Rice Disease Detection Using Deep Learning VGG-16 Model and Flask

Rice Disease Detection Using Deep Learning VGG-16 Model and Flask

Bhairu Jangid, As. Prof. R.S. Sharma

Rajasthan Technical University, Kota

Abstract

As we all know 70 percent population of our country depends on agriculture. India is the second largest producer of rice and wheat.Agriculture is an impor-tant sector of Indian of Indian economy as it contributes about 17 percent of the total GDP and provide employment to over 60 percent population.Indian agriculture registered impressive growth over last few years, but due to various kinds of diseases this production rate was affected very much.In agriculture field disease management is the process of reducing disease in crops to enlarge quality and quantity of harvest yield.Creatures that cause contagious disease in crops include fungi, bacteria, viruses, virus-like-organisms, phytoplasmas, protojoa, etc. These organisms creates different types of diseases in plants like bacterial leaf blight, brown spot, hispa, leaf smut, black spot, powdery mildew, downy mildew, canker, rust, late blight, etc. In this research paper we are trying to create an web based application that identify some diseases from these which affects rice plant.We are implementing an Convolution Neural Network(CNN) based model named VGG-16 for classification of diseases in rice plant.As CNN is very highly accurate for prediction in image classification.

Keywords: Disease detection, Image processing, CNN, VGG-16, Deep learning.