LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Why it matches plant phenotyping methods果樹葉のRGBスキャン画像を収録した公開データセットで、植物フェノタイピングおよび葉形態解析を目的とする。標準化された画像取得と再利用可能なデータ構成が中心であり、フェノタイピング用データセットとして適格。
abstractLeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
The core volume ratio (CVR) is a key indicator for evaluating the proportion of edible fraction in stone fruits. Traditionally, CVR is determined through destructive sampling by separately measuring the masses of the core and entire fruit. Recently, low-field nuclear magnetic resonance imaging (LF-NMRI) has been introduced as a non-destructive alternative, but its sparse sampling limits the ability to achieve accurate spatial and volumetric quantification of fruit quality. To address this limitation, we propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits. The method acquires tomographic LF-NMRI sequences along three orthogonal axes. Each sequence is segmented into pulp and core regions using a SwinUNet deep learning model and converted into point clouds for each view. Point clouds from the three orthogonal views are registered via a genetic algorithm to align structural information from complementary perspectives and fused into a unified 3D model through Poisson surface reconstruction. Using prunes as a representative case, the method enables accurate quantification of core and entire fruit volumes, achieving a CVR estimation with a mean absolute error of 0.13% compared to manual measurements. The proposed three-view reconstruction strategy yields a volumetric error of only 0.73%, significantly outperforming single-view (4.57%) and dual-view (3.73%) approaches. This technology provides a robust and accurate non-destructive solution for 3D internal quality analysis of fruits.
Why it matches plant phenotyping methodsLF-NMRI、深層学習セグメンテーション、3D再構成を組み合わせ、果実内部の芯・可食部体積という植物器官形質を非破壊推定する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractwe propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits.
AppleMangoPeachPearPlumField / plotNeRF / 3D Gaussian SplattingRGB / grayscaleFruitWhole plant / canopy / plot / field
FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance Fields We introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards. Our work is based on FruitNeRF [6], which employs a neural semantic field combined with a fruit-specific clusteringapproach. The requirement for adaptation for each fruit type limits the applicability of the method, and makes it difficult to use in practice. To lift this limitation, we design a shape-agnostic multi-fruit counting framework, that complements the RGB and semantic data with instance masks predicted by a vision foundation model. The masks are used to encode the identity of each fruit as instance embeddings into a neural instance field. By volumetrically sampling the neural fields, we extract apoint cloud embedded with the instance features, which can be clustered in a fruit-agnostic manner to obtain the fruit count. We evaluate our approach using a synthetic dataset containing apples, plums, lemons, pears, peaches, and mangoes, as well as a real-world benchmark apple dataset. Our results demonstrate that FruitNeRF++ is easier to control and compares favorably to other state-of-the-art methods.
Why it matches plant phenotyping methods果実を対象とした画像ベースの汎用カウント手法を開発し、合成および実データで評価しているため、植物形質（果実数）の取得・推定が研究の中心です。
abstractWe introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards.
Plums, commonly known as Indian jujube, are economically important, valued for nutritional benefits and consumed by people from all over the world. The development of a comprehensive Plum leaf and fruit dataset is highly essential for advancing agricultural research and enabling effective disease management systems using machine learning techniques. This dataset serves as a foundational resource for machine learning based classification and bridges the gap between agricultural research and computer vision to support automated disease detection and fruit quality assessment. Researchers will be able to utilize this dataset to implement early disease detection which leads to improve crop management and supply quality and reduce the usage of chemicals. Proper utilization of this dataset can help farmers to reduce financial losses and encourage sustainable farming practices. The dataset was collected between December 2024 and February 2025 under various environmental conditions. It consists of 3,554 original images, an equal number of processed images and 18,000 augmented images generated from the original dataset. The dataset is categorized into six distinct classes: Shot Hole, Bacterial Spot, Wilted Leaf, Healthy Leaf, Unhealthy Plum, and Healthy Plum. This dataset contributes significantly to advance deep learning in agriculture enabling early disease detection and fruit quality monitoring.
Why it matches plant phenotyping methods植物の葉・果実画像から病害状態を分類するためのデータセット構築が研究の中心であり、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。
abstractThis dataset contributes significantly to advance deep learning in agriculture enabling early disease detection and fruit quality monitoring.
