Development of a robust automated hydroponic system based on mesh network and artificial intelligence

Saravanan, Navin Raj (2024) Development of a robust automated hydroponic system based on mesh network and artificial intelligence. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

In today's globalized landscape, technological strides have revolutionized agriculture, prompting a shift from traditional methods to innovative solutions In response to overcome the challenges of achieving optimal growth conditions, resolving inconsistent crop tracking in hydroponics, and ensuring resilient hydroponic system node redundancy, this project establishes a comprehensive framework. The primary objectives include the implementation of Artificial Intelligence for tracking and analyzing crop growth rates through sensor parameters, enhancing communication efficiency within the mesh network, and implementing a remote control system for precise and adaptive watering in the hydroponic system. The method involves ESP32 nodes connecting to the mesh network and subsequently to the Raspberry Pi 4 via Wi-Fi, enabling wireless communication. Utilizing MQTT protocol, the nodes transmit water level and crop height data, ensuring redundancy in case of node failure. The Raspberry Pi 4 monitors water levels and triggers alerts through the Blynk application, activating the water pump to maintain optimal conditions. Data is stored in a CSV file, compiled using Python, and imported into MATLAB to generate a predictive model using Neural Network algorithms. Results from the prediction model, including RMSE: 0.51387, R-squared: 0.35, MSE: 0.26407, and MAE: 0.34951, indicate improved predictive accuracy. The Blynk application provides real-time monitoring, offering users insights into the hydroponic system's status. This comprehensive approach bridges the gap between identified problems and innovative solutions, contributing to the advancement of efficient and resilient hydroponic systems.

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