Proposta de Sistema Inteligente para Gerenciamento de Hidroponia

Abstract

This monograph presents the development of an intelligent system for hydroponic mana gement, integrating IoT technologies, data analysis, and artificial intelligence (AI) models to optimize hydroponic farming. The system was designed to continuously monitor cultivation parameters, automate manual tasks, and provide real-time recommendations. The proposed architecture includes IoT devices responsible for collecting environmental and nutritional data, controlling actuators, and communicating with a cloud server where the data is stored and processed. The collected data feeds an AI server that utilizes predictive and recommendation models to infer ideal conditions and adjust cultivation parameters such as nutrients and irrigation. Furthermore, the system provides an interactive dashboard, enabling users to monitor device states, configure personalized automations, and access historical data. The use of AI models such as Random Forest, Neural Networks, and Gradient Boosting, combined with a fertilizer recommendation system, demonstrated effectiveness in predicting and adjusting agricultural conditions. With the ability to continuously adapt to the specific needs of different types of crops, the system emerges as a promising solution to enhance productivity, reduce waste, and promote the sustainability of hydroponic farming.