DEVELOPMENT OF FOGPONICS CULTIVATION SYSTEM FOR MICROGREENS WITH INTERNET OF THINGS MONITORING SYSTEM AND MACHINE LEARNING AUTOMATION

New technologies are emerging every day to improve the productivity of food production to meet rising demands. Microgreens have gained popularity nowadays and are known for being nutritious and easy to cultivate. Fogponics is one of the emerging technologies that atomizes the nutrient solutions into fine mist, improving the oxygenation and reduces water usage that lacks from traditional farming methods. The study developed an automated fogponics system for microgreens production using machine learning automation and internet of things monitoring systems. The model's evaluation output proves that the system is reliable and capable of predicting an appropriate direction given the datasets acquired from temperature and humidity while the plants are thriving over time. The system has successfully reduced the temperature fluctuation ranging from 26°-33°C to 27°-30°C and stabilized humidity levels from 75-100% to 90-96%. As a result, the performance of the model effectively yielded the microgreens to flourish in its environmental parameters by incorporating machine learning automation and IoT-based monitoring systems. This study strengthened the importance of contributing a promising alternative for sustainable microgreens production. This prototype represents its significant advancement in agricultural strategies for indoor microgreens cultivation, offering a potential alternative for efficient and scalable production.