Development of a wireless network of micro weather stations for irrigation management

• Grant number: 18/13090-1
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - PIPE
• Start date: February 01, 2019
• End date: January 31, 2022
• Field of knowledge: Agronomical Sciences - Agricultural Engineering - Soil and Water Engineering
• Principal Investigator: Thiago Alberto Cabral da Cruz
• Grantee: Thiago Alberto Cabral da Cruz
• Company: Dietech Automação Industrial e Robótica Ltda
• CNAE: Atividades de apoio à agricultura ; Atividades de apoio à pecuária ; Fabricação de aparelhos e equipamentos de medida, teste e controle
• City: São Carlos
• Associated researchers: Fernando Campos Mendonca ; Patricia Angélica Alves Marques
• Associated research grant: 15/08689-3 - Development of a wireless sensors/controllers network for soil water content monitoring for irrigation management, AP.PIPE
• Associated scholarship(s): 19/20575-4 - Neural irrigation controller and software development and testing, BP.TT; 19/11830-0 - Artificial intelligence in the irrigation management in chili pepper and lettuce culture, BP.IC; 19/01593-1 - Development of the server software and the communication method between the sensor modules between you and the server, BP.TT; + associated scholarships 19/01600-8 - Development of electronic circuits for meteorological stations, BP.TT; 19/02670-0 - Soil parametrization, calibration of TDR sensors and management of Capsicum annuum L., BP.TT; 19/01434-0 - Development of a wireless network of micro weather stations for irrigation management, BP.eScience.PIPE - associated scholarships

Abstract

Irrigation is considered one of the most important technologies used to increase productivity and to allow greater efficiency and stability in agricultural production. Its adoption must be based on technical and economic feasibility of the project, obtained by means of a detailed and careful analysis of the climatic, agronomic and economic factors involved. Management can be defined as the judicious use of available resources to achieve a given goal. In the case of irrigation, management aims to maximize plant production at the lowest possible cost, both in environmental and economic aspects. This requires the development of a technologically efficient system, reduction of its cost and ease of installation and maintenance. Thus, an intelligent network of meteorological stations capable of monitoring the environment in real time, adapting to the plant's various phenological stages, diverse soils and cultures and communicating with each other and with a server becomes necessary. This project aims at the development and use of the wireless station network which is low cost, easy maintenance, and deployment for the determination of meteorological variables influencing crop evapotranspiration and soil water content for efficient irrigation management. In order for such a sensor network to be implemented, there is a need for such modules to have low energy microcontrollers, since they will be powered by batteries and solar panels, and capable of performing the inference algorithms of the measurement, calibration, and correction of such measures, of communication with the other elements of the network and of executing the irrigation controller, based on Artificial Neural Networks. The artificial intelligence approach used has the capacity to learn and estimate parameters based on their training base and the conditions surrounding it. In addition to the capabilities of the microcontroller, the sensor module must have elements to measure ambient temperature and humidity, brightness, temperature and soil water content, as well as a wireless communication module. At the end of this project, it is expected to obtain the commercialized version of the meteorological stations, already validated and tested in several operational situations. As a way to obtain a meteorological mapping of the locations where the stations will be installed, it is intended to couple to the system a 4G LTE (4G cellular network) modem. In this way, the station data will be sent to the company server. Such data can be shared or even sold, increasing irrigation efficiency, even in locations that do not have the installed station. The target market for this technology is small and medium-sized rural producers and amateur gardening, not being exclusive to them. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

THIAGO A. C. DA CRUZ; PATRICIA A. A. MARQUES. LOW-COST IRRIGATION MANAGEMENT SYSTEM: IMPROVING DATA CONFIDENCE THROUGH ARTIFICIAL INTELLIGENCE. Engenharia Agrícola, v. 43, 2023-03-20. (18/13090-1)