Diagnosis of Major Foliar Diseases in Black gram (Vigna mungo L.) using Convolution Neural Network (CNN)

Abstract

Background: Proper diagnosis of a foliar disease is a prerequisite to undertaking any crop protection strategy under field conditions. Poor diagnosis and a delay in confirmation in turn decrease the crop yield and increase the cost of plant protection. In this background, advanced machine learning techniques were used for diagnosis of major foliar diseases in black gram using image detection. Casually, black gram yields are highly reduced due to anthracnose and powdery mildew diseases up to 40-67%. To address the issues, the advanced disease identification method of Convolution Neural Network (CNN) is proposed for automated diagnosis in its early stages to assist farmers. Methods: Disease infected leaf samples and their images were collected from different cultivated areas of Tanjore district, Tamil Nadu, India. The image noises were removed and enhanced to improve the accuracy of the training network. A Convolution Neural Network was built with five layers to work on disease images. The first stage of training is to load the image set for training, establish the learning rate, run the optimizer and compile the training convolution model. The final part is to save the loss and accuracy during the training process and evaluate the accuracy of the model. To improve the training learning rate, the Adam optimizer and RMSprop algorithm are used to dynamically adjust the learning rate. The image dataset holds a total of 2002 images of black gram anthracnose and powdery mildew for evaluation. Result: The experiment result showed that the accuracy of disease detection in black gram is about 92.50 per cent with a Precision: 97.14 per cent, Recall: 87.17 per cent, F1 score: 91.89 per cent which proves that convolutional neural network has a faster training speed and higher accuracy. In addition, the proposed method is less time consuming, an early detection tool for the farmers to identify the anthracnose and powdery mildew in black gram leaf which is essential for the application of proper disease management strategies and reduction of yield loss and aids in promotion of smart agriculture.

Detection of Diseases in Blackgram (Vigna mungo L.) Using Machine Learning Models: A Case Study

“…The traditional methods of plant diseases identification are time consuming and laborious task. The availability of skilled persons to identify the plant diseases is low and to overcome these issues, an accurate, intelligent and less time consuming machine learning methods were used to identify plant leaf diseases in black gram [20].…”