Hydroponic Nutrient Control System based on Internet of Things and K-Nearest Neighbors

The human population significantly increases in crowded urban areas, causing a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods using water as a nutrition media. Traditionally, hydroponic farming conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of total dissolved solids (TDS), electrical conductivity (EC), and nutrient temperature. In this research, we propose a system that measures pH, TDS, and nutrient temperature values in the nutrient film technique (NFT) technique using a couple of sensors. We use lettuce as an object of experiment and apply the KNN (k-Nearest Neighbor) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed KNN algorithm achieves 93.3% accuracy when k=5.