Aqua growth: Data-Driven Approach to Predict Plant Growth and Recommend Optimal Hydroponic Systems

Hydroponic farming offers a groundbreaking alternative to traditional agriculture, providing sustainable solutions to global food security challenges. By enabling soilless cultivation in nutrient-rich solutions, hydroponics allows precise control over environmental and nutrient parameters, leading to improved yields, reduced resource consumption, and adaptability in urban or arid areas. Among various hydroponic systems, the Nutrient Film Technique (NFT), Drip System, and Ebb and Flow stand out for their effectiveness, but selecting the optimal system and growth parameters for specific crops remains a complex task. This study addresses these challenges by employing machine learning techniques to predict plant growth and recommend optimal configurations.Regression models including Linear Regression, Decision Tree, Random Forest, and XG Boost were implemented to predict plant growth using a dataset of environmental and nutrient parameters, such as light intensity, pH, electrical conductivity (EC), and nutrient flow rate. The dataset also included system-specific labels, enabling targeted recommendations. XG Boost outperformed other models with the lowest Mean Squared Error (MSE) and highest R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score, demonstrating superior predictive accuracy. Results indicate that light intensity and pH levels are universally critical for all systems, while flow rate and cycle timing significantly influence growth in NFT and Ebb and Flow systems, respectively.Feature importance analysis identified the most influential parameters, guiding system-specific optimization. For instance, the NFT system benefits from precise control of nutrient flow rates, while the Drip System requires adjustments in drip frequency to ensure optimal water retention. These insights can be integrated with IoT devices for real-time monitoring and adjustments, transforming hydroponic farms into intelligent, self-regulating systems. Additionally, this framework supports diverse crop types, making it versatile for various agricultural scenarios.The implications of this research extend beyond yield improvement. By leveraging predictive modeling, farmers can make informed decisions, reducing resource wastage and environmental impact while optimizing productivity. Furthermore, this work sets the stage for future exploration into advanced methods, such as deep learning for time-series prediction and economic analysis to assess cost-effectiveness.In conclusion, this study demonstrates the potential of machine learning in hydroponic farming, enabling data-driven recommendations for system selection and parameter optimization. The integration of these techniques with hydroponics promotes sustainable, efficient, and scalable agricultural practices, addressing global food production challenges.