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

• URL: http://arxiv.org/abs/2005.06342v1
• Date: Sat, 9 May 2020 05:54:28 GMT
• Title: sCrop: A Internet-of-Agro-Things (IoAT) Enabled Solar Powered Smart Device for Automatic Plant Disease Prediction
• Authors: Venkanna Udutalapally and Saraju P. Mohanty and Vishal Pallagani and Vedant Khandelwal
• Abstract summary: 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 proposed system has adopted the use of an energy efficient way of powering using solar energy.
• Score: 19.525915957304527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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