Tropical Fruit Leaf Disease Detection Dataset: Jujube, Star Fruit, and Guava

This dataset comprises original 1602 images & augmentation 7885 images of jujube, star fruit, and guava leaves, categorized into healthy and diseased labels. It is specifically designed for the development and training of machine learning models aimed at early disease detection in tropical fruit crops. The dataset includes various types of leaf diseases, making it a valuable resource for researchers in plant pathology and agricultural technology. It offers a solid foundation for advancing automated systems to aid in crop management and improve sustainable farming practices.

Jujube Leaf:

Jujube Original data:
Defect Jujube leaf:124
Healthy Jujube Leaf:227
Insect Feeding:177
Leaf Curl:33

Jujube Augmentation data:
Defect Jujube leaf:620
Healthy Jujube Leaf:1010
Insect Feeding:885
Leaf Curl:165

Star Fruit Leaf:

Star Fruit Original data:
Defect Star Fruit Leaf:183
Healthy Star Fruit Leaf:298
Insect Feeding:95
Star Fruit old yellow leaf:68

Star Fruit Augmentation data:
Defect Star Fruit Leaf:915
Healthy Star Fruit Leaf:1490
Insect Feeding:475
Star Fruit old yellow leaf:340

Guava Leaf:

Guava Original data:
Defect Guava leaf:158
Fungal leaf: 39
spot disease on Guava leafHealthy Guava leaf:200

Guava Augmentation data:
Defect Guava leaf:790
Fungal leaf: 195
Spot disease on Guava leafHealthy Guava leaf:1000

Purpose:
The purpose of this research is to develop an efficient and accurate system for detecting diseases in jujube, star fruit, and guava leaves using advanced image processing and machine learning techniques. By identifying diseases early, this study aims to enhance crop management, minimize agricultural losses, and promote sustainable farming practices.

本数据集汇集了包括红枣、杨桃和番石榴叶片在内的1602张原始图像及7885张增强图像，并按照健康与病害标签进行分类。该数据集旨在为针对热带水果作物早期病害检测的机器学习模型开发与训练提供专门支持。数据集中包含了多种叶片病害类型，因此对于植物病理学和农业技术领域的研究人员而言，它是一项宝贵的资源。本数据集为推进自动化系统辅助作物管理及优化可持续农业实践奠定了坚实基础。

红枣叶片：
红枣缺陷叶片：124
健康红枣叶片：227
昆虫侵害：177
叶卷：33

红枣增强数据：
红枣缺陷叶片：620
健康红枣叶片：1010
昆虫侵害：885
叶卷：165

杨桃叶片：
杨桃缺陷叶片：183
健康杨桃叶片：298
昆虫侵害：95
杨桃老黄叶：68

杨桃增强数据：
杨桃缺陷叶片：915
健康杨桃叶片：1490
昆虫侵害：475
杨桃老黄叶：340

番石榴叶片：
番石榴缺陷叶片：158
真菌叶片：39
番石榴叶片斑点病：200

番石榴增强数据：
番石榴缺陷叶片：790
真菌叶片：195
番石榴叶片斑点病：1000

研究目的：
本研究的目的是开发一种高效准确的系统，利用先进的图像处理和机器学习技术对红枣、杨桃和番石榴叶片的病害进行检测。通过早期识别病害，本研究旨在提升作物管理效率，减少农业损失，并促进可持续农业的发展。