The genetic identity of coffee cultivars is fundamental to the specialty coffee sector, where premium prices are paid under the assumption that the purchased planting material corresponds to the declared variety. However, many producing countries lack the certification infrastructure necessary to guarantee this identity in their informal seed systems, exposing producers to undetected varietal non-conformity. In this study, we examine a case from a specialty coffee ( Coffea arabica L.) farm in southern Ecuador where seeds labeled as Sidra (USD 100/kg) and Gesha (USD 500/kg) were purchased without genetic or phytosanitary certification. Using a combination of SSR-based DNA fingerprinting and quantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics, we documented varietal identity and assessed the discriminant capacity of morphological traits across the four resulting morphotypes. Using eleven microsatellite markers for SSR fingerprinting, we found that two of the four morphotypes did not match their declared commercial identity. One plant sold as Sidra was identified as compatible with Batian, a composite variety of Kenyan origin that is genetically unrelated to Ethiopian landraces. The plants acquired as Gesha corresponded to a pure Ethiopian landrace that is genetically similar to, but not identical to, the Panamanian Geisha reference accession T.02722. Only two morphotypes were confirmed as Sidra. Furthermore, the placement of Sidra within the Core Ethiopia genetic group is consistent with prior population-level analyses and with its likely status as a selected Ethiopian landrace rather than a variety of hybrid origin. Morphological linear discriminant analysis achieved 82.4% overall classification accuracy under leave-one-out cross-validation (LOOCV), with internode length dominating the first discriminant function (LD1 = 66.6%). These results demonstrate that varietal nonconformity in the specialty coffee seed sector can extend to the inadvertent introduction of genetically unrelated material and underscore the urgent need for accessible seed certification.
Why it matches plant phenotyping methodsコーヒー品種識別のための形態形質測定と判別分析が研究の中心であり、形態形質の識別性能をLOOCVで検証しているため、植物フェノタイピング手法の適用・検証に該当する。
abstractquantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics
Reproduction assets foundThe paper's morphological/functional trait dataset (used for the phenotyping and LDA analysis) is explicitly stated to be publicly available on Figshare (10.6084/m9.figshare.32841344). No author analysis code repository is stated; other URLs in the text are generic libraries or cited prior work.Dataset · publicThe morphological and functional trait dataset generated and analyzed in this study is publicly available in the Figshare repository at 10.6084/m9.figshare.32841344 .Open asset ↗Figshare · 10.6084/m9.figshare.32841344lines:526-568Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Nutrient deficiencies in coffee plants significantly impact bean quality and yield, making timely detection crucial for successful cultivation. Current assessment methods rely on manual inspection, which is labor-intensive and time-consuming, posing challenges for large-scale field management. This approach often results in inconsistent evaluations and delayed interventions. Methods This study presents CoNutriNet, an automated deep learning architecture that integrates DenseNet121 with a novel Graph-Enhanced Attention Feature Network (GEAFNet) for classifying nutrient deficiencies in coffee leaves. DenseNet121 provides deep hierarchical and regional feature representation, while GEAFNet captures local, fine-grained spatial features through Inception, Ghost, and Efficient Channel Attention (ECA) modules. Furthermore, a Graph Convolutional Network (GCN) is included to model spatial dependencies and structural variations between leaf regions. Feature representations from both pathways are concatenated and refined using a Coordinate Attention (CA) module to enhance discriminative capability. Results Evaluation on the CoLeaf dataset demonstrates that CoNutriNet achieves an accuracy of 94.5%. The integration of lightweight attention mechanisms, dense connectivity, and graph-based modeling improves both performance and computational efficiency. Conclusion These results indicate that CoNutriNet achieves and efficient performance in nutrient deficiency detection in coffee crops, highlighting its potential for deployment in agricultural environments to support precision farming and optimize yield.
Why it matches plant phenotyping methodsコーヒー葉の栄養欠乏という植物状態を画像から分類する深層学習手法を開発し、データセットで性能評価しており、表現型取得・推定が研究の中心である。
abstractThis study presents CoNutriNet, an automated deep learning architecture that integrates DenseNet121 with a novel Graph-Enhanced Attention Feature Network (GEAFNet) for classifying nutrient deficiencies in coffee leaves.
Reproduction assets foundThe paper's phenotyping analysis (coffee nutrient deficiency classification) is performed on publicly available leaf image datasets. The data availability statement links a Mendeley Data repository containing the analyzed data, which is an allowed URL. No author analysis code or trained model checkpoints are explicitlyDataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/brfgw46wzb/1Open asset ↗brfgw46wzb/1lines:866-910Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Plant disease is a serious threat to agricultural productivity and food security worldwide. Traditional diagnostic methods such as manual observation and laboratory testing are time-consuming, labor-intensive and error prone. The emergence of artificial intelligence (AI) and deep learning (DL) offer scalable solutions for precision agriculture in plant disease detection using advanced computational techniques to process large datasets. Hybrid deep learning architecture integrates Convolutional Neural Networks (CNNs) along with Artificial Neural Networks (ANNs) can leverage both visual and contextual data to improve detection performance. The hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics). The CNN module extracted spatial and textural features from plant images, while the ANN module processed environmental parameters. These outputs were fused into a unified feature vector for disease classification. A total of 15 plant species and their associated diseases were analyzed using 200-270 training samples and 150-190 testing samples for each disease across a total of 1000 images. The model was judged by metrics such as detection accuracy, AUC, sensitivity etc. Data augmentation, pre-trained architectures (e.g., ResNet50) and early stopping techniques were utilized to improvise model performance. The hybrid model saliently achieved detection accuracy consistently above 87% with majority of diseases surpassing 90%. Highperforming cases like Rice Blast (92.5%), Tomato Early Blight (93.8%), and Coffee Rust (93.0%), with AUC values of 0.93 or higher, sensitivity exceeding 94% and specifically above 90%. Diseases of Sugarcane Red Rot and Tea Blister Blight exhibited sensitivities of 92.4% and 92.1% and specificities of 91.1% and 90.5% respectively. Moderate accuracy for Coconut Bud Rot (87.5%) and Mustard Alternaria Blight (87.8%) was due to smaller training sample sizes.
Why it matches plant phenotyping methods植物画像から病害状態を分類するハイブリッドCNN-ANN手法を開発し、精度・AUC・感度などで評価しており、病害フェノタイピング手法が研究の中心である。
abstractThe hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics).
