GreenPulse-T: A Comprehensive Tea Leaf Image Dataset Featuring Field-Captured Samples Across Different Health and Disease Classes

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.

GreenPulse-T数据集由高质量茶叶图像组成，这些图像采集自孟加拉国毛尔维巴扎尔县斯里曼加尔的两个知名茶园：M.R. Khan茶园（坐标：24.27257，91.75938）和Finlay茶园（坐标：24.30334，91.74249）。数据采集历时8天，时间为2024年12月9日至12月16日。这些茶园经过精心挑选，以代表多样化的种植环境，确保数据集涵盖自然场景中观察到的各类茶叶状态。

数据集中的图像展示了真实田间条件下拍摄的健康及患病茶叶。拍摄时间覆盖一天中的不同时段，导致光照、背景和叶片朝向存在差异。这种多样性提升了数据集的实用价值，使其适用于稳健的机器学习（Machine Learning）和计算机视觉（Computer Vision）应用，例如分类（Classification）、目标检测（Object Detection）和病害分割（Disease Segmentation）。图像被压缩至480×640像素（96 DPI），以提高研究者的可访问性和可管理性，同时保留病害检测与分析所需的关键细节。

基于专家评估，数据集被划分为6个类别：藻斑叶（Algal Leaf，301张）、灰枯病（Grey Blight，302张）、健康叶（Healthy Leaf，435张）、盲蝽象危害叶（Helopeltis，321张）、尺蠖虫害叶（Looper Infested，332张）及红蜘蛛危害叶（Red Spider，316张），总计2007张图像。每张图像以JPG格式存储，并按所属类别标签分文件夹组织。

该数据集旨在为植物病理学（Plant Pathology）、农业和人工智能（Artificial Intelligence）领域的研究者、开发者及从业者提供支持。它可用于学术研究、机器学习模型开发、田间诊断移动应用开发以及其他农业技术创新。恳请使用本数据集的研究者适当引用，以认可其采集与整理过程中投入的努力。

搜集汇总

背景与挑战

背景概述

GreenPulse-T是一个包含2007张茶叶图像的数据集，图像在孟加拉国茶庄园实地采集，涵盖健康和疾病类别（如灰斑病、红蜘蛛等），适用于机器学习和农业技术研究。数据集特点包括真实田间条件下的多样性（如光照和背景变化），支持分类、检测等计算机视觉应用。