Agri-photovoltaic and agriculture machine vision AI: AI-MV for yield prediction, growth forecasting in precision agriculture

Abstract

Advanced technologies such as indoor hydroponics systems and vertical farms have emerged as potential solutions to address food security challenges. These systems create optimal growth environments, leveraging on artificial lighting, effectively maximizing crop yield while minimizing the need for extensive land space and natural lighting. However, these benefits come at the expense of increased energy consumption.

This project aims to develop an advanced rooftop agriculture photovoltaic (AgriPV) hydroponics system that harnesses the energy generated by photovoltaic (PV) technology to drive a PV cooling system, provide horticultural lighting and integrate machine learning algorithms for image processing. The resulting system is an energy-efficient solution tailored for urban rooftop environments, significantly enhancing plant growth rates. The integration of deep learning algorithms will facilitate the continuous monitoring of plant growth profiles, enabling accurate predictions of growth trajectories, which will be used to efficiently activate supplementary lighting.