The simultaneous diagnosis of diseases and evaluation of age quality grades in tea leaves are critical for precision agriculture and the economic valuation of tea products. Although deep learning has shown promise in agricultural vision tasks, current multi-task models often suffer from performance degradation due to feature conflicts: tea leaf disease recognition relies heavily on macro-structural lesions, whereas tea leaf-age quality grading depends on micro-textural features such as trichome density and color uniformity. To address this discrepancy, we propose a novel dual branch fusion network. Our architecture fundamentally decouples the feature extraction process by utilizing a dual branch mechanism. The first branch employs global average pooling to capture first-order spatial statistics; it can retain the global structural layout necessary for macro-lesion detection. The second branch introduces a dimensionality-reduced self-bilinear pooling module to compute second-order covariance matrices; it can effectively capture the fine-grained textural patterns essential for micro-grade classification. These decoupled features are subsequently fused and optimized through a weighted multi-task loss function. Experimental results on a comprehensive tea leaf dataset demonstrate that the proposed dual fusion framework significantly outperforms baseline models. The proposed network can rescue the disease classification accuracy drop observed in standard bilinear models while maintaining exceptional grading performance. Furthermore, the proposed network maintains a compact parameter footprint and low computational complexity. This balance renders it suitable for deployment on agricultural Internet of Things edge devices where inference speed is critical.
Why it matches plant phenotyping methods茶葉の病害状態と葉齢品質を画像から推定する深層学習手法を開発し、データセット上でベースラインと比較評価しており、表現型取得・推定法が中心である。
abstractwe propose a novel dual branch fusion network
Reproduction assets foundThe paper's Data Availability Statement publicly releases the two tea leaf image datasets used for its phenotyping tasks (disease recognition and leaf-age quality grading) via Mendeley Data. The authors' analysis code and trained models are only promised 'upon acceptance' with no public URL, so they do not qualify.Dataset · publicThe tea leaf disease recognition dataset analyzed in this study is available from https://data.mendeley.com/datasets/744vznw5k2/3 (accessed on 11 February 2026)Open asset ↗744vznw5k2/3lines:514-565Dataset · publicthe tea leaf grading dataset is available from https://data.mendeley.com/datasets/7t964jmmy3/1 (accessed on 11 February 2026)Open asset ↗7t964jmmy3/1lines:514-565Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reliable traits are needed for identification of tea ( Camellia sinensis ) cultivars, yet the stability of leaf morphology and color across leaf positions remains unclear. This study evaluated inter-cultivar variation and positional stability in leaf morphological, RGB color, and SPAD traits in six predominant cultivars. One-year-old shoots were sampled in a completely randomized design, and five fully expanded leaves below the apical bud were analyzed. SPAD values were measured with a chlorophyll meter, and scanned images were used to extract contour and RGB traits. Data were analyzed using ANOVA, correlation analysis, PCA, and discriminant analysis. Leaf morphology differed among cultivars and leaf positions, with significant cultivar-by-position interactions; however, the width-to-length ratio differed among cultivars but remained stable across positions in these cultivars. SPAD values increased with leaf position and were strongly associated with RGB components, being negatively correlated with R and G and positively correlated with B. Morphological traits explained 52.988% of total variance in PCA and yielded 64.6% overall classification accuracy, with LaoHan showing the highest accuracy (83.3%). Misclassification was concentrated among genetically similar cultivars. These findings suggest that stable leaf shape proportions and SPAD-RGB relationships provide useful descriptors, whereas genetic relatedness limits morphology-based cultivar identification under the present conditions.
Why it matches plant phenotyping methods茶品種識別のため、葉の形態・RGB・SPAD特性の取得と安定性、分類性能を中心に評価しており、画像由来形質抽出を含む実質的な表現型解析である。
abstractscanned images were used to extract contour and RGB traits
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。Dataset · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology15151283/s1 , Table S1: Original data of leaf morphological traits, RGB values, and SPAD values from six tea cultivars in this study.Open asset ↗lines:368-409Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The intelligent identification of tea diseases is crucial for ensuring tea quality and reducing economic losses in the tea industry. However, the deployment of deep learning models on edge devices remains challenging due to the conflict between detection accuracy and computational overhead. To address this, we propose CA-YOLOv8n, a lightweight object detection model tailored for tea disease diagnosis. Specifically, we introduce a Path-Decoupling strategy to streamline the network structure and integrate the Coordinate Attention (CA) mechanism to enhance the model's spatial awareness of subtle pathological features. Experimental results demonstrate that the proposed model achieves a mean Average Precision (mAP@50) of 98.89% while reducing the parameter count by 32.6% and FLOPs by 24.1% compared to the baseline YOLOv8n. The model was integrated into a diagnostic platform with an automated reporting interface, demonstrating that real-time tea disease identification is feasible on commodity CPU hardware in resource-constrained agricultural environments.
Why it matches plant phenotyping methods茶葉の病害を画像から検出する軽量深層学習モデルを開発・評価し、植物の病害状態を直接推定する方法が研究の中心である。
titleSpatial-aware lightweight network for real-time tea disease detection: A coordinate attention-enhanced YOLOv8n approach with path-decoupling strategy.
