Pages: 1-15

Gunjal Anushka, Gaurav Shingote, Bhagyashree Dhakulkar

DOI: https://adypsoe.in/adypjiet-journal/Artical-11-2025-04-19

Like other crops, sugarcane can get diseases that decrease the quality and quantity. For effective management of the farm of sugarcane, it is important to identify these diseases early stages. Infection and diseases can cause entire fields, which leads to significant financial losses for farmers. To help farmers and solve this problem, researchers are using Artificial Intelligence (AI) and its subfields Deep Learning (DL) and Machine Learning (ML) are used to examine agricultural data such as crop yield, and soil quality to prevent crop se damage. Farmers have to access real-time data and tools to handle this large amount of information and data. This paper specifically looks at using Convolutional Neural Networks (CNN) to detect sugarcane diseases, focusing on those diseases that are found in India majorly. This study addresses the rapid spread of new diseases and the limited ability of farmers to recognize them. By sorting sugarcane images, these images are categorized into healthy and diseased by the trained model, in disease detection accuracy of 98.73 is achieved by the trained model. A mobile-based application was also created to help farmers take pictures and detect diseases from provided data. Future research is suggested by the paper, by using feedback from users to improve the model and enhance the accuracy of the model for detecting diseases with productivity and price analysis to make decisions by farmers.