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.