Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Author(s)

Paula de Freitas Curti ^[1] ; Alana Selli ^[2] ; Diógenes Lodi Pinto ^[3] ; Alexandre Merlos-Ruiz ^[4] ; Julio Cesar de Carvalho Balieiro ^[5] ; Ricardo Vieira Ventura ^[6]

Affiliation

^[1] Universidade de São Paulo. Faculdade de Medicina Veterinária e Zootecnia. Departamento de Nutrição e Produção Animal - Brasil

^[2] Universidade de São Paulo. Faculdade de Medicina Veterinária e Zootecnia. Departamento de Nutrição e Produção Animal - Brasil

^[3] Universidade de São Paulo. Faculdade de Medicina Veterinária e Zootecnia. Departamento de Nutrição e Produção Animal - Brasil

^[4] Universidade de São Paulo. Faculdade de Medicina Veterinária e Zootecnia. Departamento de Nutrição e Produção Animal - Brasil

^[5] Universidade de São Paulo. Faculdade de Medicina Veterinária e Zootecnia. Departamento de Nutrição e Produção Animal - Brasil

^[6] Universidade de São Paulo. Faculdade de Medicina Veterinária e Zootecnia. Departamento de Nutrição e Produção Animal - Brasil

• Document type: Journal article
• Source: Animal Reproduction; v. 20, n. 2 2023-08-28.

Abstract

Abstract Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations. (AU)

• FAPESP's process: 21/03101-9 - COWADAPT: genetic variants for cattle adaptability to harsh environments uncovered through a bovine multi-assembly graph
• Grantee: Ricardo Vieira Ventura
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 16/19514-2 - Development of a genomic database for Nellore cattle and computational tools for implementing large-scale studies
• Grantee: Ricardo Vieira Ventura
• Support Opportunities: Research Grants - Young Investigators Grants
• FAPESP's process: 21/11156-8 - Impacts of the inclusion of runs of homozygosity in the mating strategy processes in a beef cattle simulated population
• Grantee: Alana Selli
• Support Opportunities: Scholarships in Brazil - Master