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.

本数据集包含枣、阳桃、番石榴叶片的原始图像1602张与数据增强图像7885张，所有样本均标注为健康与患病两类。本数据集专为开发训练用于热带果树作物早期病害检测的机器学习模型而构建，涵盖多种叶片病害类型，可为植物病理学与农业技术领域的研究人员提供宝贵的研究资源，同时为推动辅助作物管理、优化可持续农业实践的自动化系统提供坚实基础。

枣叶片：

枣叶片原始数据：
瑕疵枣叶：124张
健康枣叶：227张
虫害取食叶：177张
卷叶病叶：33张

枣叶片数据增强数据：
瑕疵枣叶：620张
健康枣叶：1010张
虫害取食叶：885张
卷叶病叶：165张

阳桃叶片：

阳桃叶片原始数据：
瑕疵阳桃叶：183张
健康阳桃叶：298张
虫害取食叶：95张
阳桃老黄化叶：68张

阳桃叶片数据增强数据：
瑕疵阳桃叶：915张
健康阳桃叶：1490张
虫害取食叶：475张
阳桃老黄化叶：340张

番石榴叶片：

番石榴叶片原始数据：
瑕疵番石榴叶：158张
真菌病害叶：39张
番石榴斑点病叶：（原文存在排版疏漏）
健康番石榴叶：200张

番石榴叶片数据增强数据：
瑕疵番石榴叶：790张
真菌病害叶：195张
番石榴斑点病叶：（原文存在排版疏漏）
健康番石榴叶：1000张

研究目的：
本研究旨在利用先进图像处理与机器学习技术，构建一套高效精准的枣、阳桃及番石榴叶片病害检测系统。通过早期识别作物病害，本研究以期优化作物管理、降低农业生产损失并推广可持续农业实践。

搜集汇总

背景与挑战

背景概述

该数据集包含枣、杨桃和番石榴叶片的1602张原始图像和7885张增强图像，分为健康和患病类别，专门用于机器学习模型开发以检测热带水果作物的早期病害。它涵盖了多种叶片病害类型，如缺陷、昆虫取食和真菌感染等，旨在通过图像处理和机器学习技术提升作物管理效率，减少农业损失并促进可持续农业实践。