Dataset for Detecting Diseases in Sweet Orange Leaves

Abstract:
This dataset is designed for the detection and classification of common diseases in Sweet Orange leaves, specifically foliage damage and mealybug infestations. The dataset was curated to support advancements in agricultural disease detection through machine learning, providing a comprehensive resource for researchers.
Data Summary:
1. Raw Data:
o Total Samples: 3,569
o Disease Dataset: 2,660 samples, including:
 Foliage Damage: 1,200
 Mealybug Infestation: 560
o Fresh Dataset: 909 samples, including:
 Healthy Leaves: 864
 Medium Healthy Leaves: 45
2. Augmented Data:
o Total Samples: 21,409
o Disease Dataset: 15,955 samples, including:
 Foliage Damage: 12,596
 Mealybug Infestation: 3,359
o Fresh Dataset: 5,454 samples, including:
 Healthy Leaves: 5,184
 Medium Healthy Leaves: 270
Purpose:
The Sweet Orange Leaf Disease Detection Dataset is a versatile resource for machine learning applications in agriculture. It is particularly suited for training image-based models, such as Convolutional Neural Networks (CNNs), to:
• Identify leaves affected by diseases like foliage damage or mealybug infestation.
• Assess varying levels of healthiness in fresh leaves.
This dataset facilitates:
• Automated Disease Detection: Streamlining monitoring processes in Sweet Orange cultivation.
• Early Intervention Strategies: Supporting timely responses to prevent crop losses.
• Enhanced Model Development: Integrating image features (e.g., shape, color, texture) with environmental parameters like humidity and temperature to improve predictive accuracy.
By fostering precision agriculture practices, this dataset contributes to sustainable citrus farming and demonstrates the potential of AI-driven solutions for crop health management.

摘要：本数据集旨在检测与分类甜橙叶片中的常见疾病，特别是叶斑病和粉虱侵染。该数据集的编纂旨在通过机器学习促进农业病害检测的进步，为研究人员提供全面的研究资源。
数据概览：
1. 原始数据：
o 总样本数：3,569
o 病害数据集：2,660个样本，包括：
 叶斑病：1,200个样本
 粉虱侵染：560个样本
o 新鲜数据集：909个样本，包括：
 健康叶片：864个样本
 中度健康叶片：45个样本
2. 增强数据：
o 总样本数：21,409
o 病害数据集：15,955个样本，包括：
 叶斑病：12,596个样本
 粉虱侵染：3,359个样本
o 新鲜数据集：5,454个样本，包括：
 健康叶片：5,184个样本
 中度健康叶片：270个样本
目的：甜橙叶片病害检测数据集是农业机器学习应用的多功能资源。特别适用于训练基于图像的模型，如卷积神经网络（CNN），以：
• 识别受叶斑病或粉虱侵染等疾病影响的叶片。
• 评估新鲜叶片的健康程度。
本数据集促进以下方面的发展：
• 自动病害检测：简化甜橙种植的监控流程。
• 早期干预策略：支持及时响应以预防作物损失。
• 模型发展增强：将图像特征（例如形状、颜色、纹理）与环境参数（如湿度、温度）相结合，以提高预测准确性。
通过促进精准农业实践，本数据集对可持续柑橘种植作出贡献，并展示了人工智能驱动解决方案在作物健康管理方面的潜力。