Deep Learning Approach to Sugarcane Disease Identification: From Image Analysis to Mobile Application

Sugarcane, a critical crop for sugar and biofuel production, is vulnerable to various diseases that can severely impact yields and profitability. Early and accurate disease detection is crucial for timely intervention and minimizing losses. This study presents a mobile application leveraging the power of Deep Learning to accurately identify sugarcane diseases directly from leaf images. This paper proposed a smart phone based application that uses Machine Learning aspects to identify Disease in Sugarcane plant from image. A convolutional neural network model that was trained on a large dataset of photos of sugarcane leaves in both healthy and diseased stages is used by the application. The model's mobile deployment optimization ensures quick and accurate disease prediction on user devices. The program offers an easy-to-use interface for taking pictures of leaves, which the trained CNN model subsequently processes and examines. The user is presented with pertinent disease information and suggested management strategies in addition to the projected disease category and confidence level. Sugarcane growers, extension agents, and farmars may find this user-friendly and easily accessible mobile application to be a useful tool for on-the-spot disease diagnosis, allowing prompt intervention and supporting the sustainable production of this essential commodity.