Disease Detection In Rice And Wheat Leaves: A Comparative Study On Various Deep Learning Techniques

Abstract

Rice and wheat are considered as the most significant foods in agriculture all over the globe. Around 50% of all calories consumed in the human diet are rendered by both rice and wheat. Rice is a rich source of carbohydrates that is the main energy source of the human body. Wheat, which comprises vitamins and minerals, is a staple food source. Also, wheat is extensively used as flour to make a variety of food products. However, the disease that occurs in the leaves of rice and wheat could decelerate the production of these two food sources. Thus, timely detection of disease that occurs within the rice and wheat leaves is very significant. To detect Rice Leaf Diseases (RLD) and Wheat Leaf Diseases (WLD), numerous conventional methods are established. Specifically, You Only Look Once (YOLO), Faster Regionbased Convolutional Neural Network (FRCNN), and Single Shot Detector (SSD) are extensively implemented in various works to detect diverse leaf diseases in rice and wheat plants. Therefore, in this review, the merits and demerits of the traditional Object Detection (OD) models in RLD and WLD detection are provided systematically. The robustness of the Deep learning (DL) techniques in detecting various kinds of leaf diseases in rice and wheat plants with a classification accuracy of 99% and a precision of 98% is proved by the analysis outcomes.