Automating Hydroponics: Enhance the Crop Cultivation and Monitoring Using IOT and ML

This paper will draw on our earlier research concerning IoT-enabled automated hydroponics systems but this time take it one step further: implementing ML to optimize nutrient management. Hydroponics is a soilless farm technique that relies on nutrient-rich water for the growth and survival of plants. The critical factors controlling such systems are nutrient levels, pH balance, temperature, and water supply. Where an IoT sensor can potentially sense these parameters in real time, the manual modification of nutrient levels for numerous crop types is resource-extensive and error-prone. Our proposed approach is an inherently machine learningdriven paradigm with dynamic predictions and subsequent adaptation to nutrient levels (NPK), allowing plant productivity to be optimized against available resource consumption. The system applies the information of sensor data including pH, temperature, EC, water levels, and history patterns of nutrient consumption in determining optimal nutrient concentrations for particular crops under environmental conditions. These models are applied within the automated system where it would then determine how to adjust the nutrient solution with or without human interaction. This suggested system employs the algorithms: Random Forest and Neural Networks in order to tackle the task of handling nonlinear relationships along with diverse datasets. Dynamically, the recommendations that the predictive model makes would get a formula applied in order to deviate it towards both its current and optimal nutrient level, thus ensuring precise nutrient delivery. Validation of this model is conducted using extensive evaluation using MAE along with R2 these will also be proven by deployment in a controlled hydroponic environment. Experimental results will show significant improvements in crop yield, resource usage, and scalability of the system: major steps towards automating and optimizing hydroponic farming systems.