Smart Mobile System for Detection and Classification of the (Oryza Sativa) Rice Plant Disease Using Deep Learning and Transfer Learning

Abstract

Abstract: In this research, we are analyzing and performing the classification of five different types of bacterial and fungal diseases (Bacterial Leaf Blight, Brown Spot, Rice Blast, Hispa, Healthy Leaf Sheath) which occur in the Oryza Sativa (Rice )plantation. Early Identification and classification of the diseases will reduce 37% quantitative loss of rice plantations. Manual visual inspection has limitations of human vision, and by the time a disease is suspected by the farmer, chances are it’s too late to take action. This creates a need for a smart system (mobile app), which will help in the accurate and timely detection of diseases and surmount the boundaries of human vision. Our app is convenient for farmers as with one-click it will give you a classification of the disease using a Deep learning model running at the back end. Additionally, it will display the ideal quantity of pesticide/ remedy to save crops from the fungal disease as well as soil from being destroyed. The algorithm created using Deep Learning (GoogLe Net) detects and classifies the five different rice sheaths, detecting features such as its shape, color and texture. We are utilizing GoogLeNet as a transfer learning approach to exploit the pre-train DCNN (Deep Convolutional Neural Network). To monitor how the DCNN differentiates between the five different sheaths we use the Grad-CAM technique. This classification system utilized the techniques of deep learning and excelled in the existing tedious image processing methods, giving an overall test accuracy of 80% for the five different leaf sheaths.