Reproduction assets foundThe paper's plant disease detection model was trained on public plant image datasets: the PlantSeg dataset (Zenodo record 13958858, DOI 10.5281/zenodo.13293891) and the UCI Machine Learning Repository Plants dataset. Both are cited in Materials and Methods as sources of the visual data used for the CNN module. No code,Dataset · publicebao (ZiranKexueBan)/Journal of
Huazhong University of Science and Technology
(Natural Science Edition). 2021;49(8).
37. Ma C, Mu X, Sha D. Multi-Layers Feature Fusion of
Convolutional Neural Network for Scene
Classification of Remote Sensing. IEEE Access.
2019;7.
38. Hämäläinen W.Plants Dataset[Internet]. 2024.
Available from:
https://archive.ics.uci.edu/dataset/180/plants
39. WeiT. PlantSeg: A Large-Scale In-the-wild Dataset
for Plant Disease Segmentation [Internet]. 2018.
Available from: https://zenodo.org/records/13958858Open asset ↗180pdf-raw-page:15 lines:1-48Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Objectives Nutritional deficiency in coffee is a major problem that compromises plant health, crop yield, and bean quality, directly threatening the economies of coffee-dependent regions. Traditional detection methods are primarily manual, time-consuming, and relied upon expert availability. Methods This study introduces a novel Deep Learning (DL)-based dual-track architecture designed for the efficient classification of nutritional deficiencies in coffee leaf. The first track utilizes a MobileNetV3 backbone integrated with a Multi-Convolutional Shape-Aware Kernel (MCSK) block to capture spatially adaptive features from leaf textures and vein patterns. The second track employs a Hierarchical Shuffled Group Attention Network (HSGAN), utilizing Efficient Channel Attention (ECA) and Local Group Attention (LGA) modules to balance fine-grained local variations with broad spatial dependencies. Finally, a Multidimensional Collaborative Attention (MCA) mechanism is applied to the fused features to enhance cross-channel interactions and feature extraction. Results The proposed model was evaluated using the CoLeaf dataset, where it achieved an accuracy score of 96.04%. This performance demonstrates an improvement over existing research and current state-of-the-art models, highlighting the architecture's ability to identify complex nutrient-related patterns in coffee leaves. Conclusion The performance of the proposed DL approach offer a solution for the automated monitoring of coffee plants. By providing a reliable alternative to manual inspection, this method presents the potential to help coffee production and support the agricultural regions worldwide.
Why it matches plant phenotyping methodsコーヒー葉の栄養欠乏という植物状態を画像から分類する深層学習手法を開発・評価しており、表現型取得・推定が研究の中心であるため。
abstractThis study introduces a novel Deep Learning (DL)-based dual-track architecture designed for the efficient classification of nutritional deficiencies in coffee leaf.
Reproduction assets foundThe paper's primary phenotyping asset is the CoLeaf coffee leaf nutrient-deficiency image dataset, which the authors state is publicly available via a Mendeley Data URL matching an allowed URL. No author analysis code or trained model checkpoints are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/brfgw46wzb/1 .Open asset ↗brfgw46wzb/1lines:733-764Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
CoffeeField / plotFruitObject detectionGrowth / development / phenology
Introduction Coffee cherry ripeness assessment is critical for harvesting efficiency and product quality, yet traditional manual inspection methods suffer from subjectivity and low efficiency. Methods To address the challenges of detecting small, occluded, and densely distributed coffee cherries in complex field environments, this study proposes an Occlusion and Density Aware Network (ODANet). Built upon the YOLOv8 framework, ODANet integrates three innovative modules: (1) Condition-Guided Windowed Attention (CGWA), which incorporates occlusion and density maps as auxiliary guidance signals for efficient feature enhancement; (2) Attention-guided Space-Preserving Convolution (ASPC), which employs space-to-depth transformation with cascaded attention to preserve spatial information during downsampling; and (3) Dual-Adaptive Dynamic Upsampling (DADU), which achieves content-adaptive feature reconstruction through dual-branch offset prediction with learnable fusion weights. Results Comprehensive evaluation on a publicly available dataset demonstrates that ODANet achieves state-of-the-art performance among 17 diverse detection architectures, attaining 76.7% mAP@0.5 with a 6.3 percentage point improvement over baseline YOLOv8, while maintaining computational efficiency (8.1 GFLOPs, 30.4M parameters) suitable for real-time deployment. Ablation studies validate the contributions of each module: ASPC improves performance by 2.2%, DADU by 0.6%, and CGWA by 3.5%. Discussion The model demonstrates robust performance across varying lighting conditions, occlusion levels, and growth stages, making it particularly suitable for practical agricultural deployment. This research provides an efficient solution for small object detection in precision agriculture.
Why it matches plant phenotyping methodsコーヒーチェリーの成熟度という植物器官の状態を画像から推定する検出手法を開発し、複数モデル比較・アブレーションで技術的に検証しているため、植物フェノタイピング手法が中心である。
abstractthis study proposes an Occlusion and Density Aware Network (ODANet)
Reproduction assets foundThe paper analyzes a publicly available coffee cherry dataset hosted on Kaggle, explicitly linked in the data availability statement. This is the paper-specific image dataset used for its coffee cherry ripeness detection experiments. No author code or model checkpoints are stated as available.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/harisyunanda/dataset-coffee-cherry/data .Open asset ↗Kaggle · harisyunanda/dataset-coffee-cherrylines:682-758Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Precise detection of crop leaf damage is essential for real-time plant health monitoring and yield estimation. However, conventional deep learning models often exhibit poor generalization when deployed across varying species and complex, unstructured field environments. To address these limitations, we propose a new modeling paradigm that shifts from traditional task-specific training to foundation model adaptation. Specifically, we introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation. By incorporating a Spatial Prior Module (SPM) and a Projection Module, our approach effectively bridges the gap between general-purpose pre-training and domain-specific requirements. Experimental results on coffee and black gram datasets demonstrate that this paradigm consistently outperforms standard networks, including Unet, Unet++, and SwinUnet. On the coffee leaf dataset, the proposed model achieves an Intersection over Union (IoU) of 78.31% and a Pixel Accuracy of 88.00%, surpassing the baseline Unet by over 10.5% in IoU. Remarkably, the architecture reduces inference time by approximately 93.6% (from 63.41s to 4.07s), proving that high-parameter foundation models can be adapted for extreme computational efficiency in agricultural scenarios. To further validate scalability, we conduct additional experiments on a larger dataset, AMG HS . The proposed paradigm achieves the best overall detection performance while maintaining superior computational efficiency, confirming its robustness under increased data scale. Interpretability analysis reveals that the foundation model backbone effectively captures high-level semantic features of lesions, providing a clear explanation for its superior performance and cross-domain reliability. This research establishes a scalable, high-performance paradigm for intelligent crop protection, demonstrating that coupling customized encoders with foundation models is a superior strategy for cross-domain agricultural tasks.
