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01 - VISÃO GERAL DO SISTEMA, INTERFACES E FUNCIONALIDADES DA PLATAFORMA DE IRRIGAÇÃO IOT

Doi: 10.47094/978-65-284-0682-1/12-22

PALAVRAS CHAVE: Inteligência artificial - Aplicações médicas. Redes neurais (Ciência da computação) - Aplicações médicas. Sistemas de detecção de objetos (YOLO).

KEYWORDS: Artificial intelligence - Medical applications. Neural networks (Computer science) - Medical applications. Object detection systems (YOLO).

ABSTRACT:
1. INTRODUCTION AND FOUNDATIONS OF IOT-BASED AUTOMATED IRRIGATION SYSTEMS

The modernization of Brazilian agriculture through the incorporation of the Internet of Things (IoT) and precision automation requires the harmonious integration of two fundamental fields of knowledge: agronomy and information technology (IT) (ATZORI; IERA; MORABITO, 2010; WOLFERT et al., 2017; BAYIH et al., 2022). Historically, irrigation management in the field has operated in a purely empirical manner or in isolation from the software ecosystem. However, the efficient and sustainable management of water resources today involves more than just the mechanical activation of water pumps; it requires the construction of a comprehensive computational architecture capable of collecting environmental soil data, validating hydraulic safety protocols, providing intuitive interfaces for operators, and executing irrigation routines with precision down to the second.
The primary objective of this book is to motivate and empower both agronomists and IT professionals to design, develop, and deploy their own automated irrigation systems using the Internet of Things. For agronomists, this work demystifies microcontroller programming and software development, demonstrating how to translate models of evapotranspiration and soil-water retention curves into executable algorithms. For information technology professionals, the book presents the real-world demands of the agricultural environment, teaching how to address challenges such as rural connectivity limitations, electromagnetic noise from machinery, the calibration of soil sensors, and field-edge computing architecture (SHI et al., 2016; SATYANARAYANAN, 2017; AL-FUQAHA et al., 2015).

Autores

  • Adriano Soares de Oliveira Bailão

  • Alexandre Garcia Rezende

  • Daniel Xavier Vieira

  • Juliana Fortunato

  • Laenor Henrique Tenório Dantas Brandão

  • Laila Teixeira de Souza

  • Leandro Manuel Pereira Sanches

  • Leonardo Nazário Silva dos Santos

  • Marconi Batista Teixeira

  • Samira Alice de Oliveira

  • Sergio Souza Novak

  • Thiago Gratz Spinassé

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