegifolius comprising 8 varieties and 2 landraces and performed principal component analysis (PCA) and linear discriminant analysis (LDA) with leave-one-out cross-validation. As a result, among the three morphometric approaches applied to primocane and floricane leaves, landmark-based analysis of primocane leaves achieved the highest classification accuracy (87.2%), with an overall average accuracy of 72.0% (range: 51.1-87.2%) across all six analytical combinations. LDA visualization suggested the presence of four major morphological groups, and primocane leaves exhibited higher discriminatory power than floricane leaves, which may reflect greater morphological uniformity under normal growing conditions. Landmark analysis effectively detected subtle differences in leaf venation and leaflet architecture that are difficult to distinguish visually, highlighting the capacity of morphometrics for objective and multidimensional morphological analysis. These findings suggest that morphometric analysis provides a practical and cost-effective preliminary screening tool, complementary to molecular approaches, for supporting Distinctness, Uniformity, and Stability (DUS) examination in raspberry variety evaluation. This approach shows strong potential for offering a scalable solution for variety registration and protection and supporting sustainable horticultural development.
Why it matches plant phenotyping methods葉の形態を幾何学的モルフォメトリクスで定量化し、品種識別とDUS評価に応用した研究で、表現型の取得・解析手法が中心である。
abstractThis study demonstrates that geometric morphometric techniques provide an objective, quantitative complementary approach for distinguishing Korean raspberry varieties
RaspberryField / plotWhole plant / canopy / plot / field
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsラズベリーの生育・フェノロジー段階を対象とする画像データセットとYOLOv8ベンチマークであり、植物状態の取得・推定手法が中心です。
titleRaspberryJalisco: A field-collected multi-class dataset of Raspberry (Rubus idaeus) phenological stages from Mexican commercial orchards with YOLOv8 benchmarks
MaizeRaspberryTomatoThermalLeafSegmentationStress / disease detectionStress response / tolerancePlant / canopy temperature
Plant stress in California has become a significant issue in recent years due to a combination of drought, malnutrition, and infections. There is an urgent need to develop a cost-effective, time-efficient, and reliable method to address this issue. This paper aims to develop a method of using infrared thermal imaging techniques to detect stress in plant leaves. The design of experiment (DOE) is divided into two phases. Phase one involved selecting three types of plants that represent significant stress factors in California - drought, infections, and malnutrition. The plants selected were raspberry, cherries, corn tomato, eggplant, and oleander. For each type of plant, three areas were chosen that each represented a stage of the plant’s stress: no stress (healthy), early stress, and fully stressed. Twenty points of surface thermal temperature were taken from each area of the plant leaf, and t-tests were conducted to calculate the p-value. The experiment indicates that thermal imaging techniques can be used for early detection in raspberry (drought and malnutrition) (p< 0.0001), cherries (drought) (p= 0.2996), corn (drought) (p< 0.0001), tomato (infections) (p< 0.0001), and eggplant (infections) (p< 0.0001), oleander (infections) (p< 0.0001). Phase two focused on developing a method to monitor the progressive development of plant stress throughout the entire drought process. A corresponding thermal model was built to understand the stress mechanism better for management of irrigation scheduling and plant phenotyping. The plants chosen were gardenia, tomato, and cucumber. In addition to recognizing thermal patterns throughout the process, an image processor was also created by code to calculate the percentage of healthy versus diseased area of the plant. The immediate application for this research is that it offers an advanced, non-invasive method for early detection of various plant stressors. This provides an effective solution for farmers to combat climate change and reduce plant losses due to infections, promoting much more sustainable agriculture.
Why it matches plant phenotyping methods植物葉のストレス状態を赤外線熱画像で検出し、さらに画像処理で健全・罹病領域を定量化する手法の開発が主題であり、植物フェノタイピング手法が中心である。
abstractThis paper aims to develop a method of using infrared thermal imaging techniques to detect stress in plant leaves.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
The application of direct-injection mass spectrometric (DI-MS) techniques, like Proton Transfer Reaction Time of Flight Mass Spectrometry (PTR-ToF-MS) has been suggested as a reliable phenotyping tool for fruit volatilome assessment in both genetic and quality-related studies. In this study the complexity of raspberry aroma was investigated by a comprehensive untargeted VOC analysis, done by combining SPME-GC-MS and PTR-ToF-MS assessments with multi-block discriminant analysis using the DIABLO mixOmics framework. The aim was to acquire an exhaustive characterization of the raspberry volatilome according to different fruit ripening stages (pink, ripe, and overripe) and genetic variances (50 accessions), as well as to investigate the potential of PTR-ToF-MS as a rapid and high throughput VOC phenotyping tool to address issues related to raspberry fruit quality. Results of this study demonstrated the complementarity between SPME-GC-MS and PTR-ToF-MS techniques to evaluate the raspberry aroma composition. PTR-ToF-MS generates reliable raspberry VOC fingerprints mainly due to a reduced compound fragmentation and precise content estimation. In addition, the high collinearity between isomers of monoterpenes and norisoprenoids, discovered by GC analysis, reduces the main analytic limitation of PTR-ToF-MS of not being able to separate isomeric molecules. The high similarity between the VOC matrices obtained by applying PTR-ToF-MS and SPME-GC-MS confirmed the possibility of using PTR-ToF-MS as a reliable high throughput phenotyping tool for raspberry volatiolome assessment. In addition, results provided by the germplasm collection investigation enabled to distinguish the best performing accessions, based on VOCs composition, to be used as superior parental lines for future breeding programs.