Why it matches plant phenotyping methods植物葉の病変を画像からセグメンテーションし、葉の損傷状態を定量化する手法の開発・検証が研究の中心であるため。
abstractwe introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation.
Reproduction assets foundThe paper analyzes two publicly available plant image datasets hosted on Mendeley Data: a coffee leaf rust/leaf miner dataset and a black gram leaf disease dataset, both explicitly linked in the Data Availability Statement. No author analysis code or trained model checkpoints are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/vfxf4trtcg/5; https://data.mendeley.com/datasets/45djgf3p96/1.Open asset ↗html-lines:473-493Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/vfxf4trtcg/5; https://data.mendeley.com/datasets/45djgf3p96/1.Open asset ↗html-lines:473-493Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Coffee is a vital agricultural commodity that sustains millions of farmers worldwide, yet its cultivation is increasingly threatened by devastating leaf diseases such as Leaf Rust, Phoma, Cercospora, and Leaf Miner. These diseases reduce photosynthetic efficiency, cause defoliation, and ultimately lower crop yield and quality. Traditional diagnostic methods, including visual inspection and laboratory-based tests such as PCR and ELISA, are often time-consuming, costly, and require expert intervention, making them impractical for large-scale use. To address these challenges, we propose EffResViT-SE FusionNet, a novel hybrid deep learning framework that integrates EfficientNetB3 and ResNet50 enhanced with Squeeze-and-Excitation (SE) blocks for adaptive local feature recalibration, along with a Vision Transformer (ViT) for modeling global contextual dependencies. This fusion design effectively combines CNN-based local feature extraction with transformer-based long-range attention in a unified architecture. The model was trained on a large-scale dataset comprising 58,555 coffee leaf images distributed across five classes: Healthy (18,984), Miner (16,983), Leaf Rust (8336), Cercospora (7681), and Phoma (6571). The dataset was split into 70%, 15%, and 15% testing. Key hyperparameters included the Adam optimizer, a learning rate of 0.001, a batch size of 32, and 80 training epochs, ensuring stable convergence. Experimental results demonstrate the superior capability of the proposed model, achieving an overall classification accuracy of 99%, with precision, recall, and F1-scores all ranging between 98% and 99% across all classes. Comparative analysis confirmed notable improvements over baseline models: ResNet50 (94% accuracy), EfficientNetB3 (95% accuracy), and standalone ViT (97% accuracy). Furthermore, ablation studies validated the critical role of SE blocks and feature fusion with the transformer in achieving optimal performance. These outcomes highlight EffResViT-SE FusionNet as a powerful, precise, and scalable solution for early detection and classification of coffee leaf diseases, supporting timely interventions and promoting sustainable agriculture.
Why it matches plant phenotyping methodsコーヒー葉画像から病害状態を分類する深層学習フレームワークの開発・比較検証が研究の中心であり、植物病害表現型の取得・推定に該当する。
titleEffResViT-SE FusionNet: A Hybrid Deep Learning Framework for Accurate Classification of Coffee Leaf Diseases.
Reproduction assets foundThe paper's plant-phenotyping input is a public Kaggle coffee leaf image dataset (58,555 images across five classes) explicitly linked in the Data Availability Statement. No author analysis code or trained model checkpoints are disclosed.Dataset · publicInterest
The authors declare no conflicts of interest.
Acknowledgments
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R238), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data Availability Statement
The dataset used in this study is publicly available on Kaggle: https://www.kaggle.com/datasets/noamaanabdulazeem/jmuben‐coffee‐dataset/data .
References
Adelaja , O.
, and
B.
Pranggono
. 2025 . “
Leveraging Deep Learning for Real‐Time Coffee Leaf Disease Identification
.” Aǧrı
7 , no. 1 : 13 .
10.3390/agriengineering7010013
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Alirezazadeh , P.
,
M.
Schirrmann
, and
F.
Stolzenburg
. 2023 . “
Improving Deep Learning‐Based Plant DisOpen asset ↗Kaggle · jmuben‐coffee‐datasetlines:822-895Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Coffee, the world’s most traded tropical crop, is vital to the economies of many producing countries. However, coffee leaf diseases pose a serious threat to coffee quality and sustainable production. Deep learning has shown strong performance in plant disease identification through automatic image classification. Nevertheless, reliance on a single convolutional neural networks (CNNs) architecture restricts feature variability and real-world generalization. Moreover, limited work has systematically combined feature selection/reduction with CNNs, which constrains the advancement of hybrid models capable of capturing complementary features while ensuring computational efficiency without accuracy loss. This article presents an enhanced deep learning-based framework for coffee disease classification incorporating a hybrid strategy that integrates CNNs and advanced feature selection algorithms. GoogLeNet and ResNet18 are paired for complementary feature extraction, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are employed for dimensionality reduction, and ANOVA and Chi-square are applied to select the most informative features. An Adam optimizer (learning rate = 0.001, batch size = 20, epochs = 50) with early stopping is used for training. Experiments on the BRACOL dataset achieved 99.78% accuracy, with precision, recall, and F1-score all exceeding 99% across classes. To the best of our knowledge, this study systematically integrates GoogLeNet and ResNet18 with PCA/SVD dimensionality reduction and analysis of variance (ANOVA)/Chi-square feature selection, for coffee disease classification, thereby addressing a key gap in prior research.
Why it matches plant phenotyping methodsコーヒー葉の病害を画像から分類する深層学習フレームワークが研究の中心であり、植物の病害状態を直接推定する実質的な表現型解析手法である。
abstractThis article presents an enhanced deep learning-based framework for coffee disease classification incorporating a hybrid strategy that integrates CNNs and advanced feature selection algorithms.
