Sugarcane Leaf Image Dataset Collected from V.C. Farm, Mandya.

This dataset contains field images of sugarcane leaves collected at V.C. Farm, Mandya, Karnataka, India. The images were captured during field visits under natural environmental conditions using handheld mobile cameras. The dataset was originally collected to support the development of deep learning models for automated sugarcane disease detection. In particular, the dataset was intended for training convolutional neural network architectures such as ResNet to identify common sugarcane diseases including red rot, rust, yellow leaf disease, and mosaic disease. All images are provided as raw, unannotated field images, with sugarcane leaves stored in a single directory without disease labels or classification. The dataset therefore serves as a base resource for tasks such as dataset annotation, disease classification research, agricultural computer vision studies, and machine learning model development. The dataset was curated and organized by undergraduate students from K.S. Institute of Technology, Bengaluru, affiliated with Visvesvaraya Technological University. During curation, duplicate and low-quality images were removed to improve overall dataset quality and usability. This dataset may support future research in agricultural AI, plant pathology, and computer vision applications for crop monitoring and disease detection.本数据集收录了采集自印度卡纳塔克邦曼迪亚区V.C.农场的甘蔗叶片田间图像。所有图像均于田间实地考察期间，在自然环境下使用手持移动摄像头拍摄获取。 本数据集最初采集的目的是支撑用于自动化甘蔗病害检测的深度学习模型开发工作。具体而言，该数据集旨在用于训练卷积神经网络（Convolutional Neural Network）架构（如ResNet），以识别甘蔗常见病害，包括赤腐病、锈病、黄叶病及花叶病。 所有图像均以原始未标注的田间图像形式提供，所有甘蔗叶片图像均存储于单一目录中，未附带病害标签或分类信息。因此，本数据集可作为数据集标注、病害分类研究、农业计算机视觉研究及机器学习模型开发等任务的基础资源。 本数据集由隶属于维斯瓦拉亚技术大学（Visvesvaraya Technological University）的班加罗尔K.S.技术学院的本科生编纂整理。在编纂过程中，团队移除了重复及低质量图像，以提升数据集整体质量与可用性。 本数据集可支撑农业人工智能、植物病理学及用于作物监测与病害检测的计算机视觉应用等领域的未来研究工作。