Year: 2024
Type: conference-paper
Abstract: Precision irrigation in greenhouses necessitates remote monitoring of soil moisture. Traditional methods often rely on point measurements, making comprehensive water stress assessment across all crop plants impractical. As an alternative, machine vision has emerged as a promising solution. This study presents a novel approach to soil moisture monitoring using plant images, implementable with low-cost devices and minimal computational resources. The method is based on the hypothesis that leaf discoloration serves as an early indicator of water stress, detectable through RGB imaging. We detail the development and installation of a monitoring system within a grow tent, designed to test irrigation automation based on leaf color across various crops in a controlled environment.
Authors: Flávio Souza, Stefan Blawid
Institution: Universidade Federal de Pernambuco
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
OpenAlex ID: W4406022719