Sugarcane Leaf Image Dataset

Published: 11 July 2023| Version 1 | DOI: 10.17632/9twjtv92vk.1
, Yogesh Suryawanshi,


Image datasets play a crucial role across diverse fields, including computer vision, machine learning, medical research, and social sciences. These datasets serve as a valuable resource, providing rich visual information that enables researchers, developers, and professionals to train and validate their models, algorithms, and theories. In the agricultural domain, a specific image dataset focused on sugarcane leaf diseases holds significant importance. Such datasets offer researchers, agronomists, and farmers a valuable tool to identify, classify, and study various leaf diseases affecting sugarcane crops. By analyzing these images, experts can develop more accurate disease detection algorithms and early warning systems, facilitating prompt disease management and preventing widespread crop damage and yield loss. Additionally, a comprehensive dataset allows for the exploration of disease patterns, environmental factors, and potential mitigation strategies, thereby advancing research and improving overall crop management practices to ensure the health and productivity of sugarcane crops. The Sugarcane Leaf Dataset consists of 7134 high-resolution images of sugarcane leaves stored in JPEG format, with dimensions of 768 × 1024 pixels. Categorized into 12 distinct classes, including 10 disease categories, a healthy leaves category, and a dried leaves category, the dataset covers a wide range of common sugarcane leaf diseases, ensuring easy access and identification of specific disease samples. These images were collected through extensive field surveys, capturing different angles and stages of the diseases and guaranteeing a comprehensive representation of visual characteristics. With their high-quality resolution of 72 dots per inch (dpi), the images in the dataset provide clear and detailed visual representation of the sugarcane leaf samples.



Agricultural Science, Disease, Machine Learning, Plant Diseases