Implementation of an Enhanced Edge Computing System for the Optimization of Strawberry Crop in Greenhouses: A Smart Agriculture Approach

Abstract

This study introduces AgroTec 4.0, which is a smart farming system designed to revolutionize strawberry cultivation in greenhouses through the integration of edge computing technology in the Andean region of Ecuador. The primary objective has been to enhance cultivation efficiency by comparing results from strawberry crops with and without the system, under identical greenhouse conditions. Given the low educational and economic status of local farmers, AgroTec 4.0 was engineered to be user-friendly, easy to operate, and cost-effective, empowering producers with data-driven decision-making capabilities. Key findings underscore the potential of AgroTec 4.0 and agricultural data, including a 15% increase in strawberry yield, from 5.0 kg/m2 in the control greenhouse to 5.75 kg/m2 with AgroTec 4.0, highlighting the system’s ability to maximize productivity. There has also been a significant 20% reduction in water usage, decreasing from 80 L/m2 in the control greenhouse to 64 L/m2 with the system, showcasing AgroTec 4.0’s efficiency in resource management. Furthermore, there were significant improvements in fruit quality, with an 11.8% increase in the Brix index (from 8.5 to 9.5) and a 16.7% increase in average fruit weight (from 30 to 35 g), demonstrating the system’s capacity to enhance product quality. Finally, there has been an impressive 103.03% return on investment (ROI) with AgroTec 4.0, compared to no change in ROI in the control greenhouse, emphasizing the economic value of implementing this technology. These results underscore the transformative potential of AgroTec 4.0 in precision agriculture, offering a scalable and sustainable approach for small-scale producers in Ecuador. The system’s modularity and real-time data analysis capabilities allow for flexible adaptation to various needs, providing farmers with an intuitive interface for managing crops and optimizing resource use. This study demonstrates the feasibility of leveraging agricultural data and edge computing to improve cultivation practices and enhance productivity, contributing efficiently to the sustainability of agriculture in challenging environments.