Reproduction assets foundThe paper's tea-leaf disease image dataset (9,591 images, YOLO format) is publicly deposited on figshare under CC BY 4.0, as stated in the Data Availability statement and dataset description. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicAll data underlying the findings of this study are publicly available on figshare at https://doi.org/10.6084/m9.figshare.32253357 (CC BY 4.0).Open asset ↗figshare · 10.6084/m9.figshare.32253357lines:1-122Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tea (Camellia sinensis) is the world's second most consumed beverage, enjoyed daily by more than two billion people. In Bangladesh, it serves as a cornerstone agricultural export and a major sector of the domestic economy. However, commercial tea cultivation remains highly vulnerable to fungal and pest-related diseases such as Blight, Red Rust, and Helopeltis which severely reduce crop yield and compromise leaf quality. While early detection is critical to preventing widespread outbreaks, traditional manual inspection is slow, subjective, and highly error-prone. Deep learning provides a scalable alternative, yet single-branch networks often struggle to capture both minute disease lesions and broader structural degradation simultaneously. To address this, we propose a Hybrid Feature Fusion architecture that runs two highly efficient feature extractors in parallel: EfficientNetV2-Small to isolate fine-grained local textures, and MobileNetV3-Small to capture the global structural context of the leaf. The models were trained and evaluated on a real-world dataset of 2,000 annotated images, evenly distributed across the four target classes (Blight, Red Rust, Helopeltis, and Healthy). Before training, the images underwent a standardized preprocessing pipeline including resizing to 224 × 224 pixels and normalization, supplemented by a dynamic augmentation strategy featuring random rotations, horizontal flips, and brightness adjustments to improve model robustness. The proposed hybrid framework achieved an outstanding peak classification accuracy of 96.80% alongside a macro Area Under the Curve (AUC) of 0.9980. To rigorously validate its performance, the hybrid model was benchmarked against six diverse architectures: a Vision Transformer (ViT-B16 at 76.40%), a Custom CNN (89.60%), MobileNetV3 (94.40%), ResNet50 (95.60%), DenseNet121 (96.40%), and EfficientNetV2-B3 (97.60%). Although EfficientNetV2-B3 achieved a marginally higher raw accuracy, the proposed dual-branch framework delivered a superior precision-recall balance and faster convergence stability. These findings demonstrate that the proposed hybrid methodology is highly reliable and computationally balanced, making it an ideal candidate for integration into Internet of Things (IoT) edge devices for real-time disease monitoring in precision agriculture.
Why it matches plant phenotyping methods茶葉の病徴を画像から分類する深層学習手法の開発と、注釈付きデータセットおよび複数モデルとのベンチマーク検証が中心であり、植物病害状態の表現型推定に該当する。
abstractwe propose a Hybrid Feature Fusion architecture
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the curated 2000-image tea leaf dataset on Mendeley Data and the analysis code on GitHub, both with public URLs matching allowed_urls.Dataset · publicThe dataset comprising 2000 annotated tea leaf images was curated under real-world field conditions. It has been made available at https://data.mendeley.com/datasets/3x42rbj8yv/1.Open asset ↗3x42rbj8yv/1html-lines:465-480Code · publicThe computational code supporting the findings of this study is publicly accessible on GitHub: https://github.com/rayhankhan2192/Tea_Leaf_Disease_Model.Open asset ↗GitHub · rayhankhan2192/Tea_Leaf_Disease_Modelhtml-lines:465-480Code / 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Leaf morphology in tea plants (Camellia sinensis L.) profoundly influences tea quality and agronomic value, yet its genetic basis remains elusive due to labor-intensive phenotyping, foliage architecture, and ecological sensitivity of traits. Moreover, traditional methods forfeit quantitative color gradients and population-level morphological complexity. To address this challenge, we developed coleaf, an open-source image recognition-based software that demonstrated 97.6% accuracy over conventional ImageJ measurements, while offering higher efficiency and color hues quantification. We then estimated 7 key morphological traits focusing on leaves from a collection of ~ 4,200 mature leaves and ~ 5,000 bud-leaf samples across 167 genetically diverse tea accessions by coleaf. While classical understanding suggests leaf shape differentiation between two varieties in genus sinensis assamica (CSA) and sinensis (CSS), our phenotypic clustering revealed incomplete congruence with phylogenetic relationships, suggesting the presence of additional genetic or environmental modulators beyond population divergence. Furthermore, we integrated phenotypic data with whole-genome resequencing for multi-model genome-wide association studies (GWAS). Candidate genes associated with leaf architecture were involved in plant development (e.g., CsFAS2), cell division and elongation (e.g., CsFIP1), and cellular morphogenesis (e.g., CsRLK), whereas those associated with leaf color, regulated pigment accumulation (e.g., ABC transporters, CsMYB113). In conclusion, this study establishes a standardized computational framework validating automated image recognition for plant leaf phenomics. The end-to-end framework from high-throughput phenotyping to gene discovery provides critical genetic targets for tea breeding, demonstrating transformative potential in accelerating the genetic improvement of tea plants.
Why it matches plant phenotyping methods茶葉形態の画像認識ソフトウェアを開発・検証し、高スループットな形質抽出フレームワークとして適用しており、植物フェノタイピング手法が研究の中心である。
abstractwe developed coleaf, an open-source image recognition-based software that demonstrated 97.6% accuracy over conventional ImageJ measurements
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 · publicAll codes and tools used in this study are described in Methods, coleaf is available on github (https://github.com/mengmeng-jiang/coleaf).Open asset ↗mengmeng-jiang/coleafhtml-lines:390-460Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Background: A major challenge to agricultural productivity in the tea industry is disease. that affects the quantity and quality of tea leaves produced. The extensive development of computational methods for treating diseases has been widely used due to fast and accurate detection. Methods: The proposed method uses sequential Convolutional Neural Network (CNN) computations with many hidden layers to classify diseased and healthy tea leaves into multiple groups. By enhancing feature identification, this structure increases the criteria for accurate disease detection. The data having 5 diseased and one healthy category is obtained from the Kaggle database. After preprocessing the data, it is split into 80:20 ratios for training and testing steps. CNN is constructed using the Keras Sequential API in Jupiter notebook using Anaconda environment. Result: The total accuracy of the ML neural network training for classification was 98.52%. After 50 epochs of training, the model performed well, achieving high accuracy on training and validation datasets. The examination of the confusion matrix showed that several tea leaf diseases may be identified with high accuracy and few misclassifications. In general, the model demonstrated remarkable precision in differentiating between unhealthy and undamaged tea leaves.
