Leafy vegetables growth analysis using machine learning at different pH and TDS levels for hydroponics

Hydroponic farming is a popular method of growing crops that involves cultivating plants in nutrient-rich water instead of soil. The cultivation of leafy vegetables in home gardens using hydroponics has proven to be productive and resource-efficient. In this study, the nutrient film technique of hydroponic system is used to examine the growth of four commonly grown vegetables such as lettuce, spinach, coriander, and fenugreek. A machine learning-based prediction model is used in this paper to predict the growth of plants. The prediction is done based on potential of hydrogen (pH), total dissolved solids (TDS), and water temperature, which helps in making the better decisions. The optimal pH and TDS values are determined for particular varieties of leafy vegetables. Home gardeners who want to improve the crop growth of their hydroponic systems can use this knowledge as a reference. To promote optimal plant development and productivity, the study seeks to provide a thorough analysis of leafy vegetable growth at different pH and TDS level for hydroponic home gardening. The potential of artificial intelligence is also explained in this paper.