ArticleScientific reports2023
Tea leaf disease detection and identification based on YOLOv7 (YOLO-T).
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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49 citing papers in PubMed, 185 citations in OpenAlex.
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- Tomato leaf disease and severity prediction using multi-task learning.BMC plant biology · 2026Article
- Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases.Journal of Zhejiang University. Science. B · 2026Article
- Cross-Breed Few-Shot Learning for Pig Detection via Improved YOLOv7 and CycleGAN-Based Sample Generation.Biology · 2026Article
- Integrating morphological and deep learning approaches for the identification of economically important nematode genera in vineyards:Helminthologia · 2026Article
- Deep learning framework for timely detection and classification of chili leaf diseases and pests.Scientific reports · 2026Article
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- DSA-DET: a tea disease detection algorithm based on dynamic spatial pyramid and polarized linear attention.Frontiers in plant science · 2026Article
- Research on the BiSpikeNet classification model for tea plant pests and diseases.Frontiers in plant science · 2026Article
- Focal-HAIN: a lightweight model with adaptive modulation and hierarchical interaction for real-time crop pest and disease monitoring.Frontiers in plant science · 2026Article
- Precise tea leaf disease detection using UAV low-altitude remote sensing and optimized YOLO11 model.PloS one · 2026Article
- From architectural complexity to minimalist optimization: refining microscopic tea disease detection via Opti-YOLOv11n.Frontiers in plant science · 2026Article
- Real-time detection of plant leaf diseases based on improved YOLOv13-LM in complex field environments.Frontiers in plant science · 2026Article
- A multi-scale small object detection framework for tomato leaf pest and disease detection in complex natural environments.Frontiers in plant science · 2026Article
- A robust cross-crop disease detection framework based on SIS-YOLOv11 with climate-adaptive mechanisms.PloS one · 2026Article
- Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs).BioData mining · 2025Article
- Enhanced Detection of Algal Leaf Spot, Tea Brown Blight, and Tea Grey Blight Diseases Using YOLOv5 Bi-HIC Model with Instance and Context Information.Plants (Basel, Switzerland) · 2025Article
- Small Object Detection in Agriculture: A Case Study on Durian Orchards Using EN-YOLO and Thermal Fusion.Plants (Basel, Switzerland) · 2025Article
- teaLeafBD: A comprehensive image dataset to classify the diseased tea leaf to automate the leaf selection process in Bangladesh.Data in brief · 2025Article
- Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.
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