Why it matches plant phenotyping methodsラズベリー果実の揮発性成分を植物表現型として取得するPTR-ToF-MSを、GC-MSと比較・検証し、高スループット表現型解析ツールとして評価しているため、方法論が中心的である。
abstractThe application of direct-injection mass spectrometric (DI-MS) techniques, like Proton Transfer Reaction Time of Flight Mass Spectrometry (PTR-ToF-MS) has been suggested as a reliable phenotyping tool for fruit volatilome assessment
Monitoring plant responses to stress is an ongoing challenge for crop breeders, growers, and agronomists. The measurement of below-ground stress is particularly challenging as plants do not always show visible signs of stress in the above-ground organs, particularly at early stages. Hyperspectral imaging is a technique that could be used to overcome this challenge if associations between plant spectral data and specific stresses can be determined. In this study, three genotypes of red raspberry plants grown under controlled conditions in a glasshouse were subjected to below-ground biotic stresses (root pathogen Phytophthora rubi and root herbivore Otiorhynchus sulcatus ) or abiotic stress (soil water availability) and regularly imaged using hyperspectral cameras over this period. Significant differences were observed in plant biophysical traits (canopy height and leaf dry mass) and canopy reflectance spectrum between the three genotypes and the imposed stress treatments. The ratio of reflectance at 469 and 523 nm showed a significant genotype-by-treatment interaction driven by differential genotypic responses to the P. rubi treatment. This indicates that spectral imaging can be used to identify variable plant stress responses in raspberry plants.
Why it matches plant phenotyping methodsハイパースペクトル画像を用いて植物のストレス応答を推定し、反射スペクトル指標と植物形質の差異を評価することが研究の中心であるため、植物フェノタイピング手法の実質的応用に該当する。
abstractHyperspectral imaging is a technique that could be used to overcome this challenge if associations between plant spectral data and specific stresses can be determined.
Monitoring plant responses to stress is an ongoing challenge for crop breeders, growers and agronomists. The measurement of below ground stress is particularly challenging as plants do not always show visible signs of stress in the above ground organs, particularly at early stages. Hyperspectral imaging is a technique that could be used to overcome this challenge if associations between plant spectral data and specific stresses can be determined. In this study, three genotypes of red raspberry plants grown under controlled conditions in a glasshouse were subjected to below ground biotic stresses (root pathogen Phytophthora rubi and root herbivore Otiorhynchus sulcatus ) or abiotic stress (soil water availability) and regularly imaged using hyperspectral cameras over this period. Significant differences were observed in plant biophysical traits (canopy height and leaf dry mass) and canopy reflectance spectrum between the three genotypes and the imposed stress treatments. The ratio of reflectance at 469nm and 523nm showed a significant genotype-by-treatment interaction driven by differential genotypic responses to the Phytophthora rubi treatment. This indicates that spectral imaging can be used to identify variable plant stress responses in raspberry plants.
Why it matches plant phenotyping methodsラズベリーのストレス応答をハイパースペクトル画像から推定する手法を中心に評価しており、スペクトル指標と植物形質・ストレス処理の関連を検証している。
abstractHyperspectral imaging is a technique that could be used to overcome this challenge if associations between plant spectral data and specific stresses can be determined.
Why it matches plant phenotyping methods生果実の品質形質（糖度・アントシアニン）をNIRで非破壊推定する校正・検証が研究の中心であり、植物器官の形質計測法に該当する。
abstractThe present work is the first calibration and validation of near infra‐red spectroscopy (NIRS) for instantaneous and simultaneous of prediction of raspberry quality parameters.
Physiological and physical traits are excellent indicators of many crop characteristics, but precise phenotyping of these traits is time consuming and, therefore, limits progress in crop breeding and the speed of crop monitoring. Hyperspectral imaging offers an opportunity to overcome these barriers as a technique for high throughput field measurements. Using a recently developed hyperspectral imaging platform devised for plantations of the perennial crop raspberry, this study aimed to further develop the tool and test its capacity as an innovative approach for high throughput field phenotyping, data collection and analysis. Hyperspectral imaging and visual crop assessments were carried out over two growing seasons in a field-grown raspberry mapping population, and data were subject to Quantitative Trait Loci (QTL) analysis. The findings show that reflectance intensity at multiple wavelengths can be linked to known genetic markers in raspberry, and many of these 'spectral traits' are expressed consistently through the growing season and between years, for example spectral ratio 719 nm / 691 nm shows up consistently as a QTL on LG4. Spectral traits were identified that co-located with previously mapped physical traits, such as 719 nm / 691 nm and cane density. The study indicates that hyperspectral imaging can be used as an innovative approach for high throughput field phenotyping of raspberry and could be transferred readily to other perennial crops. Our approach provides a pipeline for automated field data collection and analysis that can be used for rapid QTL detection of spectral traits.
Why it matches plant phenotyping methodsラズベリー向け hyperspectral imaging プラットフォームを改良し、高スループット圃場フェノタイピングと自動データ解析パイプラインを評価・適用しており、表現型取得手法が研究の中心です。
abstractthis study aimed to further develop the tool and test its capacity as an innovative approach for high throughput field phenotyping, data collection and analysis.
Why it matches plant phenotyping methods生鮮ラズベリー果実の品質形質（アントシアニンと可溶性固形分）をNIRで非破壊推定する較正・検証が研究の中心であり、植物器官の形質測定法に該当する。
abstractThe present work is the first calibration and validation of near infra-red spectroscopy (NIRS) for instantaneous and simultaneous of prediction of raspberry quality parameters.
Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400-2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r 2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved.
Why it matches plant phenotyping methods圃場ハイパースペクトル画像からラズベリー個体を自動分割・分離し、草丈や密度を推定する手法を開発・検証しており、植物表現型取得が研究の中心である。
abstractHere we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection.