Mô hình giám sát và dự báo thông minh cho rau má thủy canh dựa trên machine learning (ML) và deep learning (DL)

Abstract

This paper proposes and presents a novel approach to optimizing the yield and quality of Centella asiatica (Gotu Kola) in a hydroponic environment through the extensive application of Machine Learning (ML) and Deep Learning (DL) techniques. The system is built with IoT sensors to collect real-time data on key environmental parameters such as nutrient solution pH, EC concentration, temperature, light intensity, and air humidity. This data is then transmitted to a cloud platform where Artificial Intelligence (AI) processes and analyzes it using ML and DL models. The main objective is to develop accurate predictive models that enable automated decision-making and early warning, thereby improving cultivation efficiency and minimizing risks.