An IoT-based intelligent farming using CNN for early disease detection in rice paddy

Abstract

Huge economic losses occur in the agricultural industry due to bacterial, viral or fungal infections in crops due to which farmers incur 15–20% losses on their profits annually. India is the second largest producer of rice and a leading exporter of the same in the global market. Thus, early disease detection in crops is essential. Implementing Smart Farming is a burning area of research in order to prevent further damage to crops. The widespread development of Deep Learning makes it possible to achieve the goal of disease detection in crops. In this paper we have proposed an intelligent model based on Smart farming integrating Machine Learning with the IoT network. The novelty of this project is early detection of Brown spot disease in rice paddy for the very first time using Convolutional Neural Networks. Deep Learning uses Neural Networks to implement Artificial Intelligence. This project makes use of Image recognition and pre-processing based on real time data. Data pre-processing and feature extraction has been done using a self-designed image-processing tool. Tensor flow and Keras framework has been implemented on both training and testing data collected manually from rice fields. The proposed model achieved an accuracy of 97.701% posing the ability to minimise the losses overall to the national and global productions. Further an app has been designed for the farmers for the same.