Disease Detection in Maize Crops Using Deep Learning - A Review

Abstract

The major goal of this work is to give a survey and comparison of several plant disease detection strategies in the field of image processing. As we all know, India is a predominantly agricultural country, with agriculture serving as the primary source of income for the vast majority of the population. It is critical to focus on the domain of farming with modern technology to make their lives more comfortable and simpler. Crop productivity can be increased by introducing modern technologies. An autonomous plant disease detection technology using image processing and a neural network approach can be utilized to solve problems with plant and agricultural diseases. Detecting maize leaf disease is an important endeavor during the maize planting stage. The detection of these disorders necessitates the use of several patterns. There are various classification techniques available, including Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), Back Propagation (BP) Network, Support Vector Machine (SVM), and Convolution Neural Network (CNN).