Reproduction assets foundThe paper is a Data in Brief describing a public Mendeley Data repository containing the authors' own plum leaf/fruit image dataset (3,554 original, processed, and 18,000 augmented images) used for plant disease classification. This is a paper-specific, publicly available image dataset with an explicit URL and DOI.Dataset · publicil Smart City, Birulia, Savar, Dhaka, Bangladesh. (Latitude: 23° 52′ 39.22" N, Longitude: 90° 19′ 12.47" E)
3.
Sharankhola, Bagerhat, Khulna, Bangladesh (Latitude: 22° 13′ 26.0" N, Longitude: 89° 48′ 20.0" E).
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/w7sdx55m7z.1
Direct URL to data: https://data.mendeley.com/datasets/w7sdx55m7z/1
Related research article
none
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Value of the Data
•Open asset ↗Mendeley Data · 10.17632/w7sdx55m7z.1lines:1-52Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Highlights A tree skeleton extraction method based on the fusion of geometric and multiscale features. DBSCAN clustering resolves skeleton omissions and merging in graph algorithms for incomplete point clouds and adjacent branches. Unique breakpoint selection strategy adaptable to various breakage scenarios. Combining local and global features to enhance the accuracy of skeleton breakpoint connections. ABSTRACT. Tree topology reconstruction is essential for applications in precision agriculture, such as canopy structure analysis and yield prediction. However, 3D reconstruction based on RGB images is often affected by noise and data loss, exacerbating the problem of missing branches during skeleton extraction. To address this issue, this paper proposes a tree skeleton extraction method that integrates local and global geometric features. First, DBSCAN is introduced into the graph-based clustering process to segment the incomplete point cloud into multiple clusters, facilitating skeleton extraction in discontinuous regions and enhancing the distinction of spatially adjacent branches. Then, potential connections between skeleton segments are identified based on local branch distance and angular features. Incorrect connections are removed through closed-loop structure recognition and filtering, ensuring accurate skeleton completion. Furthermore, the global growth direction of the tree is incorporated to refine the skeleton structure, followed by Laplacian smoothing to enhance skeleton quality. Experimental validation on point clouds from 13 real plum trees and 200 simulated trees demonstrates the effectiveness of the proposed method, achieving an accuracy of 85.96% for real trees and 88.80% for simulated trees. The results significantly improve the completeness and accuracy of tree structure reconstruction, providing a reliable approach for subsequent tree topology analysis and precision agriculture applications. Keywords: Local-global features, Missing skeleton branches, Skeleton extraction, Topological structure, Tree point clouds.
Why it matches plant phenotyping methodsRGB画像由来の樹木点群から枝・樹冠構造を抽出する計算手法の開発と実データ・シミュレーションによる検証が中心であり、植物形態形質の取得に直接関係する。
abstractthis paper proposes a tree skeleton extraction method that integrates local and global geometric features.
Highlights A method was proposed to simulate and quantify canopy light interception based on structurally accurate 3D branch models of the canopy reconstructed using NeRF technology. Light scattering models, ray tracing algorithms, and the Beer–Lambert law were integrated, with full consideration given to the interactions between light rays and canopy structural units, to realistically simulate light propagation and energy attenuation within the canopy. Introduced two novel metrics, Light Interception Ratio (LIR) and Energy Interception Ratio (EIR), to comprehensively evaluate canopy light interception from quantitative and energetic perspectives. This study provides a tool for simulating and quantifying canopy light interception, offering a valuable reference for canopy pruning. ABSTRACT. The light interception capacity of fruit tree canopies is a critical factor that affects photosynthetic efficiency, yield, and fruit quality. Traditional methods, such as field measurements and radiation transfer models, are widely used but often lack high spatial resolution data and are prone to significant errors in complex conditions. To address this, we proposed a 3D reconstruction-based method to simulate and quantify canopy light interception by modeling spatial light distribution within the canopy. First, NeRF technology was used to reconstruct high-resolution 3D models of 12 plum trees, accurately capturing their complex canopy structures. Second, a canopy light model was developed and combined with ray tracing algorithms to simulate light propagation within the canopy and its interactions with branches and leaves. Finally, two core metrics, the Light Interception Ratio (LIR) and Energy Interception Ratio (EIR), were proposed to quantitatively evaluate canopy light interception capacity from the perspectives of quantity and energy. Using this method, we analyzed the differences in light interception capacities among 12 canopies with varying morphologies and evaluated the impact of different pruning strategies on canopy light interception. The results indicate that canopy density, branch and leaf distribution, and morphological characteristics significantly influence light interception capacity, with open shapes and evenly distributed canopies demonstrating superior performance. For instance, an open-canopy structure with evenly spaced foliage achieved an LIR of 33.48% and an EIR of 7.13%, compared to 32.81% and 6.58% in a denser, more compact canopy. This study offers an intuitive tool to simulate and quantify canopy light interception efficiency, providing scientific insights for pruning and optimal canopy design. Keywords: 3D reconstruction, Canopy light interception, Light distribution simulation, Orchard management optimization, Ray tracing.
