Sugarcane Disease Recognition using Deep Learning

Abstract

This paper "Sugarcane Disease Recognition Using Deep Learning" describes the concept of a "CNN" as a Convolution Neural Networks. Convolutional neural network (CNN) is a type of feed-forward artificial Neural Network whose connectivity structure is inspired by the organization of the animal visual cortex. Recognizing sugarcane diseases using deep learning techniques can be an effective approach to automate the disease detection process. Deep learning models, such as Convolution Neural Networks (CNN), have shown promising results in image recognition tasks, including plant disease recognition. The primary crop used to produce sugar and ethanol in the globe is sugarcane. The eradication of growing crops infected with the illness is one issue in the sugar sector, and if these diseases are not treated and recognized early, small-scale farmers may suffer financial loss. The rationale for undertaking this study was the rapidly expanding classes of diseases and farmers' insufficient knowledge of disease identification and recognition. As a result, this research offers a suggestion for using deep learning algorithms to assist farmers in identifying and categorize sugarcane infections. The paper also discusses the existing literature in the field, addresses the limitations of previous research, and presents the methodology and functioning of the proposed system