ArticleScientific reports2024
Automated detection of selected tea leaf diseases in Bangladesh with convolutional neural network.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Review
- A comprehensive combined dataset onData in brief · 2026Article
- AgriX-SENet: Squeeze-and-Excitation-based deep learning framework for explainable plant disease detection in sustainable agriculture.Frontiers in plant science · 2026Article
- A curated dataset and lightweight deep learning framework for tea leaf disease classification.PloS one · 2026Article
- teaLeafBD: A comprehensive image dataset to classify the diseased tea leaf to automate the leaf selection process in Bangladesh.Data in brief · 2025Article
- Agricultural Pest Management: The Role of Microorganisms in Biopesticides and Soil Bioremediation.Plants (Basel, Switzerland) · 2024Review
- Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.Scientific reports · 2024Article
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Authors and funding
5 authors.
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Abstract
Globally, tea production and its quality fundamentally depend on tea leaves, which are susceptible to invasion by pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element in preventing and controlling the spreading of diseases that hinder yield and quality. Image processing techniques are a sophisticated tool that is rapidly gaining traction in the agricultural sector for the detection of a wide range of diseases with excellent accuracy. This study focuses on a pragmatic approach for automatically detecting selected tea foliage diseases based on convolutional neural network (CNN). A large dataset of 3330 images has been created by collecting samples from different regions of Sylhet division, the tea capital of Bangladesh. The proposed CNN model is developed based on tea leaves affected by red rust, brown blight, grey blight, and healthy leaves. Afterward, the model's prediction was validated with laboratory tests that included microbial culture media and microscopic analysis. The accuracy of this model was found to be 96.65%. Chiefly, the proposed model was developed in the context of the Bangladesh tea industry.
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