Sugarcane Disease Detection: A Machine Learning-Based Image Dataset for Precision Agriculture

Sugarcane is important crop for global sugar industry. Sugarcane diseases significantly impaired the sugar cane fields and its quality. The accurate disease detection is important for prevention of the disease widespread in the crops. We present sugarcane disease detection what does it of consisting 3358 high resolution images classified into five disease categories, a healthy leaves category, and a dried leaves category. The diseases in dataset are brown rust, mites, red spot, yellow leaves, mawa. These datasets help researchers, developers, and professionals to train models algorithms and theories. These datasets enable researchers and developers to train and validate the models. The dataset having diverse image does it increase the accuracy of their model. It is open accessible collection of sugarcane leaf samples. It encourages researchers to work together which accelerates the discovery tracking and controlling of diseases in sugarcane farming. It is open accessible collection of sugarcane leaf samples. It encourages researchers to work together which accelerates the discovery tracking and controlling of diseases in sugarcane farming.