Why it matches plant phenotyping methods茶葉画像から病害状態をCNNで分類する手法が研究の中心であり、植物の病徴・健全性という状態を直接推定しているため、植物フェノタイピング手法として含める。
abstractThe proposed method uses sequential Convolutional Neural Network (CNN) computations with many hidden layers to classify diseased and healthy tea leaves into multiple groups.
Reproduction assets foundThe paper's tea leaf disease image dataset is publicly available on Kaggle, with an explicit dataset link in the references. No author code or trained model is publicly deposited; other data are available only upon request.Dataset · publical tealeaf disease recognition
using a convolutional neural network model. Symmetry.
11(3): 343. https://doi.org/10.3390/sym11030343.Cho, O.H., Na, I.S. and Koh, J.G. (2024). Exploring advanced machine
learning techniques for swift legume disease detection.
Legume Research. 47(7): 1221-1227. doi: 10.18805/LRF-789.
Dataset Link: https://www.kaggle.com/datasets/shashwatwork/identifying-disease-in-tea-leafs?select=tea+sickness+
dataset. (Accessed on 06/05/2024).
Datta, S. and Gupta, N. (2023). A novel approach for the detection
of tea leaf disease using deep neural network. Procedia
Computer Science. 218: 2273-2286. https://doi.org/10.1016/j.procs.2023.01.203.Deka, N. and Goswami, K. (2020). EcOpen asset ↗Kaggle · shashwatwork/identifying-disease-in-tea-leafspdf-raw-page:8 lines:1-75Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tea leaf diseases seriously affect its yield and quality, and consequently there is an urgent need for intelligent detection methods with high precision and edge deployment capabilities. To address low detection accuracy in complex backgrounds, overfitting due to limited data, and redundant parameters for existing methods, this paper proposes an improved lightweight detection model FCHE-YOLO based on the YOLO11, which aims to achieve rapid and accurate identification of tea leaf disease combining low altitude remote sensing with unmanned aerial vehicle (UAV). The model has made three key optimizations in the structure: Introduce the self-developed lightweight backbone module FC_C3K2, which significantly reduces computation and parameter count while enhancing the robustness of the model to complex scenarios; construct an efficient feature fusion structure HSFPN, optimizing multi-scale information integration and compressing model volume; design the detection head Efficient Head, integrating group convolution and lightweight attention mechanism to improve detection accuracy and suppress overfitting. The experimental results from the self built tea gardens show that the FCHE-YOLO improves the average accuracy (mAP) from 94.1% to 98.1% compared to the benchmark model YOLO11, with an improvement of 4.0 percentage points. Meanwhile, the inference speed of the model increases from 43.3 FPS to 47.5 FPS, with an increase of 9.0%, meeting the real-time detection requirements. More importantly, by network structure optimization, the model's computational complexity is significantly reduced: The floating-point operations per second (FLOPs) decreases from 6.4 G to 4.2 G, with a decrease of 34.3%, and the parameter count decreases from 2.59 M to 1.46 M, with the compression rate reaching 38.9%, which makes the model more suitable for deployment on resource-constrained UAV edge devices. The final test show that the FCHE-YOLO significantly reduces the missed-detection rate, owns better detection accuracy and deployment practicality, and is suitable for real-time monitoring scenarios of tea leaf diseases with UAVs.
Why it matches plant phenotyping methods茶葉の病害状態をUAV画像から検出する軽量深層学習手法を開発・評価しており、植物病害表現型の取得が中心的な技術貢献である。
abstractthis paper proposes an improved lightweight detection model FCHE-YOLO based on the YOLO11, which aims to achieve rapid and accurate identification of tea leaf disease combining low altitude remote sensing with unmanned aerial vehicle (UAV).
Reproduction assets foundThe paper's Data Availability Statement points to a public figshare repository containing the study's relevant data (UAV tea leaf disease imagery/dataset). No separate author code deposit is stated.Dataset · publicAll relevant data for this study are publicly available from the figshare repository (https://figshare.com/s/316807b23895bc3ba3ae).Open asset ↗figsharehtml-lines:693-736Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye's parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates' "multi-scale parallelism-global modulation-long-range integration," multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network's backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems.
Why it matches plant phenotyping methods茶樹の干ばつストレス状態を画像から検出する改良YOLO手法を開発・検証しており、植物状態の取得・推定が研究の中心である。
abstractthis study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision.
Reproduction assets foundThe paper's Data Availability Statement states the original code is openly available in IEEE DataPort at the allowed DOI URL, making the authors' analysis code a paper-specific public asset.Code · publicThe original code presented in the study are openly available in IEEE DataPort at https://dx.doi.org/10.21227/v32y-mv49.Open asset ↗IEEE DataPort · 10.21227/v32y-mv49html-lines:829-851Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
In this study, we present a combined image dataset created from two distinct plant species: Hibiscus and Tea leaf. The dataset consists of high-resolution images of leaves from both species, captured using a SONY α7 II DSLR camera and a OnePlus 7T lubricant Tea Leaf dataset includes images categorized into five disease classes: Algal Leaf Spot, Brown Blight, Grey Blight, Red Leaf Spot, and Healthy, while the Hibiscus Leaf dataset includes images labeled across eight conditions, including citrus spot, fungal infection, mild edge damage, and healthy foliage. To ensure balanced representation and address class imbalances, extensive data augmentation techniques-such as flipping, rotation, zooming, shifting, noise addition, and brightness adjustment-were applied, resulting in a total of 1,413 combined original images and 13,000 augmented images. The ConvNextTiny deep learning model was fine-tuned on this combined dataset to classify the various leaf conditions, achieving an overall accuracy of 96%. This demonstrates the model's robust performance and high discriminatory power across the diverse set of leaf diseases and conditions. This experiment highlights the utility of combining multiple plant species into a single dataset and utilizing a lightweight yet effective model like ConvNextTiny for plant disease classification. The resulting dataset, along with the model and training scripts, is publicly available to facilitate further research in plant pathology, computer vision, and smart farming applications, enabling more accurate and efficient early-stage disease detection for both Hibiscus and Tea plants.