Why it matches plant phenotyping methods3D再構成と光線追跡を統合し、果樹キャノピーの光 interception を定量化する手法と指標を開発・適用しており、植物状態の取得・推定が研究の中心である。
abstracttwo core metrics, the Light Interception Ratio (LIR) and Energy Interception Ratio (EIR), were proposed to quantitatively evaluate canopy light interception capacity
Purpose Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset. Design/methodology/approach The proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one. Findings These proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India. Originality/value The proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.
Why it matches plant phenotyping methodsドローン画像から植物の健康状態・病害を推定する画像解析システムの開発が研究の中心であり、特徴抽出、分類、データセット構築と性能評価を含むため。
abstracta light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset.
AppleCitrusMangoPeachPearPlumField / plotLiDAR / point cloudRGB / grayscaleFruit
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.
Why it matches plant phenotyping methods果実を対象に、画像・NeRF・点群クラスタリングを組み合わせて3D果実数を推定する手法を開発し、実データおよび合成データで評価しているため、植物表現型取得法が中心である。
abstractWe introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D.
Reproduction assets foundThe paper's real-world apple tree image dataset with manual ground-truth counts and synthetic Blender fruit tree data are publicly released via the project website, and the FruitNeRF analysis code is open-source on GitHub. The Zenodo DOI refers to the third-party BlenderNeRF plugin (cited tool), not a paper-specific.Dataset · publicThe data has been made publicly available, and visualizations can be accessed on the project website.Open asset ↗lines:183-221Code · publicFruitNeRF code: https://github.com/meyerls/FruitNeRF has been made open-source.Open asset ↗meyerls/FruitNeRFlines:74-108Code / dataset availability confirmedbioRxiv · checked 14 Sept 2026
The evolutionary histories of species have been shaped by genomic, environmental, and morphological variation. Understanding the interactions among these sources of variation is critical to infer accurately the biogeographic history of lineages. Here, using the geographically widely distributed plum genus (Prunus, Rosaceae) as a model, we investigate how changes in genomic and environmental variation drove the diversification of this group, and we quantify the morphological features that facilitated or resulted from diversification. We sequenced 587 nuclear loci and complete chloroplast genomes from 99 species representing all major lineages in Prunus, with a special focus on the understudied tropical racemose group. The environmental variation in extant species was quantified by synthesizing bioclimatic variables into principal components of environmental variation using thousands of georeferenced herbarium specimens. We used machine learning algorithms to classify and measure morphological variation present in thousands of digitized herbarium sheet images. Our phylogenomic and biogeographic analyses revealed that ancient hybridization and/or allopolyploidy spurred the initial rapid diversification of the genus in the early Eocene, with subsequent diversification in the north temperate zone, neotropics, and paleotropics. This diversification involved successful transitions between tropical and temperate biomes, an exceedingly rare event in woody plant lineages, accompanied by morphological changes in leaf and reproductive morphology. The machine learning approach detected morphological variation associated with ancient hybridization and quantified the breadth of morphospace occupied by major lineages within the genus. The paleotropical lineages of Prunus have diversified steadily since the late Eocene/early Oligocene, while the neotropical lineages diversified much later. Critically, both the tropical and temperate lineages have continued to diversify. We conclude that the genomic rearrangements created by reticulation deep in the phylogeny of Prunus may explain why this group has been more successful than other groups with tropical origins that currently persist only in either tropical or temperate regions, but not both.
