Howlader Shakib, Ahamed Md Sabbir, Mojumdar Mayen Uddin, Noori Sheak Rashed Haider, Siddiquee Shah Md Tanvir, Chakraborty Narayan Ranjan
Multidisciplinary Action Research Lab, Department of CSE, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Data Brief. 2025 Jan 31;59:111353. doi: 10.1016/j.dib.2025.111353. eCollection 2025 Apr.
This dataset on eggplant leaf diseases has been meticulously developed to provide a valuable resource for agricultural research and the advancement of automated disease detection systems. It comprises 4,089 high-resolution images of eggplant leaves, systematically categorized into six distinct classes: Healthy Leaf, Insect Pest Disease, Leaf Spot Disease, Mosaic Virus Disease, White Mold Disease, and Wilt Disease. The images were captured using smartphone cameras under controlled conditions with a consistent white background to ensure clarity and uniformity. To reflect real-world agricultural scenarios, data collection was conducted across multiple geographic locations and in varying lighting conditions. This approach enhances the dataset's diversity and applicability. The dataset underwent thorough manual labelling and preprocessing to ensure accuracy and consistency across all samples. Each image is clearly labelled according to its respective disease class, making the dataset readily usable for machine learning applications. The balanced representation of healthy and diseased leaves allows for comprehensive training and testing of classification models. Designed to support the development of machine learning models for the early detection and classification of eggplant diseases, this dataset holds significant reuse potential in various research domains. It is particularly suitable for applications in plant pathology, precision agriculture, and disease forecasting, where timely and accurate diagnosis is crucial. The dataset is freely available for academic and research purposes, making it a valuable resource for researchers and developers aiming to innovate in agricultural technology and crop management. With its robust design and practical focus, the dataset has the potential to drive advancements in sustainable farming practices and enhance agricultural productivity.
这个茄子叶部病害数据集经过精心开发，为农业研究和自动化病害检测系统的发展提供了宝贵资源。它包含4089张茄子叶的高分辨率图像，系统地分为六个不同类别：健康叶、虫害病害、叶斑病、花叶病毒病、白霉病和枯萎病。这些图像是在可控条件下使用智能手机相机拍摄的，背景为一致的白色，以确保清晰度和一致性。为反映真实的农业场景，数据收集在多个地理位置和不同光照条件下进行。这种方法增强了数据集的多样性和适用性。该数据集经过了全面的人工标注和预处理，以确保所有样本的准确性和一致性。每张图像都根据其相应的病害类别进行了清晰标注，使该数据集可直接用于机器学习应用。健康叶和患病叶的平衡呈现允许对分类模型进行全面的训练和测试。该数据集旨在支持用于茄子病害早期检测和分类的机器学习模型的开发，在各个研究领域具有显著的重用潜力。它特别适用于植物病理学、精准农业和病害预测等应用，在这些领域及时准确的诊断至关重要。该数据集可供学术和研究目的免费使用，对于旨在创新农业技术和作物管理的研究人员和开发人员来说是一个宝贵的资源。凭借其强大的设计和实际应用重点，该数据集有潜力推动可持续农业实践的进步并提高农业生产力。
