Maize Plant Disease classification using optimized DenseNet121

Abstract

In many countries, agriculture is the predominant root of income.Agriculture provides food, as well as income to farmers. Maize is one of world's leading crops and universally cultivated as cereal grain. Usually, agricultural specialists or farmers use their skills to identify pests and diseases that affect fruit and leaves on the spot. Even the most experienced farmer is prone to making errors in disease identification while growing crops in a greater scale. To treat leaf disease, pesticides are used, however, this is damaging to people's health [1]. Several Machine learning, Deep learning algorithms are suggested to classify diseases in the maize plant. Identification of maize leaf disease is a great challenge due to environmental changes and illumination variation in weather conditions. This research focuses on using different Deep Learning architectures like optimized DenseNet121,CNN, ResNet50, MobileNet, VGG16, and Inception-V3for classification of maize leaves disease so that preventive measures can be taken by the farmers at early stage to protect the crops. Our proposed optimized Densenet121 model outperformed compared to optimized CNN, and ResNet50 with lesser parameters and higher accuracy.