Why it matches plant phenotyping methods機械学習を用いて標本画像から植物の形態変異を分類・測定し、葉および生殖形態の表現型を定量化しており、形態計測ワークフローが研究の中心的要素である。
abstractWe used machine learning algorithms to classify and measure morphological variation present in thousands of digitized herbarium sheet images.
Reproduction assets foundThe paper's herbarium image data and supplementary material are deposited in Dryad; trained Prunus ML classifiers are on Hugging Face; author Jupyter notebooks for the ML workflow are on GitHub. All are paper-specific, public, and actionable.Dataset · public1126 Data Availability
1127
1128 Data available from the Dryad Digital Repository: DOI: 10.5061/dryad.x95x69pwr; Reviewer
1129 URL: http://datadryad.org/share/L-QUcxrgnpTr6l0Td3MxdfJDCUT1iRbPmT4SzA2LwMA.
1130
1131 Supplementary material, image data, and DNA sequence matrices are available from the Dryad
1132 Digital Repository. Raw sequence data were submitted to NCBI GenBank (SUB13638423).
1133 Machine learning models are hosted on Hugging Face with temporary URLs
1134 (https://huggingface.co/richiehodel/Prunus_lineage_classiOpen asset ↗Dryad Digital Repository · 10.5061/dryad.x95x69pwrpdf-layout-page:54 lines:1-31Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
PlumRaman / spectroscopyFruitClassificationGrowth / development / phenology
Harvested plums can be stored at low temperatures for a long time. During storage, several quality attributes of plums undergo changes, which are also associated with their best‐before date. This research proposed a fast method for the quick determination of plum storage time. With the integration of near‐infrared spectra measurements, plum quality parameters (firmness, soluble solids content, pH, and color), several classification models have been developed to determine the storage time. A variety of methods such as linear discriminant analysis (LDA), partial least squares (PLS), support vector machine (SVM), and generalized linear models (GLM) were used. It is concluded that the models based on near‐infrared spectra only with 14 selected features using LDA and PLS provide the best estimations, with accuracy values of 0.9917 and 0.9854, respectively. The models built with the attributes provide favorable results when using LDA and SVM (Radial basis), with accuracy values of 0.9261 and 0.8734, respectively. NOVELTY IMPACT STATEMENT: For the first time, we proposed the use of non‐destructive NIRS to establish classification models in plums based on post‐harvest storage time for maturity prediction. At the same time, several fruit quality attributes were also used for comparison.
Why it matches plant phenotyping methods収穫果実の貯蔵期間・成熟状態をNIRSと分類モデルで非破壊推定する手法が研究の中心であり、果実品質形質の取得・推定を技術的に評価している。
abstractThis research proposed a fast method for the quick determination of plum storage time.
Plant leaf area (LA) is a key metric in plant monitoring programs. Machine learning methods were used in this study to estimate the LA of four plum genotypes, including three greengage genotypes (Prunus domestica [subsp. italica var. claudiana.]) and a single myrobalan plum (prunus ceracifera), using leaf length (L) and width (W) values. To develop reliable models, 5548 leaves were subjected to experiments in two different years, 2019 and 2021. Image processing technique was used to extract dimensional leaf features, which were then fed into Linear Multivariate Regression (LMR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Model evaluation on 2019 data revealed that the LMR structure LA = 0.007+0.687 L×W was the most accurate among the various LMR structures, with R2 = 0.9955 and Root Mean Squared Error (RMSE) = 0.404. In this case, the linear kernel-based SVR yielded an R2 of 0.9955 and an RMSE of 0.4871. The ANN (R2 = 0.9969; RMSE = 0.3420) and ANFIS (R2 = 0.9971; RMSE = 0.3240) models demonstrated greater accuracy than the LMR and SVR models. Evaluating the models mentioned above on data from various genotypes in 2021 proved their applicability for estimating LA with high accuracy in subsequent years. In another research segment, LA prediction models were developed using data from 2021, and evaluations demonstrated the superior performance of ANN and ANFIS compared to LMR and SVR models. ANFIS, ANN, LMR, and SVR exhibited R2 values of 0.9971, 0.9969, 0.9950, and 0.9948, respectively. It was concluded that by combining image analysis and modeling through ANFIS, a highly accurate smart non-destructive LA measurement system could be developed.