Why it matches plant phenotyping methods植物葉の病害・健全状態を画像から分類するデータセットを構築し、分類モデルで性能評価しているため、植物フェノタイピング手法・ベンチマークが中心です。
abstractwe present a combined image dataset created from two distinct plant species: Hibiscus and Tea leaf
Reproduction assets foundThe paper's combined Hibiscus and Tea leaf disease image dataset is publicly deposited on Mendeley Data (DOI 10.17632/5bzy89brkv.4), and the authors' augmentation/training scripts are on a public GitHub repository; both are paper-specific, public, and directly actionable.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/5bzy89brkv.4
Direct URL to data: https://data.mendeley.com/datasets/5bzy89brkv/4Open asset ↗Mendeley Data · 10.17632/5bzy89brkv.4lines:1-46Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Tea flowers play a crucial role in taxonomic research and hybrid breeding of tea plants. As traditional methods of observing tea flower traits are labor-intensive and inaccurate, TflosYOLO and Tea Flowering Stage Classification (TFSC) models were proposed for tea flowering quantification, which enable the estimation of flower count and flowering period. In this study, a highly representative and diverse dataset was constructed by collecting flower images from 29 tea accessions in 2 years. Based on this dataset, the TflosYOLO model was built on the YOLOv5 architecture and enhanced with the Squeeze-and-Excitation (SE) network, Adaptive Rectangular Convolution, and Attention Free Transformer, which is the first model to offer a viable solution for detecting and counting tea flowers. The TflosYOLO model achieved a mean Average Precision at 50% IoU (mAP50) of 0.844, outperforming YOLOv5, YOLOv7, and YOLOv8. Furthermore, the TflosYOLO model was tested on 31 datasets encompassing 26 tea accessions and five flowering stages, demonstrating high generalization and robustness. The correlation coefficient (R 2 ) between the predicted and actual flower counts was 0.964. Additionally, the TFSC model-a seven-layer neural network-was designed for the automatic classification of the flowering period. The TFSC model was evaluated for 2 years and achieved an accuracy of 0.738 and 0.899. Using the TflosYOLO+TFSC model, the tea flowering dynamics were monitored, and the changes in flowering stages were tracked across various tea accessions. The framework provides crucial support for tea plant breeding programs and the phenotypic analysis of germplasm resources.
Why it matches plant phenotyping methods茶花画像から花数と開花期を推定するモデルを開発・検証しており、植物表現型の取得・抽出手法が研究の中心である。
abstractTflosYOLO and Tea Flowering Stage Classification (TFSC) models were proposed for tea flowering quantification, which enable the estimation of flower count and flowering period.
Reproduction assets foundThe paper's data availability statement explicitly deposits the tea flower datasets and models in a public GitHub repository (sufie-mi/tea-flower-model), which directly supports this paper's tea flower phenotyping measurements and models. The labelImg repository is a generic third-party annotation tool, not a paper-ownDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/sufie-mi/tea-flower-model .Open asset ↗https://github.com/sufie-mi/tea-flower-model · tea-flower-modellines:764-781Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
To address the challenges of variable target scale, complex background, blurred image, and serious occlusion in the yield detection of Yunnan large-leaf tea tree, this study proposes a deep learning network DE-YOLOv13-S that integrates the visual mechanism of primates. DynamicConv was used to optimize the dynamic adjustment process of the effective receptive field and channel the gain of the primate visual system. Efficient Mixed-pooling Channel Attention was introduced to simulate the observation strategy of 'global gain control and selective integration parallel' of the primate visual system. Scale-based Dynamic Loss was used to simulate the foveation mechanism of primates, which significantly improved the positioning accuracy and robustness of Yunnan large-leaf tea tree yield detection. The results show that the Box Loss, Cls Loss, and DFL Loss of the DE-YOLOv13-S network decreased by 18.75%, 3.70%, and 2.54% on the training set, and by 18.48%, 14.29%, and 7.46% on the test set, respectively. Compared with YOLOv13, its parameters and gradients are only increased by 2.06 M, while the computational complexity is reduced by 0.2 G FLOPs, precision, recall, and mAP are increased by 3.78%, 2.04% and 3.35%, respectively. The improved DE-YOLOv13-S network not only provides an efficient and stable yield detection solution for the intelligent management level and high-quality development of tea gardens, but also provides a solid technical support for the deep integration of bionic vision and agricultural remote sensing.
