TLD-BD: A Comprehensive Tea Leaf Image Dataset for Leaf Condition Analysis

The GreenPulse-T dataset consists of high-quality images of tea leaves collected from two prominent tea estates in Sreemangal, Moulvibazar, Bangladesh: M.R. Khan Tea Estate (coordinates: 24.27257, 91.75938) and Finlay Tea Estate (coordinates: 24.30334, 91.74249). Data collection took place over the course of eight days, from December 9 to December 16, 2024. The estates were carefully selected to represent diverse cultivation environments, ensuring the dataset includes a wide variety of tea leaf conditions observed in natural settings.

Images in the dataset depict both healthy and diseased tea leaves captured under real-world field conditions. Photographs were taken at different times of day, resulting in variations in lighting, background, and leaf orientation. This variability enhances the dataset’s practical value, making it suitable for robust machine learning and computer vision applications such as classification, object detection, and disease segmentation. The images were compressed to 480x640 pixels at 96 DPI to make them more accessible and manageable for researchers while preserving the key details necessary for disease detection and analysis.

The dataset is categorized into six classes based on expert assessment: Algal Leaf (301 images), Grey Blight (302 images), Healthy Leaf (435 images), Helopeltis (321 images), Looper Infested (332 images), and Red Spider (316 images), totaling 2007 images. Each image is stored in JPG format and organized into folders according to its respective class label.

This dataset is intended to support researchers, developers, and practitioners working in the fields of plant pathology, agriculture, and artificial intelligence. It can be used for academic research, the development of machine learning models, mobile application development for field diagnosis, and other agricultural technology innovations. Researchers using this dataset are kindly requested to cite it appropriately to acknowledge the effort invested in its collection and curation.