Why it matches plant phenotyping methods画像処理で葉の寸法特徴を抽出し、機械学習モデルで葉面積を非破壊推定する手法の開発・検証が中心であるため、植物フェノタイピング方法論として含める。
abstractImage processing technique was used to extract dimensional leaf features, which were then fed into Linear Multivariate Regression (LMR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
PlumMultispectral / hyperspectralLeafRootPhysiological trait estimationWater status / transpiration
The leaf equivalent water thickness (EWT, g cm −2 ) and fuel moisture content (FMC, %) are key variables in ecological and environmental monitoring. Although a variety of hyperspectral vegetation indices have been developed to estimate the leaf EWT and FMC, most of these indices are defined considered two or three specific bands for a specific plant species, which limits their applicability. In this study, we proposed three new spectral absorption indices (SAI 970 , SAI 1200 , and SAI 1660 ) for various plant types by considering the symmetry of the spectral absorption at 970 nm, 1200 nm and 1660 nm and spectral heterogeneity of different leaves. The indices were calculated considering the absorption peak and shoulder bands of each leaf instead of the same specific bands for all leaves. A pooled dataset of three tree species (camphor (VX), capricorn (VJ), and red-leaf plum (VL)) was used to test the performance of the SAIs in terms of the leaf EWT and FMC estimation. The results indicated that, first, SAI 1200 was more suitable for estimating the EWT than FMC, whereas SAI 970 and SAI 1660 were more suitable for estimating the FMC. Second, SAI 1200 achieved the most accurate estimation of the EWT with a cross-validation coefficient of determination ( R cv 2 ) of 0.845 and relative cross-validation root mean square error ( rRMSE cv ) of 8.90%. Third, SAI 1660 outperformed the other indices in estimating the FMC at the leaf level, with an R cv 2 of 0.637 and rRMSE cv of 8.56%. Fourth, SAI 970 achieved a moderate accuracy in estimating the EWT ( R cv 2 of 0.25 and rRMSE cv of 19.68%) and FMC ( R cv 2 of 0.275 and rRMSE cv of 12.10%) at the leaf level. These results can enrich the application of the SAIs and demonstrate the potential of using SAI 1200 to determine the leaf EWT and SAI 1660 to obtain the leaf FMC among various plant types.
Why it matches plant phenotyping methods葉のハイパースペクトルデータからEWTとFMCという植物生理形質を推定する新規スペクトル指標を開発し、交差検証で性能評価しており、表現型取得・推定手法が中心である。
abstractIn this study, we proposed three new spectral absorption indices (SAI 970 , SAI 1200 , and SAI 1660 )
One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.
Why it matches plant phenotyping methodsリモートセンシングによる植物形質・蒸発散の推定が研究の中心で、熱・マルチスペクトル画像、フォトグラメトリ、モデルを用いた推定精度も評価している。
abstractIn recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks.
Reproduction assets foundThe paper's data availability statement points to the author's public GitHub profile (Héctor Nieto, pyTSEB developer) as the location of the datasets analyzed, which include the remote sensing phenotyping measurements (thermal/multispectral imagery-derived ETa, LAI, fiPAR, Ψstem relationships) and the TSEB-based model.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hectornieto .Open asset ↗hectornietolines:1046-1107Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices.
Why it matches plant phenotyping methods圃場画像からプラムの健全・罹病状態を推定するCNN手法を開発し、データ収集、頑健性向上、量子化、モバイル実装、精度評価まで扱っており、病害表現型の取得・判定が中心である。
abstractwe propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices.