Why it matches plant phenotyping methods茶樹の収量を画像から検出する深層学習モデルを開発・評価しており、植物形質の取得手法が研究の中心である。
abstractthis study proposes a deep learning network DE-YOLOv13-S
Reproduction assets foundThe paper's Data Availability Statement explicitly states the original code is openly available in IEEE DataPort with a DOI link, making the authors' analysis code a public, paper-specific asset.Code · publicData Availability Statement: The original code presented in the study are openly available in IEEE
DataPort at https://dx.doi.org/10.21227/drd6-b843.Open asset ↗10.21227/drd6-b843pdf-page:18 lines:1-57Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Tea is an extremely popular beverage around the world due to its exquisite taste and flavor. Unfortunately, it is prone to different types of illness, which can reduce the amount of harvest along with its standard. Among these, leaf infections are a serious concern since they negatively affect the quality of tea leaves. As a consequence, tea producers often encounter a great deal of obstacles and financial losses. Keeping this in mind, a thorough dataset has been compiled, which contains 5278 images of diseased and healthy leaves. The purpose of this dataset is to improve our knowledge of how these conditions impact cultivating tea plants and tea production. These images are collected from a variety of locations and meteorological circumstances, which provide an extensive knowledge of the disease patterns unique to tea leaves. The pictures have been captured with the help of some high-quality devices from different angles and in high resolution to ensure the standard and increase the usability of the dataset. Rigorous steps were followed when preparing the dataset that would be of great help in building a precise artificial intelligence model. The dataset carefully determined and classified six tea leaf diseases: Tea algal leaf spot, Brown Blight, Gray Blight, Helopeltis, Red spider, and Green mirid bug. There is one more class in the dataset containing images of healthy leaves. These illnesses are known for their devastating impact on tea leaves. An automated disease classification system can be made utilizing deep learning techniques that will enable estate managers to take timely action to stop the spread of the disease, and this meticulously collected dataset will immensely help to train that model.
Why it matches plant phenotyping methods茶葉の健全・病害状態を画像で収集・分類した再利用可能なデータセットであり、植物病害状態の画像ベース表現型計測を中心とする。
abstracta thorough dataset has been compiled, which contains 5278 images of diseased and healthy leaves
Reproduction assets foundThe paper's own teaLeafBD image dataset (5278 tea leaf images, 7 classes) is publicly deposited on Mendeley Data with an explicit DOI and direct URL, making it a paper-specific, publicly actionable asset.Dataset · public24.30795976, longitude: 91.73760171),
7.
Finlay Tea Estate in Sreemangal (latitude: 24.30357177, longitude: 91.74245382),
8.
Jungle Bari Tea Estate in Sreemangal (latitude: 24.25329433, longitude: 91.77409053)
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/744vznw5k2.4
Direct URL to data: https://data.mendeley.com/datasets/744vznw5k2/4
Related research article
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Value of the Data
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The dataset was collected from tea harvesting areas in Bangladesh, which is one of the top tea-producing regions in the world, supplying tea globally. A total of 5278 images were captured by the camera from eight tea gardens, and they were annotated by human experts.
•Open asset ↗Mendeley Data · 10.17632/744vznw5k2.4lines:1-60Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Tea ( Camellia sinensis L.) disease detection in complex field conditions faces significant challenges due to the scarcity of labeled data. While current mainstream visual deep learning algorithms depend on large-scale curated datasets. To address this, we propose a novel few-shot end-to-end detection network called MAF-MixNet that achieves robust detection with minimal annotation data. The network effectively overcomes the bottleneck of insufficient feature extraction under limited samples of existing methods, through the design of a mixed attention branch (MA-Branch) and a multi-path feature fusion module (MAFM). The former extracts contextual features, while the latter combines and enhances the local and global features. The entire model uses a two-stage paradigm to pretrain on public datasets and fine-tune on balanced subset datasets, including novel tea disease classes, anthracnose, and brown blight. Comparative experiments with six models on four evaluation metrics verified the advancement of our model. At 5-shot, MAF-MixNet achieves scores of 62.0%, 60.1%, and 65.9% in precision, nAP50, and F1 score, respectively, significantly outperforming other models. Similar superiority is achieved in the 10-shot scenario, where nAP50 is 73.8%. Our model maintains a certain computational efficiency and achieves the second fastest inference speed at 11.63 FPS, making it viable for real-world deployment. The results confirm MAF-MixNet's potential to enable cost-effective, intelligent disease monitoring in precision agriculture.
Why it matches plant phenotyping methods植物病害の症状を画像から検出する新規深層学習手法を開発し、複数モデルとの比較検証を行っているため、植物フェノタイピング手法が中心である。
abstractwe propose a novel few-shot end-to-end detection network called MAF-MixNet
Reproduction assets foundThe authors openly released the annotated leaf disease detection dataset (718 JPEG images with XML annotations of tea and cotton diseases) used in this study via Hugging Face Datasets with a DOI, making it a public, paper-specific, actionable asset.Dataset · publicThe leaf disease detection dataset supporting the findings of this
study is openly available in Hugging Face Datasets. This dataset contains 718 annotated images
of tea and cotton leaves across four disease categories (Tea Anthracnose Disease, Tea Brown Blight
Disease, Cotton Fusarium Wilt Disease, and Cotton Powdery Mildew), formatted as JPEG with
accompanying XML metadata.Open asset ↗Hugging Face Datasetspdf-page:26 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The economic development of many countries largely depends on tea plantations that suffer from diseases adversely affecting their productivity and quality. This study presents a high-resolution dataset aimed at advancing precision agriculture for managing tea garden diseases. The size of the dataset is 3960 images and pixel dimension is (1024 × 1024) of the images were collected by using smartphones. This dataset contains detailed images of Tea Leaf Blight, Tea Red Leaf Spot and Tea Red Scab maladies inflicted on tea leaves as well as environmental statistics and plant health. The images were captured and stored in JPG format. The main aim of this dataset is to provide tool for detection and classification of different types of tea garden disease. Applying this dataset will enable the development of early detection systems, best-practice care regimens, and enhanced general garden upkeep. A range of images presenting the most prevalent diseases afflicting tea plants are paired with images of healthy leaves to provide a comprehensive overview of all the circumstances that can arise in a tea plantation. Therefore, it can be used to automate diseases tracking, targeted pesticide spraying, and even the making of smart farm tools with development of smart agricultural tools hence enhancing sustainability and efficiency in tea production. This dataset not only provides a strong foundation for applying precision techniques in tea cultivation in agriculture, but also can become an invaluable asset to scientists studying the issues of tea production.