Reproduction assets foundThe paper uses the public BRACOL/RoCoLe coffee leaf image dataset (Mendeley) and provides authors' analysis code publicly on GitHub and Zenodo, all explicitly linked in the text.Dataset · publicWe utilized the BRACOL dataset, a publicly available dataset of coffee leaf images. The dataset can be accessed at the following DOI: ( https://data.mendeley.com/datasets/c5yvn32dzg/2 ).Open asset ↗lines:30-49Code · publicAll implementation details, including preprocessing scripts, model training, and evaluation codes, are available in the following GitHub repository: ( https://github.com/DrMaherAlrahhal/coffe-code ).Open asset ↗GitHublines:30-49Code · publicThe code is available at GitHub and Zenodo:
- https://github.com/DrMaherAlrahhal/coffe-code .
- w. (2025). coffee code. Zenodo. https://doi.org/10.5281/zenodo.17470672 .Open asset ↗Zenodo · 10.5281/zenodo.17470672lines:2688-2696Code / dataset availability confirmedCrossref · checked 6 Sept 2026
CoffeeMultispectral / hyperspectralLeafClassificationPhysiological trait estimationWater status / transpiration
Water potential is an important indicator used to study water relations in plants, as it reflects the level of hydration in their tissues. There are different numerical variables that describe plant properties and can be acquired from leaf reflectance. The objective of this study was to estimate water potential in coffee plants using spectral variables. For this, a range of wavelengths that provided analytical flexibility was used. After this, machine learning techniques were employed to build data-driven models. The dataset used presents spectral characteristics (wavelength) of coffee plants, collected through the CI-710 Mini-Leaf Spectrometer equipment and also the water potential of each coffee plant, measured by the Scholander Chamber equipment. The dataset was divided into two crop management groups: irrigated and rainfed. Four machine learning techniques were implemented: Multi-Layer Perceptron (MLP), Decision Tree, Random Forest and K-Nearest Neighbor (KNN). The implementation of machine learning techniques followed two distinct strategies: regression and classification. The results indicate that the decision tree-based model demonstrated superior performance under irrigated conditions for regression tasks. In contrast, the KNN technique achieved the best performance for classification. Under rainfed conditions, the MLP model outperformed the other techniques for regression, while the Random Forest method exhibited the highest accuracy in classification tasks. While no hardware prototype was developed, the machine learning-based methods presented here suggest a possible pathway toward future intelligent, user-friendly, and accessible sensing technologies for coffee plantations.
Why it matches plant phenotyping methodsコーヒー植物の葉スペクトルから水ポテンシャルという生理形質を機械学習で推定・分類する手法が研究の中心であり、植物フェノタイピング手法の開発・評価に該当する。
abstractThe objective of this study was to estimate water potential in coffee plants using spectral variables.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the study's datasets (coffee leaf spectral reflectance and water potential measurements) and the MATLAB analysis codes are publicly available at the authors' UFLA repository, which is an allowed URL. This is a paper-specific, public, actionable asset.Dataset · publicData Availability Statement: The datasets and MATLAB codes used in this study are available at
http://www.aia.ufla.br/home/filesdatasets/, accessed on 27 November 2025.Open asset ↗aia.ufla.brpdf-page:18 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Precision agriculture technologies based on satellite remote sensing remain largely inaccessible to smallholder farmers in developing countries due to technical complexity, cost barriers, and infrastructure demands. This study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery tailored to the specific needs of family farming systems. The platform integrates a FastAPI backend for geospatial data processing with a Next.js frontend providing simplified tools for spectral index computation (NDVI, EVI, SAVI, NDWI, NDBI), crop classification using supervised and unsupervised machine learning, and interactive 2D/3D visualization. A laboratory module implements thirteen digital image processing techniques—including Gaussian filtering, edge detection, morphological operations, and thresholding—for educational and comparative analysis. The browser-based system eliminates installation requirements and automates key workflows such as coordinate reprojection, JP2 band extraction, and statistical evaluation. Validation using ground-truth data from coffee and soybean fields in the Brazilian Cerrado achieved classification accuracies above 85% and correlation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics. The platform contributes to the democratization of remote sensing technologies and enhances accessibility of precision agriculture tools for smallholder farmers.
Why it matches plant phenotyping methods植物圃場の衛星画像を処理し、NDVI等からバイオマスを推定するオープンソース基盤の設計・実装・検証が中心であり、植物形質推定ワークフローとして収録対象。
titleAn Open-Source Web Platform for Sentinel-2 Multispectral Analysis in Smallholder Agriculture: Design, Implementation and Validation
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' complete source code, documentation, and example datasets for the Sentinel-2 phenotyping/analysis platform on a public GitHub repository under MIT license. Sentinel-2 imagery is from the public Copernicus browser, but that is a generic data sourceCode · publicresearch received no external funding
Institutional Review Board Statement: Not applicable. This study did not involve humans or animals.
Informed Consent Statement: Not applicable. This study did not involve humans.
Data Availability Statement: Complete source code, documentation, and example datasets are publicly available
at https://github.com/rexionmars/icev-remote-sensing under MIT license. The platform can be deployed locally
or accessed via hosted instance for testing purposes. Sentinel-2 satellite imagery used in this study was obtained
from the Copernicus Open Access Hub (https://browser.dataspace.copernicus.eu/) and is freely available.
Acknowledgments: The authors thank the iCEV IOpen asset ↗https://github.com/rexionmars/icev-remote-sensing · icev-remote-sensingpdf-layout-page:11 lines:1-70Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract The growth and productivity of banana crops are critically affected by micronutrient deficiencies, which are often difficult to detect at early stages. Lightweight deep learning models, optimized through neural architecture search (NAS) and attention mechanisms, are hypothesized to provide accurate and efficient classification of such deficiencies for real-time agricultural applications. In this study, multiple convolutional neural networks (CNNs) and mobile-friendly architectures, including ResNet50, VGG16, NASNetMobile, and MobileNet variants (V1, V2, V3), were evaluated using transfer learning on a curated banana leaf deficiency dataset. To improve robustness and prediction accuracy, modified classification layers and ensemble strategies–initially average ensembling and later a NAS-guided dynamic attention weighting mechanism were employed. This optimization resulted in a novel lightweight model, NASMobV2 (NASNetMobile + MobileNetV2), capable of both classifying nutrient deficiencies and assessing their severity levels. The proposed model achieved a validation accuracy of 98.57%, outperforming baseline and state-of-the-art counterparts in precision, recall, and F1 score. To improve generalization, banana crop diseases along with an additional Coffee crop dataset were included for evaluation. Finally, the practical utility of the model was demonstrated by deploying the trained system in both mobile and web applications, enabling farmers and agronomists to perform fast and accurate diagnostics directly in the field.
