  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.
