Detection Of Corn Leaf Diseases Using Convolutional Neural Network And Transfer Learning

Abstract

This study seeks to develop a neural network model for the identification of corn leaf diseases. Corn leaf diseases substantially affect agricultural yield and food security. Three different types of corn leaf diseases are caused by pathogens, and fungi drastically hamper the production. Without proper action, the entire crop field can be infected, resulting in immense loss in the economy. By consuming affected crops, many diseases can get into the human body. This also increases the probability of getting cancer, which is fatal for humans. In order to address this issue, this study suggests a deep learning-based multiclass classification approach that uses transfer learning (TL) and convolutional neural networks (CNNs) to identify different maize leaf diseases. A dataset of infected and healthy corn leaves is used to train the CNN model. This dataset has been gathered from Kaggle and other sources. Two supervised learning methods are used, and the performance of the models is compared. A pre-trained DenseNet121 transfer learning model is used to enhance the performance of our research. To identify leaves from four different classes, we employed softmax activation in our neural network’s output layer. We got 93.4% accuracy in the convolutional neural network model, and 98.21% accuracy in DenseNet121 in the evaluation phase. This model can efficiently be applied in our agricultural production. Compared to the old native ways, this process requires less time and provides a dynamic way in the detection of corn or maize diseases.