Why it matches plant phenotyping methodsバナナ葉画像から栄養欠乏と重症度を推定する軽量深層学習モデルを開発・評価し、実運用アプリにも展開しており、植物状態の取得・推定手法が中心である。
titleA neural architecture search optimized lightweight attention ensemble model for nutrient deficiency and severity assessment in diverse crop leaves.
Reproduction assets foundThe paper's banana leaf nutrient-deficiency image dataset is publicly available on Mendeley Data and was directly used for the phenotyping/classification measurements. No author analysis code or trained model checkpoints are explicitly deposited; the GitHub/Streamlit links are deployment apps rather than deposited codeDataset · publiclidation and editing in addition to overall supervision.
Funding
Open access funding provided by Vellore Institute of Technology. We thank our Management “Vellore Institute of Technology, Vellore” for open access funding support.
Data availability
An openly available repository (Mendeley dataset) was used to perform this study;(https://data.mendeley.com/datasets/7vpdrbdkd4/1), Request for any data or materials shall be addressed to the author(sudhakar.m2020@vitstudent.ac.in).
Declarations
Competing interests
The authors declare that they have no competing interests.
References
1.
Sherefu A Zewide I
Review paper on effect of micronutrients for crop production
J. Nutr. Food Process. 2021
10.31Open asset ↗Mendeley · 7vpdrbdkd4lines:1245-1307Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this work, a unique database of 6726 multispectral images of coffee leaves is presented. These images were captured in JPG format for the RGB photos and in TIF format for the five multispectral bands: blue, green, red, NIR and red edge, providing a detailed view of different wavelengths of the electromagnetic spectrum. Images in TIF format have a color depth of 16 bits per pixel, ensuring good quality. The blue band (Band 1) captures light in the blue region of the spectrum, approximately 450 to 500 nm. The green band (Band 2) records light in the green region, approximately between 500 and 620 nm. The red band (Band 3) captures light in the red region, between 620 and 750 nm. The red-edge band (Band 4) lies between the red band and the NIR, and is sensitive to the transition between green vegetation and non-vegetation, around 840 nm. Finally, the near infrared band (Band 5) captures light in the near infrared region, between 750 and 900 nm. For ease of identification, images are labeled as follows: if the image name ends in 0, it is an RGB image; if it ends in 1, it corresponds to the blue band; if it ends in 2, to the green band; if it ends in 3, to the red band; if it ends in 4, to the red-edge band; and if it ends in 5, to the near-infrared band. The images show coffee leaves with and without lesions caused by the Hemileia vastatrix fungus, known as coffee rust. These samples were collected from Colombian coffee farms and the images were captured under controlled lighting conditions to ensure quality and consistency. This database is an invaluable resource for precision agriculture research and early detection of crop diseases. With these 6726 images, researchers can use advanced image processing and machine learning techniques to identify differences between healthy leaves and those affected by rust. This can lead to the development of effective predictive models, enabling early detection and more efficient management of diseases in coffee plantations, optimizing production and reducing economic losses for farmers.
Why it matches plant phenotyping methodsコーヒー葉の病斑という植物の病害状態を対象としたマルチスペクトル画像データセットであり、再利用可能なフェノタイピング用データセットの提供が中心です。
abstractIn this work, a unique database of 6726 multispectral images of coffee leaves is presented.
Reproduction assets foundThe paper is a data descriptor whose own multispectral coffee leaf image dataset is publicly deposited on Kaggle with an explicit direct URL and DOI, matching an allowed URL.Dataset · publicth of 16 bits per pixel .
Data source location
Institution: Escuela Colombiana de Ingeniería Julio Garavito University
City/Town/Region: Bogotá D.C.
Country: Colombia Latitude: 4.5983° * Longitude: 74.0051°.
Data accessibility
Repository name: Coffe Rust
Data identification number: 10.34740/kaggle/ds/5644659
Direct URL to data: https://www.kaggle.com/ds/5644659
Instructions for accessing these data: Data available free of charge to anyone with access to the Internet and the web server address provided.
Related research article
[ 1 ] Jorge Luis Aroca Trujillo, Alexander Pérez-Ruiz. “Technologies Applied in the Field of Early Detection of Coffee Rust Fungus Diseases: A Review.” Nongye JOpen asset ↗Kaggle · 10.34740/kaggle/ds/5644659lines:1-53Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation (HDMR) to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems.
Why it matches plant phenotyping methodsコーヒー葉画像から病害の種類と重症度を推定する画像・深層学習手法が研究の中心であり、植物状態の表現型評価に該当する。
abstractThis study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images.
Reproduction assets foundThe authors explicitly state that the data, algorithms, and code for the DeepEMPR coffee leaf disease detection study are publicly available on GitHub and Zenodo, and the leaf image dataset (LeafData.zip) is deposited on Figshare. These are paper-specific, publicly actionable assets directly supporting the study's phenCode · publicd the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The data, algorithms and code are available at GitHub and Zenodo:
- https://github.com/ArticleCodeHub/DeepEMPR
- Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 .
The code is available in the Supplemental File .
The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 .
References
Agarwal et al. (2020)
Agarwal M
Singh A
ArjOpen asset ↗ArticleCodeHub/DeepEMPRlines:660-763Code · publicgures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The data, algorithms and code are available at GitHub and Zenodo:
- https://github.com/ArticleCodeHub/DeepEMPR
- Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 .
The code is available in the Supplemental File .
The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 .
References
Agarwal et al. (2020)
Agarwal M
Singh A
Arjaria S
Sinha A
Gupta S
2020
ToLeD: tomato leaf disease detection using convolutiOpen asset ↗10.5281/zenodo.13823450lines:660-763Dataset · publicdata, algorithms and code are available at GitHub and Zenodo:
- https://github.com/ArticleCodeHub/DeepEMPR
- Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 .
The code is available in the Supplemental File .
The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 .
