AI for Shading and Radiance Simulation in Agro-Photovoltaics

In collaboration with Davele

BionomeeX has developed a simulation platform to model shading effects and radiance distribution in agro-photovoltaic systems, integrating both direct sunlight and indirect light scattering.

These systems combine solar panels with crop cultivation, and understanding light dynamics is critical to maximize both energy generation and plant growth.

Modeling Light and Growth

The platform uses computational modeling and AI-driven analysis to process large datasets of light measurements, panel geometries, and crop canopy structures.

Key Functionalities

• 🔆 Predicting spatial and temporal light distribution across both crops and solar panels

• 🌿 Assessing the impact of shading on photosynthesis, crop yield, and microclimate

• ⚙️ Optimizing panel placement, tilt angles, and spacing to balance energy output and agricultural productivity

• 🌦️ Simulating environmental conditions such as seasonal sun angles and weather variations to evaluate system performance year-round

By leveraging advanced radiative transfer simulations and machine learning, this tool allows researchers to uncover complex interactions between light, crops, and solar infrastructure, which would be difficult to model manually.

Toward Sustainable Agro-Photovoltaics

This project illustrates how AI and high-fidelity simulations can guide the design of sustainable agro-photovoltaic systems, enabling data-driven decisions that enhance energy efficiency while supporting agricultural productivity.

It represents a significant step forward in integrating renewable energy technologies with modern farming practices, paving the way toward smarter, more efficient, and environmentally responsible agriculture.