Machine Learning and Explainable AI for Thai Basil Growth Prediction in Hydroponics - Sankalp Kadam; Vinaya Gohokar; Rupali Kute (IEEE Access)
Hydroponic farming has emerged as a sustainable solution to modern agricultural challenges, offering enhanced resource efficiency, reduced environmental impacts, and optimized crop growth. Compared with traditional hydroponic systems, deep-water culture (DWC) is a hydroponic technique that provides continuous nutrients and oxygenated water to plants to increase plant growth. Thai basil is a medicinal plant chosen for this study. The growth of plants is affected by temperature, humidity, solar radiation, pH and total dissolved solids (TDS). This study emphasized the impact of DWC on the cultivation of Thai basil over a one-month monitoring period in terms of environmental and physiological parameters. The green area and plant height were recorded as growth parameters. Six machine learning (ML) models are employed to estimate the growth of Thai Basil. The best model is selected on the basis of the ensemble voting regressor method. The predictions of the best model are explained with Shapley additive explanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs), which are explainable AI models. Explainable AI provides clear and understandable justifications for predictions. Using explainable AI, the proposed research predicts Thai basil growth while providing reasoning for its prediction.