Why it matches plant phenotyping methods茶葉の病害状態を画像で記録したデータセットであり、植物病害の画像ベース表現型評価を支えるデータ資源が中心です。
abstractThis study presents a high-resolution dataset aimed at advancing precision agriculture for managing tea garden diseases.
Reproduction assets foundThe paper is a Data in Brief article describing a tea leaf disease image dataset (3960 original images, 4000 augmented) publicly deposited on Mendeley Data with an explicit DOI and direct URL. This is a paper-specific, public, actionable plant-phenotyping asset (plant images used for disease classification phenotyping)Dataset · publicar, Sylhet, Bangladesh. The project was conducted under the supervision of an expert from Bangladesh's Ministry of Agriculture.
Data source location
Location: Moulvi Bazar tea garden,Sylhet
Country: Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/tt2smzrzrs.4
Direct URL to data: https://data.mendeley.com/datasets/tt2smzrzrs/4
Related research article
None
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Value of the Data
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Tea is a major global agricultural crop with economic implications as well as cultural significance. This drink is famous for diverse tastes and health benefits. In many civilizations, tea remains their main beverage [ 1 ]. There are several countries that supply most oOpen asset ↗Mendeley Data · 10.17632/tt2smzrzrs.4lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Addressing the challenge of identifying tea plant diseases against the complex background of tea gardens, this study proposes the ECA-ResNet50 model. By optimizing the ResNet50 architecture, adopting a multi-layer small convolution kernel strategy to enhance feature extraction capabilities, and introducing the ECA attention mechanism to focus on key features, the model achieves a 93.06% accuracy rate in tea disease identification, representing a 3.18% improvement over the original model, demonstrating industry-leading performance advantages. This model not only accurately identifies tea diseases in gardens but also possesses excellent generalization capabilities, performing outstandingly on datasets of other plant categories. These results indicate that ECA-ResNet50 can effectively mitigate the interference of complex backgrounds and precisely recognize tea disease targets.
Why it matches plant phenotyping methods茶葉の病害を画像から識別する深層学習モデルを開発し、精度比較と汎化性能を評価しており、植物病害状態の取得・推定手法が中心である。
abstractthis study proposes the ECA-ResNet50 model.
Reproduction assets foundThe paper's tea disease image dataset (885 images, seven disease types plus healthy leaves) is a public Kaggle dataset explicitly cited by the authors with URL. No author analysis code or trained model checkpoints are stated as publicly available; the data availability statement only offers raw data on request.Dataset · publica wide variety of diseases, totaling approximately over 140 types, which are widely distributed across various parts of the tea plants, including leaves, stems, roots, and flowers ( Chen, 2022 ). Given the limitations of experimental conditions, this study collected a total of 885 images of tea diseases through search engines ( https://www.kaggle.com/datasets/shashwatwork/identifying-disease-in-tea-leafs ). After meticulous identification and classification by authoritative experts, these images were categorized into seven distinct types of leaf diseases, as well as healthy leaves. The seven disease types are algae leaf spot, anthracnose, bird’s eye spot, cloud blotch, gray spot, red leaf spOpen asset ↗Kaggle · shashwatwork/identifying-disease-in-tea-leafslines:33-59Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
To address the issues of low accuracy and slow response speed in tea disease classification and identification, an improved YOLOv7 lightweight model was proposed in this study. The lightweight MobileNeXt was used as the backbone network to reduce computational load and enhance efficiency. Additionally, a dual-layer routing attention mechanism was introduced to enhance the model's ability to capture crucial details and textures in disease images, thereby improving accuracy. The SIoU loss function was employed to mitigate missed and erroneous judgments, resulting in improved recognition amidst complex image backgrounds.The revised model achieved precision, recall, and average precision of 93.5%, 89.9%, and 92.1%, respectively, representing increases of 4.5%, 1.9%, and 2.6% over the original model. Furthermore, the model's volum was reduced by 24.69M, the total param was reduced by 12.88M, while detection speed was increased by 24.41 frames per second. This enhanced model efficiently and accurately identifies tea disease types, offering the benefits of lower parameter count and faster detection, thereby establishing a robust foundation for tea disease monitoring and prevention efforts.
Why it matches plant phenotyping methods茶葉の病害画像から病状を推定するYOLOv7改良モデルの開発・性能評価が中心であり、植物の病害状態を対象とする画像ベース表現型計測に該当する。
abstractan improved YOLOv7 lightweight model was proposed in this study
Reproduction assets foundThe paper's data availability statement says all data and code are available on GitHub and provides an authors' public URL for the improved YOLOv7 code, which matches an allowed URL. The tea disease image dataset itself is referenced but no explicit dataset URL is supplied, so only the code asset qualifies.Code · publicAll data generated or analysed during this study are available in the Github repository. Links to the code and datasets are provided in the below hyperlinked text. Code of Improved YOLOv7 project: https://github.com/anqi99/yolov7.gitOpen asset ↗https://github.com/anqi99/yolov7.gitlines:192-263Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
To overcome the constraints associated with conventional approaches used in the classification and detection of tea diseases, which are characterized by their limited accuracy and sluggish responsiveness, this study introduces an enhanced YOLOv7 lightweight model algorithm integrated with MobileNeXt. This refinement not only bolsters the model's capacity for extracting and processing features but also effectively lightens the computational load, expedites recognition, and integrates a dual-layer routing attention mechanism visual converter to enhance the capture of crucial details and textures within disease images. Consequently, these enhancements lead to improved model performance and computational efficiency, ensuring precise and rapid identification of tea diseases. Furthermore, this model incorporates the more appropriate SIoU as the loss function, mitigating losses, minimizing omissions, and reducing misclassifications, thus resulting in superior recognition, even in complex image backgrounds. Based on the training outcomes, the enhanced model attains Precision, Recall and mean Average Precision scores of 93.5%, 89.9%, and 92.1%, respectively, marking substantial enhancements of 5.06%, 2.16%, and 2.91% compared to the original YOLOv7 model. Additionally, the model's size is reduced by 19.12%, and its detection speed accelerates by 11.13%. This improved model excels in accurately and expediting.