Bacterial canker is a major disease of Prunus avium (cherry), Prunus domestica (plum) and other stone fruits. It is caused by pathovars within the Pseudomonas syringae species complex including P. syringae pv. morsprunorum (Psm) race 1 (R1), Psm race 2 (R2) and P. syringae pv. syringae (Pss). Psm R1 and Psm R2 were originally designated as the same pathovar; however, phylogenetic analysis revealed them to be distantly related, falling into phylogroups 3 and 1, respectively. This study characterized the pathogenicity of 18 newly genome‐sequenced P. syringae strains on cherry and plum, in the field and laboratory. The field experiment confirmed that the cherry cultivar Merton Glory exhibited a broad resistance to all clades. Psm R1 contained strains with differential specificity on cherry and plum. The ability of tractable laboratory‐based assays to reproduce assessments on whole trees was examined. Good correlations were achieved with assays using cut shoots or leaves, although only the cut shoot assay was able to reliably discriminate cultivar differences seen in the field. Measuring bacterial multiplication in detached leaves differentiated pathogens from nonpathogens and was therefore suitable for routine testing. In cherry leaves, symptom appearance discriminated Psm races from nonpathogens, which triggered a hypersensitive reaction. Pathogenic strains of Pss rapidly induced disease lesions in all tissues and exhibited a more necrotrophic lifestyle than hemibiotrophic Psm. This in‐depth study of pathogenic interactions, identification of host resistance and optimization of laboratory assays provides a framework for future genetic dissection of host–pathogen interactions in the canker disease.
Why it matches plant phenotyping methods病害症状と病原性を対象に、圃場評価と切枝・葉 assay の相関、培養・測定法の識別性能を検証し、植物病害表現型の取得法を最適化しているため、単なる病理実験を超えて方法検証が中心的です。
abstractThe ability of tractable laboratory‐based assays to reproduce assessments on whole trees was examined.
Maturity is the key factor which determines the storage life and ripening quality of fruits. In order to provide marketing flexibility and to guarantee the acceptable eating quality to the buyer it is very critical to determine the right maturity stage. Maturity indices are also important for trade regulation, marketing strategy and for the efficient use of labor and resources. The proposed system is based on implementation of image processing techniques on the JPEG images of different maturity stages of the plum variety 'Satluj Purple' grown under sub-tropical conditions. The external quality features like color, texture and size were analyzed. Color feature was extracted by using mean RGB values. Entropy, Local Binary Pattern and Discrete Cosine transformation were used for extracting textural features. Correlation coefficients between images of various categories were recorded to determine the most dominant factor for classification. Multi-Attribute Decision Making theory was used for taking final decision. The developed system accurately determined the maturity level. Color was found to be the most dominant factor for classifying the plums according to maturity level. The error percentage was less than 2.4%, when the length and width computed from application were compared with the manual readings. When RGB indices of fruit images were correlated with chemical properties of fruits, strong association was found between fruit acidity and mean intensity of green color (R 2 = 0.9966). Significant variability in total soluble solids was also explained by variation in R/G ratio (R 2 = 0.8464).
Why it matches plant phenotyping methodsスモモ果実の成熟度という植物器官の状態を、画像処理で色・テクスチャ・サイズから推定する手法を開発し、手動測定および化学特性との関連で検証しており、表現型取得が研究の中心である。
abstractThe proposed system is based on implementation of image processing techniques on the JPEG images of different maturity stages of the plum variety 'Satluj Purple'
Bacterial canker is a major disease of Prunus avium (cherry), Prunus domestica (plum) and other stone fruits. It is caused by pathovars within the Pseudomonas syringae species complex including P. syringae pv. morsprunorum (Psm) race 1 (R1), Psm race 2 (R2) and P. syringae pv. syringae (Pss). Psm R1 and Psm R2 were originally designated as the same pathovar; however, phylogenetic analysis revealed them to be distantly related, falling into phylogroups 3 and 1, respectively. This study characterized the pathogenicity of 18 newly genome-sequenced P. syringae strains on cherry and plum, in the field and laboratory. The field experiment confirmed that the cherry cultivar Merton Glory exhibited a broad resistance to all clades. Psm R1 contained strains with differential specificity on cherry and plum. The ability of tractable laboratory-based assays to reproduce assessments on whole trees was examined. Good correlations were achieved with assays using cut shoots or leaves, although only the cut shoot assay was able to reliably discriminate cultivar differences seen in the field. Measuring bacterial multiplication in detached leaves differentiated pathogens from nonpathogens and was therefore suitable for routine testing. In cherry leaves, symptom appearance discriminated Psm races from nonpathogens, which triggered a hypersensitive reaction. Pathogenic strains of Pss rapidly induced disease lesions in all tissues and exhibited a more necrotrophic lifestyle than hemibiotrophic Psm. This in-depth study of pathogenic interactions, identification of host resistance and optimization of laboratory assays provides a framework for future genetic dissection of host-pathogen interactions in the canker disease.
