Sugarcane Plant Disease Detection Using Deep Learning

Plant diseases are responsible for major economic losses in the agricultural industry worldwide. Sugarcane plant disease is one of them. Sugarcane is the main source of sugar and ethanol. Sugarcane plant disease is a problem that is mostly known among our farmers. Effective detection of diseases in sugarcane plants is crucial for maintaining crop health and optimizing agricultural productivity, yet traditional methods often fall short in accuracy and efficiency. In this research, we work on various diseases of this plant to find a solution and find some better ways to reduce it. This research helps the farmers to know the diseases of sugarcane and agronomists to identify the diseases accurately. After studying related papers, articles, and journals, we found some factors that are related to us research. Then, we collect images of sugarcane for datasets. Collecting images of both disease and healthy plants of sugarcane. We collected almost 2212 sugarcane images. Then, we applied Convolutional Neural Network (CNN) as the basic deep learning method in our processed dataset. This proposed model helps to classify and detect disease images. In this work, Convolutional Neural Networks (CNN) achieved the best accuracy of 99.46 %. Our model's high performance suggests its potential for real-world application, leading to better disease management and increased crop productivity.