This study addresses the growing need for intelligent monitoring in aquaponic systems by developing a predictive system based on artificial intelligence and environmental sensing. The goal is to improve fish welfare through the early detection of adverse water conditions. The system integrates low-cost digital sensors to continuously measure key physicochemical variables—pH, dissolved oxygen, and temperature—using these as inputs for real-time classification of fish health status. Four supervised machine learning models were evaluated: linear discriminant analysis (LDA), support vector machines (SVMs), neural networks (NNs), and random forest (RF). A dataset of 1823 instances was collected over eight months from a red tilapia aquaponic setup. The random forest model yielded the highest classification accuracy (99%), followed by NN (98%) and SVM (97%). LDA achieved 82% accuracy. Performance was validated using 5-fold cross-validation and label permutation tests to confirm model robustness. These results demonstrate that sensor-based predictive models can reliably detect early signs of fish stress or mortality, supporting the implementation of intelligent environmental monitoring and automation strategies in sustainable aquaponic production.

This study presents an engineering-oriented correlation analysis of water quality variables—pH, temperature, and dissolved oxygen (DO)—in a passive aquaponic system integrating red tilapia (Oreochromis spp.) and lettuce (Lactuca sativa) under tropical outdoor conditions. Environmental data were collected at 45-minute intervals over three months using embedded sensors and a microcontroller-based data logger. Pearson, Spearman, and Kendall correlation coefficients were applied to evaluate linear and monotonic relationships among variables. The results indicate a moderate, statistically significant positive correlation between pH and DO (r = 0.566, p < 0.001), and a weaker but still significant correlation between temperature and DO (r = 0.420, p < 0.001). No significant relationship was found between pH and temperature (r = 0.081, p = 0.271). These findings demonstrate that natural environmental dynamics can support acceptable water quality levels without active aeration or control systems. The correlation structure obtained may inform the design of low-cost, energy-efficient monitoring frameworks and decision-support tools for aquaponic systems in resource-constrained or offgrid environments.

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.