Intelligent Health Assessment System for Paddy Crop Using CNN

Abstract

Crop cultivation plays an essential role in the agricultural field. Presently, the loss of food is mainly due to infected crops, which reflexively reduces the production rate. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires a tremendous amount of work as well as expertise. Hence, an intelligent crop health assessment system using deep learning based on Convolutional Neural Networks (CNN) was proposed. The steps involved are image acquisition, pre-processing, data augmentation and classification. Image of the plant is captured using a smartphone with a camera. Captured images are pre-processed. Data augmentation has been done on the training data set. Implementation of CNN model yielded an accuracy of 85%. The model has been tested against a set of images collected manually.