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

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.

Plant phenotyping relevance

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。

Code and data availability

The 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)