Automating Paddy Crop Disease Classification With Deep Learning Models

Abstract

Rice is a staple food crop for more than 10 countries. High consumption of rice demands better yield of crop. Timely disease diagnosis in paddy is fundamental to preventing yield losses and ensuring an adequate supply of rice for a rapidly rising worldwide population. Agriculture and modern farming is one of the fields where IoT and automation can have a great impact. Maintaining healthy plants and monitoring their environment in order to identify or detect diseases is essential in order to maintain a maximum crop yield. The implementation of current high rocketing technologies including artificial intelligence (AI), machine learning, and deep learning have proved to be extremely important in modern agriculture as a method of advanced image analysis domain. Several studies showed that machine learning and deep learning technologies can detect plant diseases upon analyzing plant leaves with great accuracy and sensitivity. In this study, considering the value of deep learning for disease detection, two-dimensional convolutional neural network models - VGG-16, VGG-19, and ResNet50 - are presented to detect plant diseases, enabling farmers to take timely action regarding treatment without further delay. To carry this out, 3 different classes of plants diseases were chosen, where 2,871 plant leaf images were acquired from the real time dataset for training and testing. Based on the experimental results, the proposed model is able to achieve an accuracy of about 99.43% with ResNet50 compared to other models like 2D-CNN, VGG-16 and VGG-19.