High-resolution dataset for tea garden disease management: Precision agriculture insights

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.

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Publication Details

Journal
Data in Brief
Published
2025-02-12
DOI
https://doi.org/10.1016/j.dib.2025.111379
Citations
1
Primary Topic
Plant Pathogens and Fungal Diseases
Type
article
Field-Weighted Citation Impact
1.15
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High-resolution dataset for tea garden disease management: Precision agriculture insights

Amatul Bushra Akhi, Mohammad Ahmad, Sajib Bormon, Rimon et al.
1 citations
Data in Brief
Plant Pathogens and Fungal Diseases
1.15
article

High-resolution dataset for tea garden disease management: Precision agriculture insights

Amatul Bushra Akhi, Mohammad Ahmad, Sajib Bormon, Rimon, Sohanur Rahman Sohag
article en
1 citations

Abstract

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.

Data in BriefVol. 59
Daffodil International University (BD)
Zero hunger
Openalex Percentile: Top 28%
Plant Pathogens and Fungal Diseases
1.15
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High-resolution dataset for tea garden disease management: Precision agriculture insights — Amatul Bushra Akhi, Mohammad Ahmad, et al. · Data in Brief (2025) | TGRS Research Map | TGRS