Tea Leaf Diseases Dataset: Towards Accurate Field Diagnosis Using Image-Based Detection

The dataset comprises a total of 31,265 images, including 4,009 raw images and 27,256 augmented images, distributed across six categories. The raw dataset captures natural variability in leaf conditions under diverse environmental settings, while the augmented dataset enhances this variability by applying techniques such as rotation, scaling, flipping, and brightness adjustments to improve machine learning model generalizability.

Category Distribution:

Sunlight Scorching:
Raw: 1,712 images
Augmented: 4,009 images
Total: 5,721 images

Red Spider:
Raw: 410 images
Augmented: 4,746 images
Total: 5,156 images

Red Rust:
Raw: 184 images
Augmented: 4,849 images
Total: 5,033 images

Heliopeltis:
Raw: 281 images
Augmented: 4,825 images
Total: 5,106 images

Thrips:
Raw: 176 images
Augmented: 4,881 images
Total: 5,057 images

Normal (Healthy):
Raw: 1,246 images
Augmented: 3,946 images
Total: 5,192 images

Key Features:
Diversity: The dataset captures variability in backgrounds, lighting, and growth stages, ensuring robust training and testing data.
Augmentation: Includes 27,256 augmented images, generated using state-of-the-art techniques to simulate real-world variability.
Category Coverage: Spans six distinct leaf conditions—ranging from pest-related damage (e.g., Red Spider, Heliopeltis) to environmental stress (e.g., Sunlight Scorching) and healthy leaves.

Purpose:
This dataset is designed to aid in the development of machine learning models capable of:
Automated Leaf Condition Classification: Identifying specific conditions for improved crop health monitoring.
Ecological Research: Supporting biodiversity studies by cataloging leaf conditions.
Agricultural Applications: Enabling early detection of plant stressors, pests, and diseases to enhance sustainable farming practices.
This combined raw and augmented dataset serves as a valuable resource for researchers, ecologists, and machine learning practitioners, contributing to advancements in agriculture, botany, and AI-driven ecological monitoring.

本数据集总计包含31265张图像，其中原始图像4009张、增强图像27256张，共分为6个类别。原始数据集采集了多样环境下叶片状态的自然变异特征；增强数据集则通过旋转、缩放、翻转及亮度调整等技术进一步丰富数据多样性，以提升机器学习模型的泛化能力。

类别分布：

日光灼害（Sunlight Scorching）：原始图像1712张，增强图像4009张，总计5721张。
红蜘蛛（Red Spider）：原始图像410张，增强图像4746张，总计5156张。
红锈病（Red Rust）：原始图像184张，增强图像4849张，总计5033张。
盾蚧（Heliopeltis）：原始图像281张，增强图像4825张，总计5106张。
蓟马（Thrips）：原始图像176张，增强图像4881张，总计5057张。
健康叶片（Normal, Healthy）：原始图像1246张，增强图像3946张，总计5192张。

核心特性：

多样性：本数据集涵盖背景、光照及生长阶段的多重变异，可为模型训练与测试提供鲁棒性极强的数据支撑。
数据增强：包含27256张增强图像，通过前沿技术生成以模拟真实场景下的各类变异。
类别覆盖：涵盖6种典型叶片状态——包括虫害损伤（如红蜘蛛、盾蚧）、环境胁迫（如日光灼害）以及健康叶片。

数据集用途：

本数据集旨在助力开发具备以下功能的机器学习模型：
1. 自动化叶片状态分类：识别特定叶片状态，以优化作物健康监测流程。
2. 生态研究：通过记录叶片状态信息，为生物多样性研究提供数据支持。
3. 农业应用：实现植物胁迫因子、虫害及病害的早期检测，助力可持续农业实践的升级。

这套融合原始与增强图像的数据集，可为研究人员、生态学家及机器学习从业者提供宝贵的科研资源，推动农业、植物学及AI驱动的生态监测领域的技术进步。

搜集汇总

背景与挑战

背景概述

该数据集包含31,265张茶叶病害图像，涵盖6种不同病害类型，通过原始图像和增强技术处理的数据组合，为机器学习模型开发提供多样化的训练资源，主要用于农业病害自动检测和生态研究。