Energy performance analysis of an edge-AI irrigation system for cocoa production in rural Colombia

Abstract

This study presents the design and energy performance evaluation of a low-cost intelligent irrigation system tailored for cocoa (Theobroma cacao) agroforestry in tropical Colombia. The system integrates DHT11 temperature and humidity sensors, as well as analog soil hygrometers, with a Raspberry Pi 4 for edge computing. A multilayer perceptron (MLP) model, trained using Edge Impulse, was deployed on an Arduino Uno to enable real-time, autonomous irrigation control. Field validation was carried out over nine weeks in Piedecuesta, Colombia, with environmental variables recorded at 60-second intervals. Compared to conventional timer-based systems, the proposed solution reduced water consumption by 25%, while maintaining soil moisture consistently within the agronomic threshold of 70–85%. Post-irrigation measurements revealed stable microclimatic conditions, with relative humidity maintained between 81% and 87%. In terms of energy performance, the system operated at an average daily consumption of 204 Wh, powered entirely by a standalone solar photovoltaic unit. The deployed neural network model achieved 82% classification accuracy in predicting irrigation states based on sensor data. These results underscore the system’s potential as a scalable, energy-autonomous solution for smallholder agriculture in infrastructure-limited tropical settings.