Advancing Sugarcane Disease Detection through CNN-Based Deep Learning

Abstract

Agriculture produce especially sugarcane crop is no exception to diseases as compared to the other crops. Sugarcane, a vital cash crop for the global sugar industry, faces numerous challenges, with the Top Borer disease. Disease prone sugarcane crop directly affects the production quality and quantity. Sugarcane infections are a cause of worry for the farmers because they can wipe out the entire crop field. Researchers are working on applying Artificial Intelligence (AI) techniques, like Machine Learning (ML) and Deep Learning (DL), to analyse the agricultural data (yield prediction, selling price forecasting, climate, and soil quality etc.) and prevent crop damage due to various reasons, diseases being one of them. Deep neural network which includes Convolutional Neural Network (CNN) is a modern technique for agricultural disease detection. Hence, this paper presents the feasibility study and the effectiveness of DL based CNN algorithm in the disease detection of crops with special reference to selective four diseases of sugarcane crop in India. The proposed system integrates state-of-the-art deep learning algorithms, leveraging Convolutional Neural Networks (CNNs) and recurrent models, to analyze high-resolution images captured by unmanned aerial vehicles (UAVs) or ground-based sensors. These images provide a comprehensive view of the sugarcane plantation, allowing for the identification of subtle symptoms and early-stage infections that may go unnoticed by the human eye. The key components of the developed system include a robust image preprocessing pipeline to enhance the quality of input data, a customized deep neural network architecture trained on a diverse dataset of sugarcane images, and a real-time monitoring system for timely intervention. The model's performance is evaluated on a large-scale dataset collected from sugarcane plantations across diverse geographic regions. The results demonstrate the system's high accuracy in detecting and classifying the Top Borer disease, outperforming traditional methods.