Maize Leaf Disease Detection using Convolutional Neural Networks

Abstract

Digital image analysis has brought great benefit to agriculture. Plant diseases are an important impediment to food security because they lead to reduction in the quality and quantity of agricultural produce. Detection of plant diseases relies on the skills of the field personnel and often times lacks accuracy since it depends on the experience of the field team. In some cases, disease symptoms vary from one plant to another and this can lead to misclassifications is that. The objective of this study was to develop a classification model for maize leaves diseases using a Convolutional neural network (CNN). Maize leaves showing variation from normal leaves were collected from farms within Embu County, Kenya. Disease symptoms on each leaf were used to diagnose the underlying disease by a plant pathologist. A total of 405 images were captured by focusing on the diseases sections of each leaf using digital camera. We divided our dataset into three parts which is training, validation and testing sets as required for the deep neural network. We used two deep convolutional neural networks AlexNet and ResNet50 to perform maize disease classification. It was observed that both models (AlexNet and ResNet-50) achieved the highest accuracy when the training data was set at 70% and testing dataset was at 30%. The overall performance indicated that ResNet-50 model performed better than AlexNet. This study will help the farmers to take necessary arrangements based on detected diseases at the earliest to avoid the spread of maize diseases. However, the increase of sample size and training of extra neural networks and an application of Ensemble methods is recommended for a more conclusive model accuracy of maize leaves disease and other plant diseases.