sCrop: A Internet-of-Agro-Things (IoAT) Enabled Solar Powered Smart Device for Automatic Plant Disease Prediction

TL;DR

This paper introduces sCrop, an IoAT-based solar-powered device that automatically predicts plant diseases using image analysis with CNN, demonstrating high accuracy and sustainability in real-world agricultural environments.

Contribution

The paper presents a novel solar-powered IoAT system with integrated deep learning for real-time plant disease prediction, improving energy efficiency and accuracy over existing battery-powered solutions.

Findings

Achieved 99.2% testing accuracy in disease prediction.

Demonstrated robustness and sustainability in varied weather conditions.

Deployed system operated effectively for two months in real-time environment.

Abstract

Internet-of-Things (IoT) is omnipresent, ranging from home solutions to turning wheels for the fourth industrial revolution. This article presents the novel concept of Internet-of-Agro-Things (IoAT) with an example of automated plant disease prediction. It consists of solar enabled sensor nodes which help in continuous sensing and automating agriculture. The existing solutions have implemented a battery powered sensor node. On the contrary, the proposed system has adopted the use of an energy efficient way of powering using solar energy. It is observed that around 80% of the crops are attacked with microbial diseases in traditional agriculture. To prevent this, a health maintenance system is integrated with the sensor node, which captures the image of the crop and performs an analysis with the trained Convolutional Neural Network (CNN) model. The deployment of the proposed system is demonstrated in a real-time environment using a microcontroller, solar sensor nodes with a camera module, and an mobile application for the farmers visualization of the farms. The deployed prototype was deployed for two months and has achieved a robust performance by sustaining in varied weather conditions and continued to remain rust-free. The proposed deep learning framework for plant disease prediction has achieved an accuracy of 99.2% testing accuracy.