References
Agarwal et al. (2020)
Agarwal M
Singh A
Arjaria S
Sinha A
Gupta S
2020
ToLeD: tomato leaf disease detection using convolution neural network
Procedia Computer Science 167 293 301
10.1016/j.procs.2020.03.225
Ahmed et al. (2019)
Ahmed K
Shahidi TR
Alam SMI
Momen S
2019
Rice leaf disease detection using machineOpen asset ↗10.6084/m9.figshare.26060464.v1lines:660-763Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とYOLOv9、二重チャネルNMSを用いてコーヒー植物の検出・セグメンテーション・個体数推定手法を開発し、精度評価しているため、植物フェノタイピング手法が中心である。
titleA Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicWe will publish all the codes and datasets in this study after the article is accepted
https://github.com/legend2588/Coffee-plant-counting.gitOpen asset ↗https://github.com/legend2588/Coffee-plant-counting.gitpdf-page:19 lines:1-58Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Deep learning and computer vision, using remote sensing and drones, are 2 promising nondestructive methods for plant monitoring and phenotyping. However, their applications are infeasible for many crop systems under tree canopies, such as coffee crops, making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low cost. This study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach. The approach uses basic smartphones to take a few pictures of coffee trees; 2,968 trees were investigated with 8,904 pictures in Junín and Piura (Peru), Cauca, and Quindío (Colombia) in 2022, with the help of nearly 1,000 smallholder coffee farmers. Then, we trained and validated YOLO (You Only Look Once) v8 for detecting cherries in the dataset in Peru. An average number of cherries per picture was multiplied by the number of branches to estimate the total number of cherries per tree. The model's performance in Peru showed an R 2 of 0.59. When the model was tested in Colombia, where different varieties are grown in different biogeoclimatic conditions, the model showed an R 2 of 0.71. The overall performance in both countries reached an R 2 of 0.72. The results suggest that the method can be applied to much broader scales and is transferable to other varieties, countries, and regions. To our knowledge, this is the first AI-powered method for counting coffee cherries and has the potential for a geographic-scale, multiyear, photo-based phenotypic monitoring for coffee crops in low-income countries worldwide.
Why it matches plant phenotyping methodsスマートフォン画像と深層学習でコーヒー果実数を推定する方法を開発・検証しており、植物形質の取得が研究の中心です。
abstractThis study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits representative coffee cherry pictures (phenotyping image data) and the authors' Python analysis script in a public GitHub repository, matching the paper's smartphone-image cherry counting analysis. Other URLs (FAOSTAT, SENAMHI, IDEAM, Label Studio, YOLOv5 docsCode · publicSome representative pictures and the Python script used for the study are available at the GitHub repository: https://github.com/j-river1/Croppie .Open asset ↗j-river1/Croppielines:207-221Code / dataset availability confirmedCrossref · checked 15 Sept 2026
In an effort to advance Digital Agriculture, this paper provides a comparative assessment of Artificial Neural Networks for intelligent detection of a major biotic stress factors in coffee cultivation. Through a multi-class Computer Vision task, the superior performance of Convolutional Neural Networks, notably the ShuffleNet architecture, was discerned, further substantiated by statistical analyses. This model's performance, akin to state-of-the-art solutions, was achieved with reduced training data and parameter requirements. Robustness was affirmed through external validation using alternative datasets. This contribution directly enhances coffee plantations' quality and supports the development of Edge Computing devices for Agricultural IoT.
Why it matches plant phenotyping methodsコーヒー葉の病害状態を画像から検出するコンピュータビジョン手法を比較・評価し、外部データセットで検証しているため、植物病害フェノタイピング手法が中心です。
abstractThrough a multi-class Computer Vision task, the superior performance of Convolutional Neural Networks, notably the ShuffleNet architecture, was discerned
Reproduction assets foundThe paper's Declarations section explicitly states that the datasets generated and/or analysed during the study are available at the authors' public GitHub repository (https://github.com/elloa/jis-2023), which is an allowed URL. This repository is the paper-specific asset covering the coffee leaf disease image datasetsDataset · publicent Systems (LSI) at the Amazonas State University (UEA).
Authors’ Contributions
Both authors have contributed equally to this manuscript.
Competing interests
The authors declare they do not have competing interests.
Availability of data and materials
The datasets generated and/or analysed during the current study are
available https://github.com/elloa/jis-2023
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z.,
Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M.,
Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Is-
ard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M.,
Levenberg, J., Mané, D., Monga, R., Moore, S., Mur-
ray, D., Olah, C., Schuster, M., ShOpen asset ↗github.com/elloa/jis-2023pdf-raw-page:12 lines:1-91Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Coffee leaf disease is a problem that needs attention because it affects the quality and productivity of the coffee harvest and is detrimental to farmers. Therefore, a system is needed to identify types of coffee leaf diseases using artificial intelligence. There are four types of coffee leaf diseases, namely Miner leaf, Phoma leaf, Rust leaf, and Nodisease leaf. The research used the EfficientNet Architecture Convolutional Neural Network (CNN) method to detect types of disease on coffee leaves. This method was chosen because it is capable and reliable in processing digital images for pattern recognition. The dataset used is 1,464 images with dimensions of 2048 x 1024 pixels with RGB type which are divided into 1,264 training data and 400 testing data. Several architectures used in EfficientNet are EfficientNet B0, EfficientNet B1, EfficientNet B2, EfficientNet B3, EfficientNet B4. Parameters used are Lanczos resampling, Epoch 25, Learning Rate 0.0001, Loss Function Sparse Categorical Cross Entropy, Optimizer Adam. The results of training data testing, namely the CNN EfficientNet B1 Architecture Model method, got the best accuracy of 97% and a loss of 0.1328 and testing data testing got an accuracy of 0.97% and a loss of 0.1328. The architecture of the EfficientNet B1 model is better than other architectural models, namely VGG16, ResNet50, MobileNetv2, EfficientNet B0, EfficientNet B2, EfficientNet B3, EfficientNet B4, EfficientNet B5, EfficientNet B6, EfficientNet B7.
Why it matches plant phenotyping methodsコーヒー葉画像から病害状態を分類するCNN手法が研究の中心であり、植物病害表現型の画像ベース推定に該当する。
abstractThe research used the EfficientNet Architecture Convolutional Neural Network (CNN) method to detect types of disease on coffee leaves.
