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Cattle monitoring using drone images.

Grant number: 23/03870-8
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): August 01, 2023
Effective date (End): July 31, 2025
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Jayme Garcia Arnal Barbedo
Grantee:Everton Castelão Tetila
Host Institution: Embrapa Agricultura Digital. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA). Ministério da Agricultura, Pecuária e Abastecimento (Brasil). Campinas , SP, Brazil
Associated research grant:22/09319-9 - Center of Science for Development in Digital Agriculture - CCD-AD/SemeAr, AP.CCD


The management of large areas dedicated to cattle farming is difficult and, in many cases, it is flawed, especially in the case of extensive production systems. With the popularization of unmanned aerial vehicles (UAVs, also known as drones), high-resolution aerial images can be obtained at relatively low costs. Although this is a promising technology, it is difficult to achieve its full potential due to the challenges involved in the extraction of relevant information from the images obtained. In the specific case of cattle monitoring, difficulties come from animal movement, terrain variety (exposed soil, dry pasture, vigorous pasture, etc.), from occlusions by obstacles such as trees and buildings, and from the tendency of animals to group together.One of the most basic applications in herd management is the estimation of the number of animals using digital images. A project on this theme was recently financed by Fapesp (2018/12845-9 - Cattle detection and counting using unmanned aerial vehicles), which generated many relevant results reported in some articles published in international journals. Despite the advancements achieved, the technology is not yet developed and validated enough for practical use. This project will employ artificial intelligence and deep learning techniques, deep learning platforms like tersorflow, and will demand knowledge on agricultural applications, and cattle production in particular. The objective is that the models already developed for animal counting be perfected and new models dedicated to applications such as anomaly detection (sick animals, calf births, etc.) and estimation of corporal dimensions be developed.

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