Bioacústica como ferramenta de avaliação do comportamento ingestivo de bovinos a pasto.

A pecuária brasileira se caracteriza pela criação de bovinos a pasto, seja de forma extensiva (tradicional) quanto intensiva (integração lavoura-pecuária-floresta). Bovinos nesses sistemas externam a qualidade do ambiente no qual estão inseridos por meio do de manifestações comportamentais de vários tipos. Neste contexto, o comportamento ingestivo, isto é, o conjunto de atividades ligadas à busca, apreensão e digestão da forragem, que o animal realiza durante sua jornada, é um indicador tanto de aspectos quantiqualitativos do alimento disponível, quanto do ambiente físico onde está inserido. A observação visual, metodologia mais utilizada para esse fim por ser de baixo custo, é de baixa acurácia e muito trabalhosa, e seu uso já começa a ser questionado no meio acadêmico, embora as alternativas existentes ainda sejam praticamente inviáveis do ponto de vista técnico-financeiro para uso em grandes extensões. Este documento busca trazer à tona o potencial de uso da bioacústica para avaliação do comportamento ingestivo de bovinos, com ênfase na possibilidade de sua aplicação, inclusive, em animais mantidos em ambientes abertos, de grande extensão, típicos dos sistemas de produção de bovinos de corte em pastagens tropicias. São abordados, assim, aspectos da origem e fundamentação da técnica, suas aplicações, os pré-requisitos técnicos para aquisição, armazenamento e análise dos arquivos sonoros, avanços na sua utilização em animais de produção e os principais desafios que ainda persistem, dentre outros itens.bitstream/item/172214/1/Bioacustica-como-ferramenta-de-avaliacao-do-comportamento-ingestivo.pd

Application of deep learning for livestock behaviour recognition: a systematic literature review.

Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techniques empowered by deep learning (DL) models that, at the core, act as decision-making tools. These models have previously been used to address several issues, including individual animal identification, tracking animal movement, body part recognition, and species classification. However, over the past decade, there has been a growing interest in using these models to examine the relationship between livestock behaviour and associated health problems. Several DL-based methodologies have been developed for livestock behaviour recognition, necessitating surveying and synthesising state-of-the-art. Previously, review studies were conducted in a very generic manner and did not focus on a specific problem, such as behaviour recognition. To the best of our knowledge, there is currently no review study that focuses on the use of DL specifically for livestock behaviour recognition. As a result, this systematic literature review (SLR) is being carried out. The review was performed by initially searching several popular electronic databases, resulting in 1101 publications. Further assessed through the defined selection criteria, 126 publications were shortlisted. These publications were filtered using quality criteria that resulted in the selection of 44 high-quality primary studies, which were analysed to extract the data to answer the defined research questions. According to the results, DL solved 13 behaviour recognition problems involving 44 different behaviour classes. 23 DL models and 24 networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being the most popular networks. Ten different matrices were utilised for performance evaluation, with precision and accuracy being the most commonly used. Occlusion and adhesion, data imbalance, and the complex livestock environment were the most prominent challenges reported by the primary studies. Finally, potential solutions and research directions were discussed in this SLR study to aid in developing autonomous livestock behaviour recognition systems

Application of deep learning for livestock behaviour recognition: a systematic literature review

Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techniques empowered by deep learning (DL) models that, at the core, act as decision-making tools. These models have previously been used to address several issues, including individual animal identification, tracking animal movement, body part recognition, and species classification. However, over the past decade, there has been a growing interest in using these models to examine the relationship between livestock behaviour and associated health problems. Several DL-based methodologies have been developed for livestock behaviour recognition, necessitating surveying and synthesising state-of-the-art. Previously, review studies were conducted in a very generic manner and did not focus on a specific problem, such as behaviour recognition. To the best of our knowledge, there is currently no review study that focuses on the use of DL specifically for livestock behaviour recognition. As a result, this systematic literature review (SLR) is being carried out. The review was performed by initially searching several popular electronic databases, resulting in 1101 publications. Further assessed through the defined selection criteria, 126 publications were shortlisted. These publications were filtered using quality criteria that resulted in the selection of 44 high-quality primary studies, which were analysed to extract the data to answer the defined research questions. According to the results, DL solved 13 behaviour recognition problems involving 44 different behaviour classes. 23 DL models and 24 networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being the most popular networks. Ten different matrices were utilised for performance evaluation, with precision and accuracy being the most commonly used. Occlusion and adhesion, data imbalance, and the complex livestock environment were the most prominent challenges reported by the primary studies. Finally, potential solutions and research directions were discussed in this SLR study to aid in developing autonomous livestock behaviour recognition systems