Why it matches plant phenotyping methods茶の病害画像から病気を分類・検出するYOLOv7改良モデルの開発と性能評価が中心であり、植物の病害状態を画像ベースで推定するフェノタイピング手法に該当する。
abstractthis study introduces an enhanced YOLOv7 lightweight model algorithm integrated with MobileNeXt
Reproduction assets foundThe paper's Data Availability statement links a public GitHub repository containing the authors' code and tea disease image dataset used to train and evaluate the improved YOLOv7 model.Code · public359 Data Availability
360 Our relevant data are allowed by Yunnan Agricultural University and related bases. All data
361 generated or analysed during this study are available in the Github repository. Links to the code
362 and datasets are provided in the below hyperlinked text. Code and dataset of Improved YOLOv7
363 project: https://github.com/anqi99/yolov7.git.
364
365
366
367 References
368 1. Xue, Z., Xu, R., Bai, D. & Lin, H. Yolo-Tea: A Tea Disease Detection Model Improved by Yolov5.
369 Forests. 14, 415 (2023). https://doi.org/10.3390/f14020415
370 2. Lee, L. K. & Foo, K. Y. Recent Advances On the Beneficial Use and Health Implications of Pu-Erh
12Open asset ↗https://github.com/anqi99/yolov7.gitpdf-layout-page:14 lines:1-46Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate detection of tea diseases is essential for optimizing tea yield and quality, improving production, and minimizing economic losses. In this paper, we introduce TeaDiseaseNet, a novel disease detection method designed to address the challenges in tea disease detection, such as variability in disease scales and dense, obscuring disease patterns. TeaDiseaseNet utilizes a multi-scale self-attention mechanism to enhance disease detection performance. Specifically, it incorporates a CNN-based module for extracting features at multiple scales, effectively capturing localized information such as texture and edges. This approach enables a comprehensive representation of tea images. Additionally, a self-attention module captures global dependencies among pixels, facilitating effective interaction between global information and local features. Furthermore, we integrate a channel attention mechanism, which selectively weighs and combines the multi-scale features, eliminating redundant information and enabling precise localization and recognition of tea disease information across diverse scales and complex backgrounds. Extensive comparative experiments and ablation studies validate the effectiveness of the proposed method, demonstrating superior detection results in scenarios characterized by complex backgrounds and varying disease scales. The presented method provides valuable insights for intelligent tea disease diagnosis, with significant potential for improving tea disease management and production.
Why it matches plant phenotyping methods茶葉画像から病害状態を推定する新規画像解析手法を開発し、比較実験とアブレーションで検証しており、植物病害フェノタイピングが中心です。
abstractwe introduce TeaDiseaseNet, a novel disease detection method designed to address the challenges in tea disease detection
Reproduction assets foundThe article's data availability statement provides a public link to the raw tea disease dataset (776 annotated images used for TeaDiseaseNet training/evaluation). The other allowed URL is a cited prior-work reference, not a paper-specific asset.Dataset · publicons can improve the practicality and effectiveness of tea disease detection systems.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. The raw data can be accessed at the following link: https://www.jianguoyun.com/p/DRwyMxYQqJnmCxiGl5IFIAA .
Author contributionsOpen asset ↗lines:321-358Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Drought tolerance and quality stability are important indicators to evaluate the stress tolerance of tea germplasm resources. The traditional screening method of drought resistant germplasm is mainly to evaluate by detecting physiological and biochemical indicators of tea plants under drought stresses. However, the methods are not only time consuming but also destructive. In this study, hyperspectral images of tea drought phenotypes were obtained and modeled with related physiological indicators. The results showed that: (1) the information contents of malondialdehyde, soluble sugar and total polyphenol were 0.21, 0.209 and 0.227 respectively, and the drought tolerance coefficient (DTC) index of each tea variety was between 0.069 and 0.81; (2) the comprehensive drought tolerance of different varieties were (from strong to weak): QN36, SCZ, ZC108, JX, JGY, XY10, QN1, MS9, QN38 , and QN21 ; (3) by using SVM, RF and PLSR to model DTC (drought tolerance coefficient) data, the best prediction model was selected as MSC-2D-UVE-SVM (R 2 = 0.77, RMSE = 0.073, MAPE = 0.16) for drought tolerance of tea germplasm resources, named Tea-DTC model. Therefore, the Tea-DTC model based on hyperspectral machine-learning technology can be used as a new screening method for evaluating tea germplasm resources with drought tolerance.
Why it matches plant phenotyping methods茶樹の乾燥耐性という植物状態をハイパースペクトル画像と機械学習で推定するモデルを開発し、従来の生理・生化学指標に代わるスクリーニング手法として性能評価しているため、フェノタイピング手法が中心である。
abstractIn this study, hyperspectral images of tea drought phenotypes were obtained and modeled with related physiological indicators.
