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.

RESUMO O presente trabalho teve como objetivo desenvolver painéis de partículas aglomeradas de baixa densidade com casca de amendoim, fibra da casca do coco-verde (híbrido) e resina poliuretana bicomponente à base de óleo de mamona. Os painéis experimentais foram fabricados com partículas de até 8 mm de comprimento e 15% de resina poliuretana. A prensagem foi realizada a 5,0 MPa, por 10 minutos, a uma temperatura de 100°C. Foram determinadas as propriedades físico-mecânicas do material, seguindo as recomendações da norma ABNT 14810:2006. Os resultados demonstraram que o desempenho mecânico que atende às recomendações estabelecidas pela normativa ANSI A208.1-1999 e ABNT 14.810-2:2006.

ABSTRACT The present study aimed to develop particleboards of low density with peanut shell, coconut husk fiber green (hybrid) and two-component polyurethane resin based on castor oil. The experimental panels were manufactured with pArtigos up to 8mm and 15% polyurethane resin. The pressing was carried out at 5.0 MPa for 10 minutes at 100°C of temperature. The physical-mechanical properties of the material were determined as recommended by ABNT 14810:2006. The results demonstrated a mechanical performance that meets the recommendations established by normative ANSI A208.1-1999 and ABNT 14.810-2:2006.

A avicultura brasileira é uma atividade econômica com expressiva participação no agronegócio brasileiro, porém o setor se depara com desafios, entre esses, destaca-se o estresse térmico dos animais provocado pelas variações climáticas. Dentro desse contexto, este trabalho teve como objetivo estudar as propriedades térmicas de um forro ecológico de baixa densidade a base de partículas de resíduos agroindustriais (casca de amendoim e fibra de coco verde) aglutinadas com resina poliuretana bi-componente à base de óleo de mamona e avaliar seu desempenho quando aplicado como forro em galpões avícolas. Os resultados obtidos demonstram a eficiência do material quando aplicado como forro em aviários.

The Brazilian poultry industry is one the activities relevant to the agribusiness, but the industry is faced with challenges such as heat stress, which is influenced by climate. The production of eco-lining is an alternative that adds value to waste and helps to solve the problem of thermal control in poultry houses. This research aimed to study thermal properties of an ecological liner low density particle-based on agribusiness residues (peanut hulls and coconut fiber) agglutinated with bi-component polyurethane resin based on castor oil and evaluate its performance when applied as lining in poultry houses. The results show that the material has the potential to be used as lining in poultry broiler.