ArticleData in brief2025
teaLeafBD: A comprehensive image dataset to classify the diseased tea leaf to automate the leaf selection process in Bangladesh.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Dual Branch Fusion Network for Simultaneous Tea Leaf Disease Diagnosis and Age-Based Quality Grade Evaluation.Plants (Basel, Switzerland) · 2026Article
- A curated dataset and lightweight deep learning framework for tea leaf disease classification.PloS one · 2026Article
- Bridging CNNs and vision transformers for efficient tea leaf phytopathogen diagnosis: GL-MobFormer.Frontiers in plant science · 2026Article
- CS-D: A dataset for disease in tea leaf (Camellia sinensis) from Assam.Data in brief · 2025Article
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8 authors.
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Abstract
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.
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