Cost-Effective Cyber-Physical System Prototype for Precision Agriculture with a Focus on Crop Growth

TL;DR

This paper presents a cost-effective cyber-physical system prototype for precision agriculture that uses sensors and image processing to non-destructively monitor and predict crop growth, aiming to enhance sustainability and yields.

Contribution

It introduces a rapid prototyping approach for adaptive CPS platforms tailored to specific agricultural environments, focusing on non-destructive plant growth estimation.

Findings

Effective monitoring and prediction of plant growth using the prototype

Demonstrated potential for sustainable crop management at moderate hardware costs

Validated the integration of sensors, image processing, and modeling in agriculture

Abstract

In precision agriculture, integrating advanced technologies is crucial for optimizing plant growth and health monitoring. Cyber-physical system (CPS) platforms tailored to specific agricultural environments have emerged, but the diversity of these environments poses challenges in developing adaptive CPS platforms. This paper explores rapid prototyping methods to address these challenges, focusing on non-destructive techniques for estimating plant growth. We present a CPS prototype that combines sensors, microcontrollers, digital image processing, and predictive modeling to measure leaf area and biomass accumulation in hydroponic environments. Our results show that the prototype effectively monitors and predicts plant growth, highlighting the potential of rapid CPS prototyping in promoting sustainability and improving crop yields at a moderate cost of hardware.