Why it matches plant phenotyping methods樹体圃場評価と切枝・葉アッセイを比較し、病徴や病原体増殖による植物病害状態の測定法を検証・最適化しており、病原性研究に加えて表現型取得法が実質的な中心となっている。
abstractThe ability of tractable laboratory-based assays to reproduce assessments on whole trees was examined.
Bacterial canker is a major disease of cherry and other stone fruits caused by several pathovars of Pseudomonas syringae . These are P.s pv. morsprunorum race 1 (Psm R1), P.s pv. morsprunorum race 2 (Psm R2) and P.s pv. syringae (Pss). Psm R1 and R2 were originally designated as races of the same pathovar, however phylogenetic analysis has revealed them to be distantly related. This study characterised the pathogenicity of P. syringae on cherry and plum, in the field and the laboratory. The field experiment identified variation in host cultivar susceptibility to the different pathogen clades. The cherry cultivar Merton Glory exhibited a broad resistance to all clades, whilst cultivar Van showed race-specific resistance. Psm R1 may be divided into a race structure with some strains pathogenic to both cherry and plum and others only pathogenic to plum. The results of laboratory-based pathogenicity tests were compared to results obtained on whole-trees. Only cut shoot inoculations were found to be sensitive enough to detect cultivar variation in susceptibility. Measuring population growth of bacteria in detached leaves reliably discriminated pathogens from non-pathogens. In addition, symptom appearance discriminated Psm races from non-pathogens which triggered a rapid hypersensitive response (HR). The pathogen Pss rapidly induced disease lesions and therefore may exhibit a more necrotrophic lifestyle than hemi-biotrophic Psm races. This in-depth study of pathogenic interactions, identification of host resistance and optimisation of laboratory assays, will provide a framework for future genetic dissection of virulence and host resistance mechanisms.
Why it matches plant phenotyping methods植物病害の感受性・症状を評価する病原性試験を比較し、圃場・実験室アッセイの感度と識別性能を検証・最適化しており、病害表現型の取得法が中心的です。
abstractThe results of laboratory-based pathogenicity tests were compared to results obtained on whole-trees.
Weather conditions can affect sensors' readings when sampling outdoors. Although sensors are usually set up covering a wide range of conditions, their operational range must be established. In recent years, depth cameras have been shown as a promising tool for plant phenotyping and other related uses. However, the use of these devices is still challenged by prevailing field conditions. Although the influence of lighting conditions on the performance of these cameras has already been established, the effect of wind is still unknown. This study establishes the associated errors when modeling some tree characteristics at different wind speeds. A system using a Kinect v2 sensor and a custom software was tested from null wind speed up to 10 m·s -1 . Two tree species with contrasting architecture, poplars and plums, were used as model plants. The results showed different responses depending on tree species and wind speed. Estimations of Leaf Area (LA) and tree volume were generally more consistent at high wind speeds in plum trees. Poplars were particularly affected by wind speeds higher than 5 m·s -1 . On the contrary, height measurements were more consistent for poplars than for plum trees. These results show that the use of depth cameras for tree characterization must take into consideration wind conditions in the field. In general, 5 m·s -1 (18 km·h -1 ) could be established as a conservative limit for good estimations.
Why it matches plant phenotyping methodsKinect RGB-Dによる樹木形質推定を風速条件下で評価し、測定誤差と運用限界を検証しているため、植物フェノタイピング手法が中心である。
abstractThis study establishes the associated errors when modeling some tree characteristics at different wind speeds.