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。Supplement · publicThe content data of physiological and biochemical components of tea leaves measured with the kit are shown in supplementary Table 1Open asset ↗lines:322-334Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity, and so on. Therefore, it is very necessary to find a method that could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.
Why it matches plant phenotyping methods茶葉の病斑・虫害斑という植物の病害状態を、Mask R-CNNによる分割とF-RNetによる認識で抽出・評価する画像解析手法が研究の中心であるため。
abstractwe proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet.
Reproduction assets foundThe paper's data availability statement points to two GitHub repositories. The F-RNet repository (github.com/Du553/F-RNet) is the authors' own public code implementing the paper's four-channeled residual network analysis and qualifies as a paper-specific asset. The matterport/Mask_RCNN link is a generic third-partyMaskCode · publicThe dataset and source code has been uploaded to GitHub: https://github.com/matterport/Mask_RCNN , https://github.com/Du553/F-RNet .Open asset ↗Du553/F-RNetlines:469-489Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract At the moment, there are increasing trends of using deep learning for plant diseases detection. However, their implementations may be difficult in developing countries due to several reasons. First, existing deep learning models are usually trained with images with adequate resolutions. In developing countries however, with limited internet connection, models that would perform well even when data with low resolution are used are needed. Secondly, the generated models are large. Hence, most deep learning based applications are available on-line. Unfortunately, the trend for new deep learning architectures are either have larger models or require a heavy memory usage. So, models with smaller size would be preferred. In this paper, we evaluate various existing deep learning models for plant diseases detection when low resolution data are used. They are: VGGNet, AlexNet, Resnet, Xception, and MobileNet. Our focus is deep convolutional neural network (DCNN) which is commonly applied for image data. We also propose a new DCNN architecture with two branches of concatenated residual networks. It is well known that the deeper the networks the better performance of DCNN. However, DCNN with very deep networks and large number of training parameters is prone to vanishing gradient problems. One solutions for that is to apply residual networks as branches to DCNN. While it is found that increasing the branch of the networks benefit the performance, larger memory are required to train the networks. So, we apply two concatenated residual networks only. We called it Compact Networks (ComNet). We compare our method other with six popular CNN architectures. We evaluate the performance on the PlantVillage dataset and our own dataset. We collected images of tea leaves which consist of 6 classes: 5 classes of diseases that are commonly found in Indonesia and a healthy class. Our experiments show that our method is generally better than referenced DCNN networks.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を提案し、複数モデルおよびデータセットで性能比較・評価しており、植物状態の取得手法が中心である。
abstractWe also propose a new DCNN architecture with two branches of concatenated residual networks.
Reproduction assets foundThe paper evaluates its ComNet and reference DCNN architectures on a subset of the public PlantVillage dataset (Apple, Corn, Potato; 9,176 images), which the authors explicitly link to a public GitHub repository. The authors' own tea disease dataset is not public and requires contacting the corresponding author. No作者-пDataset · publicoding; FF and VPR validated the dataset. All authors are contributed to the data
collections. All authors read and approved the final manuscript.
Funding
This work is partially funded by INSINAS grant from the Indonesian Ministry of Research, Technology, and Higher
Education.
Availability of data and materials
The Plantvillage: https://github.com/spMohanty/PlantVillage-Dataset. The tea dataset that are used during the current
study are not publicly available due to it is in the process of agreement between Research Center for Informatics and
Research Institute for Tea and Cinchona but are available from the corresponding author on reasonable request.
Competing interests
The authors declare tOpen asset ↗spMohanty/PlantVillage-Datasetpdf-raw-page:19 lines:1-50Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
It is generally feasible to classify different species of vegetation based on remotely sensed images, but identification of different sub-species or even cultivars is uncommon. Tea trees ( Camellia sinensis L.) have been proven to show great differences in taste and quality between cultivars. We hypothesize that hyperspectral remote sensing would make it possibly to classify cultivars of plants and even to estimate their taste-related biochemical components. In this study, hyperspectral data of the canopies of tea trees were collected by hyperspectral camera mounted on an unmanned aerial vehicle (UAV). Tea cultivars were classified according to the spectral characteristics of the tea canopies. Furthermore, two major components influencing the taste of tea, tea polyphenols (TP) and amino acids (AA), were predicted. The results showed that the overall accuracy of tea cultivar classification achieved by support vector machine is higher than 95% with proper spectral pre-processing method. The best results to predict the TP and AA were achieved by partial least squares regression with standard normal variant normalized spectra, and the ratio of TP to AA-which is one proven index for tea taste-achieved the highest accuracy ( R CV = 0.66, RMSE CV = 13.27) followed by AA ( R CV = 0.62, RMSE CV = 1.16) and TP ( R CV = 0.58, RMSE CV = 10.01). The results indicated that classification of tea cultivars using the hyperspectral remote sensing from UAV was successful, and there is a potential to map the taste-related chemical components in tea plantations from UAV platform; however, further exploration is needed to increase the accuracy.
Why it matches plant phenotyping methodsUAV搭載ハイパースペクトル画像を用いて茶樹キャノピーから品種および生化学的形質を推定する手法が研究の中心であり、単なる生物学的実験の routine measurement ではない。
abstracthyperspectral data of the canopies of tea trees were collected by hyperspectral camera mounted on an unmanned aerial vehicle (UAV).
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。Dataset · publicHyperspectral Images obtained by UAV, measured TP and AA content were used as raw data and uploaded to Figshare:
https://figshare.com/articles/spectra_data_of_tea_plantation/5844801 .
Fei, Teng (2018): Spectra data of tea plantation. figshare. Figure. https://doi.org/10.6084/m9.figshare.5844801.v1 .Open asset ↗figshare · 10.6084/m9.figshare.5844801.v1lines:386-480