SLIF-Brinjal: An In-Field Leaf Dataset for Disease Recognition in Precision Agriculture

This dataset contains 8987 images of brinjal (eggplant) leaves collected under real-time agricultural field conditions in Sri Lanka. Images were captured with a smartphone camera under natural light, without controlled backgrounds, to reflect realistic field environments across diverse agroclimatic zones. The dataset includes eight classes: Bacterial Blight, Bacterial Leaf Spot, Bacterial Wilt, Cercospora Leaf Spot, Little Leaf, Mosaic Virus, Powdery Mildew and Healthy leaves. All images are stored in JPG format at a consistent resolution and were manually labelled and cross-validated by agricultural pathology experts based on visible disease symptoms. The dataset is created to support research and development in machine learning, deep learning, and computer vision, particularly for brinjal leaf disease detection and precision agriculture applications under real-world conditions. Users of this dataset are required to cite the following publications. George, R., Nishankar, S., Thuseethan, S., Pakeerathan, K., Ragel, R.G., Pavindran, V., 2026. SLIF-Brinjal: An In-Field Leaf Dataset for Disease Recognition in Precision Agriculture. Scientific Data.本数据集包含8987张茄子（brinjal）叶片图像，采集自斯里兰卡的真实农田环境。所有图像均采用智能手机摄像头在自然光下拍摄，未使用受控背景，以反映不同农业气候区的真实田间环境。本数据集共包含8个类别：细菌性疫病、细菌性叶斑病、细菌性萎蔫病、尾孢叶斑病、小叶病、花叶病毒病、白粉病及健康叶片。所有图像均以JPG格式存储，分辨率统一，且由农业病理学专家依据可见病害症状进行人工标注与交叉验证。本数据集旨在支持机器学习、深度学习及计算机视觉领域的研发工作，尤其适用于真实场景下的茄子叶片病害检测与精准农业应用。 使用本数据集需引用以下文献： George, R., Nishankar, S., Thuseethan, S., Pakeerathan, K., Ragel, R.G., Pavindran, V., 2026. 《SLIF-Brinjal：面向精准农业病害识别的田间叶片数据集》，《Scientific Data》。