Estimating wheat productivity using crop modeling: assimilation of in-situ data

• Grant number: 24/01308-3
• Support Opportunities: Regular Research Grants
• Start date: May 01, 2024
• End date: April 30, 2026
• Field of knowledge: Agronomical Sciences - Agronomy - Crop Science
• Principal Investigator: Jayme Garcia Arnal Barbedo
• Grantee: Jayme Garcia Arnal Barbedo
• Host Institution: Embrapa Agricultura Digital. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA). Ministério da Agricultura, Pecuária e Abastecimento (Brasil). Campinas , SP, Brazil
• Associated researchers: Alessandra Dutra Coelho ; Douglas Lau ; Fernando de Almeida Martins ; Genei Antonio Dalmago ; Jose Mauricio Cunha Fernandes ; Telmo De Cesaro Júnior ; Wânderson de Oliveira Assis ; Willingthon Pavan
• Associated scholarship(s): 25/04417-0 - Implementation of and atmospheric variables monitoring system with a weather station and soil moisture sensors using LoRa technology and the LoRaWAN protocol, BP.IC; 25/01821-5 - Climatic information and remote sensing to complement the functions of the Trap System program, BP.IC; 24/08406-0 - Development of Algorithms with Image Processing for a Wheat Crop Monitoring System with the Objective of Collaborating in Monitoring Plant Growth and Identification of Pests, BP.IC

Abstract

The recent approval of the Center for Development in Digital Agriculture (CCD-AD), financed by Fapesp through process 2022/09319-9, enabled the development of research with broad potential for practical use. The objective of the Center to overcome the inequalities in agriculture by means of research, development and innovation (RD&I) aimed toward the improvement of production and productivity of small and medium farmers, is also a fundamental premise of this project. One of the municipalities chosen in CCD-AD, São Miguel Arcanjo (SP), has a significant wheat production and, with the articulation to be performed with all the players of the local agriculture sector, it will be the ideal place for carrying out research on the improvement of wheat's production management. In addition, since a major part of the project's team is based in Passo Fundo (RS), it will be possible to carry out experiments in two regions with different characteristics, improving the generality of the results to be achieved. The research proposed in this project will provide subsidies for a better comprehension and lead to a more systemic view about the various production factors.The monitoring of environmental variables to which plants and soil are subjected to can contribute to improve the productivity and quality of the harvest. With the adequate collection and treatment of the system's data it is possible to evaluate variables related to atmospheric conditions, use of resources and their impact on the production. Additionally, with the use of sensors and technological resources, it becomes feasible to implement strategies for controlling the irrigation process, for monitoring plant growth, to control the dosage of nutrients and fertilizers, for monitoring and controlling pests and diseases, as well as to provide other benefits resulting from applying those technologies in the automation of the processes. Besides soil and environmental sensors, the use of digital images to aid with the crop monitoring will also be explored. In the specific case of the models to be developed in this project, a potentially important input is the incidence of diseases, especially regarding the location and intensity of outbreaks. Among the objectives of the project are the construction of an image database containing symptoms and detailed descriptions of their causes and consequences, the development of a method capable of providing a reliable diagnosis using digital images captured by users, and feed the wheat simulation models with information about disease incidence. Besides, models for the recognition of aphids in traps for the early detection of risks should also be developed. The automation of the process using models based on images should make the decision-making process faster and more effective.The ultimate objective of this project is to produce a set of automatized tools/sensors/traps for the acquisition and processing of data captured in the field, integrated in web platforms providing data management and models with relationship chains between the components of the system, allowing the calculation of the ratio and progress of the biological processes involved that could be used to estimate wheat productivity and issue warnings during the crop season. (AU)