Reproduction assets foundThe paper's coffee leaf disease image dataset (1,464 RGB images of Miner, Phoma, Rust, and healthy leaves) is a public Kaggle dataset explicitly linked by the authors. No author analysis code or trained model checkpoints are reported as available.Dataset · publicIICS SEMNASTIK 2023 E-ISSN: 2774-5899 | P-ISSN: 2774-5880 ■ 61
pixels with RGB color mode, and the total number of images is 1.464, as detailed in Table 3. This dataset
can be accessed via the following link:
https://www.kaggle.com/datasets/gauravduttakiit/coffee-leaf-diseases.
Rust Phoma
Nodisease Miner
Figure 4. Types of Coffee Leaf Diseases
Table 3. Dataset details
Type Training Testing
Miner 332 128
No Disease 284 116
Phoma 388 96
Rust 260 60
Total 1.264 400
2.3 Evaluation
Evaluation is a critical step to obtain performance from model results [27][28]. This evaluation
process utilizes a matrix, aOpen asset ↗Kaggle · gauravduttakiit/coffee-leaf-diseasespdf-layout-page:4 lines:1-52Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unmanned Aerial Vehicle . The datasets contain 3000 coffee and 3086 cashew nut images, constituting 6086 images. Annotated objects of interest in the coffee dataset consist of five classes namely: unripe, ripening, ripe, spoilt, and coffee_tree. Annotated objects of interest in the cashew nut dataset consist of six classes namely: tree, flower, premature, unripe, ripe, and spoilt. The datasets may be used for various machine-learning tasks including flowering intensity estimation, fruit maturity stage analysis, disease diagnosis, crop variety identification, and yield estimation.
Why it matches plant phenotyping methodsコーヒーとカシューナッツの画像データセットを構築し、開花強度、成熟段階、収量などの植物形質・状態推定に利用する方法基盤を提供しており、表現型取得用データセットが研究の中心である。
abstractWe present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons.
Reproduction assets foundThe paper's own UAV coffee and cashew image datasets with YOLO annotations are publicly deposited on Mendeley Data (DOI 10.17632/r46c6bpfpf.1), directly reproducing the paper's phenotyping measurements. Annotation tools (Makesense AI, VGG Image Annotator) are generic third-party tools, not paper-specific assets.Dataset · publicre of f/1.7 and focus range of 1 m to ∞, shutter speed of 2-1/8000s and ISO range of 100-6400 (Auto and Manual)
Data source location
Institution: Makerere University
City: Kampala
Country: Uganda
Data accessibility
Repository name: Mendely Data
Data identification number: http://doi.org/10.17632/r46c6bpfpf.1
Direct URL to data:
https://data.mendeley.com/datasets/r46c6bpfpf/1
1.
Value of the Data
•
Flowering intensity estimation. Flowering represents an important stage in coffee and cashew farming since it affects crop yield. It has a significant impact on yield in that flowering intensity is positively correlated with the amount of crop yield. Therefore, flowering intensity could be an imporOpen asset ↗10.17632/r46c6bpfpf.1lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The Segment Anything Model (SAM) is a versatile image segmentation model that enables zero-shot segmentation of various objects in any image using prompts, including bounding boxes, points, texts, and more. However, studies have shown that the SAM performs poorly in agricultural tasks like crop disease segmentation and pest segmentation. To address this issue, the agricultural SAM adapter (ASA) is proposed, which incorporates agricultural domain expertise into the segmentation model through a simple but effective adapter technique. By leveraging the distinctive characteristics of agricultural image segmentation and suitable user prompts, the model enables zero-shot segmentation, providing a new approach for zero-sample image segmentation in the agricultural domain. Comprehensive experiments are conducted to assess the efficacy of the ASA compared to the default SAM. The results show that the proposed model achieves significant improvements on all 12 agricultural segmentation tasks. Notably, the average Dice score improved by 41.48% on two coffee-leaf-disease segmentation tasks.
Why it matches plant phenotyping methods農業画像から作物病害を分割・推定するモデルアダプターを開発し、複数タスクで性能検証しているため、植物病害状態の画像ベース表現型計測が中心である。
abstractthe agricultural SAM adapter (ASA) is proposed, which incorporates agricultural domain expertise into the segmentation model through a simple but effective adapter technique.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Dataset · publicThese data can be
downloaded from https://doi.org/10.17632/yy2k5y8mxg.1Open asset ↗10.17632/yy2k5y8mxg.1pdf-page:15 lines:1-59Dataset · publicdownloaded from https://doi.org/10.17632/yy2k5y8mxg.1 and https://data.mendeley.com/datasetsOpen asset ↗pdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
Why it matches plant phenotyping methodsクロロフィル蛍光データを用いたフェノミック予測モデルを構築・比較し、成長形質の予測性能と条件間・集団間の転移性を評価しているため、植物フェノタイピング手法が中心である。
abstractThe models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors.
Reproduction assets foundThe paper's Data availability statement explicitly states that all code and datasets (including the ChlF phenomic data and growth-trait phenotypes used for GP/PP modeling) are freely available at the authors' public GitHub repository https://github.com/alainmbebi/GP-PP, which matches an allowed URL. Other URLs (BGLR CRCode · publicr and is an excellent proxy for photosynthesis in coffee, making it a tool of choice for assessing the vigor of a genotype, which the present study tends to prove.
Data availability
We implemented all statistical models using R programming language; the codes and all data sets used in the current study are freely available from https://github.com/alainmbebi/GP-PP .
Supplemental material is available at G3 online.
Supplementary Material
jkac170_Supplementary_Data_File_S1
Click here for additional data file.
jkac170_Supplementary_Data_File_S2
Click here for additional data file.
Acknowledgments
We would like to thank the 2 anonymous reviewers for their suggestions and comments.
Funding
ThOpen asset ↗alainmbebi/GP-PPlines:876-910Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
CoffeeLeafClassificationVisualization / data managementDisease symptoms / severity
Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model "sees" as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods.
Why it matches plant phenotyping methodsコーヒー葉の病害領域を画像から可視化・分類する手法が研究の中心であり、Grad-CAM系手法の比較と分類精度の評価を行っているため、植物病害状態の画像ベース表現型解析に該当する。
abstractIn this study, we present a methodology for visualizing coffee diseases using different visualization approaches.
