A Detailed Investigation of Deep Learning and Machine Learning Approaches for Sugarcane Foliage Disorder Identification

Our nation grows an enormous amount of sugarcane. Sugarcane is no exception to the rule that plants are susceptible to disease. One problem with the sugar industry is that small-scale farmers lose revenue when sugarcane diseases aren’t identified and addressed in time to save the growing crops. A popular way for a farmer to discover and diagnose sugarcane illnesses is by visual inspection. Using this method on large farms becomes more laborious and time-consuming. The farmers and sugarcane business are both put at risk if it is not handled sooner. This inquiry was motivated by the ever-increasing types of ailments and the fact that farmers have limited abilities when it comes to diagnosing and identifying these diseases. A combination of Deep Learning (DL), Computer Vision (CV), and Machine Learning (ML) can solve this challenge. To boost agricultural output and efficiency, specialists have taken an interest in prompt disease identification and diagnosis. Losses may be avoided by employing ML and DL algorithms for early disease detection in sugarcane crops. The purpose of this research was to quickly and accurately extract diagnostic information from sugarcane using a variety of image processing and DL methods. Additionally, the difficulties and potential future directions of computational methods for evaluating sugarcane infestations are brought to light.