Sugarcane Stem and Leaf Disease Prediction Using Deep Neural Network

Abstract

Deep convolutional neural network is a diverting area where researches and achievement are taking excellent progress in agriculture field. The most recent enhancements in computer vision formulated thorough deep learning have covered the method for how to identify and analyze diseases in plants by utilizing a camera to capture images an basis for recognizing several types of plant diseases. This research elaborates disease detection and classification with help of deep learning convolutional neural network. Sugarcane is a vital crop in the world. For detecting sugarcane diseases the researchers used the convolutional neural networks (CNNs) as the basic deep learning method. This study trained and test deep learning model consisting of 2200 sugarcane images dataset. After applying CNNs it achieves an accuracy of 92% and also get error rate 8%. The trained model acquired it motive by detecting and classifying sugarcane images into healthy and infected of sugarcane plants. Therefore, this research provides a step of helping farmers with the process of deep learning algorithm in detecting and classifying sugarcane diseases.