A Comprehensive Review of Machine Learning and Deep Learning Models for Sugarcane Disease Detection

The agricultural sector plays a pivotal role in global economies, with sugarcane being a significant cash crop in many countries. However, sugarcane production is often severely impacted by various diseases, leading to substantial economic losses. In this paper we provides a comprehensive review of current advancements in machine learning (ML) and deep learning (DL) methodologies applied to the detection and diagnosis of sugarcane diseases. We thoroughly investigate a wide range of ML and DL algorithms, from traditional classifiers like Support Vector Machines (SVM) and Random Forest (RF) to Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Our evaluation covers the important components of data gathering, preprocessing, feature extraction, and model tuning, providing insights into the strengths and limits of each method. This survey aims to showcase the potential of advanced ML and DL models in transforming sugarcane disease prediction techniques. Our survey paper emphasizes the importance of interdisciplinary collaboration and the development of strong, scalable solutions to the problems posed by sugarcane diseases.