Application of low-cost sensors to support decision-making in precision irrigation
Year: 2025
Type: dissertation
Abstract: Food production plays a fundamental role in developing a fairer world, which is essential for food security and human well-being. As one of the pillars of this production, agriculture has faced increasing challenges due to water scarcity, a vital resource directly responsible for plant health and productive performance. Water stress in plants, characterized by the lack of water in the soil, can be aggravated by adverse weather conditions or inadequate agricultural practices, such as insufficient irrigation. Although there are consolidated techniques for detecting water stress, most of these are expensive and operationally complex, which makes their adoption difficult for small and medium-sized producers. In this context, this work proposes developing a low-cost system using Internet of Things technologies for monitoring plant water stress, using leaf temperature as an indicator. Under normal conditions, transpiration keeps leaf temperature below that of the air, while in water stress, the lack of transpiration raises leaf temperature above the air temperature. The proposal was validated in an experimental corn plantation of the BRS-Gorotuba cultivar and two sorghum cultivars (the first tolerant and the second sensitive to water deficit), in which the results obtained, based on the physiological response of the plants and the collection of air temperature through aspirated psychrometers, demonstrated the effectiveness of the developed system. In addition, the analysis of the collected data highlights the tool's potential to improve precision irrigation practices, enabling more efficient management of water resources and, consequently, contributing to the sustainability of agricultural production.
Author: Sousa, Otto Álan Pinto de
Language: Portuguese
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7120657172