Reproduction assets foundThe paper's plant-phenotyping input data (the Robusta coffee leaf image dataset, RoCoLe, used for disease classification and visualization experiments) is explicitly declared openly available in Mendeley Data. No author analysis code or trained model checkpoints are stated as publicly available. The only allowed URL isDataset · publicThe data presented in this study are openly available in Mendeley Data at doi:10.17632/c5yvn32dzg.2, reference number 36.Mendeley Data · doi:10.17632/c5yvn32dzg.2lines:198-232Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This article introduces Arabica coffee leaf datasets known as JMuBEN and JMuBEN2. Image acquisition was done in Mutira coffee plantation in Kirinyaga county-Kenya under real-world conditions using a digital camera and with the help of a pathologist. JMuBEN dataset contains three compressed folders with images inside. The first file contains 7682 images of Cerscospora, the second contains 8337 images of rust and the last one contains 6572 images of Phoma. JMuBEN2 contains two compressed files where the first file contains 16,979 images of Miner while the other contains 18,985 images of healthy leaves. In total, the dataset contains 58,555 leaf images spread across five classes (Phoma, Cescospora, Rust, Healthy, Miner,) with annotations regarding the state of the leaves and the disease names. The Arabica datasets contain images that facilitates training and validation during the utilization of deep learning algorithms for coffee plant leaf disease recognition and classification. The dataset is publicly and freely available at https://data.mendeley.com/datasets/tgv3zb82nd/1 and https://data.mendeley.com/datasets/t2r6rszp5c/1 respectively.
Why it matches plant phenotyping methodsコーヒー葉の病害・健全状態を画像と注釈で体系化した公開データセットであり、植物表現型（病害状態）の取得・分類基盤が研究の中心である。
abstractThis article introduces Arabica coffee leaf datasets known as JMuBEN and JMuBEN2.
Reproduction assets foundThe paper is a data descriptor for the JMuBEN and JMuBEN2 Arabica coffee leaf image datasets, which are the paper's own phenotyping assets (58,555 annotated leaf images across five classes) and are publicly available on Mendeley Data.Dataset · publicHealthy, Miner,) with annotations regarding the state of the leaves and the disease names. The Arabica datasets contain images that facilitates training and validation during the utilization of deep learning algorithms for coffee plant leaf disease recognition and classification. The dataset is publicly and freely available at https://data.mendeley.com/datasets/tgv3zb82nd/1 and https://data.mendeley.com/datasets/t2r6rszp5c/1 respectively.
Keywords
Arabica coffee Image datasets Machine learning Deep learning Disease diagnosis pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-Open asset ↗lines:1-54Dataset · publicte of the leaves and the disease names. The Arabica datasets contain images that facilitates training and validation during the utilization of deep learning algorithms for coffee plant leaf disease recognition and classification. The dataset is publicly and freely available at https://data.mendeley.com/datasets/tgv3zb82nd/1 and https://data.mendeley.com/datasets/t2r6rszp5c/1 respectively.
Keywords
Arabica coffee Image datasets Machine learning Deep learning Disease diagnosis pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supOpen asset ↗lines:1-54Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
In this article we introduce a robusta coffee leaf images dataset called RoCoLe. The dataset contains 1560 leaf images with visible red mites and spots (denoting coffee leaf rust presence) for infection cases and images without such structures for healthy cases. In addition, the data set includes annotations regarding objects (leaves), state (healthy and unhealthy) and the severity of disease (leaf area with spots). Images were all obtained in real-world conditions in the same coffee plants field using a smartphone camera. RoCoLe data set facilitates the evaluation of the performance of machine learning algorithms used in image segmentation and classification problems related to plant diseases recognition. The current dataset is freely and publicly available at https://doi.org/10.17632/c5yvn32dzg.2.
Why it matches plant phenotyping methodsコーヒー葉の病害状態と重症度を画像・アノテーションとして収録し、植物病害認識手法の評価用データセットとして提供することが中心であるため、植物フェノタイピング手法文献に含める。
abstractwe introduce a robusta coffee leaf images dataset called RoCoLe
Reproduction assets foundThe paper is a Data in Brief article introducing the RoCoLe dataset of 1560 annotated robusta coffee leaf images, explicitly stated as freely and publicly available on Mendeley Data with DOI 10.17632/c5yvn32dzg.2.Dataset · publicThe current dataset is freely and publicly available at https://doi.org/10.17632/c5yvn32dzg.2 .Open asset ↗10.17632/c5yvn32dzg.2lines:1-55Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
CoffeeGrapevineLeafMorphology / geometry measurementPhysiological trait estimationLeaf traitsWater status / transpiration
Fresh water is a key natural resource for food production, sanitation and industrial uses and has a high environmental value. The largest water use worldwide (~70%) corresponds to irrigation in agriculture, where use of water is becoming essential to maintain productivity. Efficient irrigation control largely depends on having access to reliable information about the actual plant water needs. Therefore, fast, portable and non-invasive sensing techniques able to measure water requirements directly on the plant are essential to face the huge challenge posed by the extensive water use in agriculture, the increasing water shortage and the impact of climate change. Non-contact resonant ultrasonic spectroscopy (NC-RUS) in the frequency range 0.1-1.2 MHz has revealed as an efficient and powerful non-destructive, non-invasive and in vivo sensing technique for leaves of different plant species. In particular, NC-RUS allows determining surface mass, thickness and elastic modulus of the leaves. Hence, valuable information can be obtained about water content and turgor pressure. This work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure. A sensing prototype is proposed, described and, as application example, used to study two different species: Vitis vinifera and Coffea arabica, whose leaves present thickness resonances in two different frequency bands (400-900 kHz and 200-400 kHz, respectively), These species are representative of two different climates and are related to two high-added value agricultural products where efficient irrigation management can be critical. Moreover, the technique can also be applied to other species and similar results can be obtained.
Why it matches plant phenotyping methods植物葉の水分量・膨圧を非接触超音波で測定するセンサー方式の要件分析、試作、応用を中心に扱っており、植物表現型取得法が明確に中心的である。
abstractThis work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure.
Reproduction assets foundThe paper's inverse-problem analysis code for extracting leaf parameters (thickness, density, ultrasound velocity, attenuation) from measured resonance spectra is explicitly stated to be publicly available via the authors' GitHub repository and the US-BIOMAT resource page. No phenotype dataset deposit is mentioned.Code · publicUS-BIOMAT
Available online: https://us-biomat.com/resources/code-2/ or https://github.com/usbiomat/ultrasonic-thickness-resonance (accessed on 12 July 2016)Open asset ↗usbiomat/ultrasonic-thickness-resonancelines:327-413