Artificial intelligence in Animal Science

Os sistemas biológicos são surpreendentemente flexíveis pra processar informação proveniente do mundo real. Alguns organismos biológicos possuem uma unidade central de processamento denominada de cérebro. O cérebro humano consiste de 10(11) neurônios e realiza processamento inteligente de forma exata e subjetiva. A Inteligência Artificial (IA) tenta trazer para o mundo da computação digital a heurística dos sistemas biológicos de várias maneiras, mas, ainda resta muito para que isso seja concretizado. No entanto, algumas técnicas como Redes neurais artificiais e lógica fuzzy tem mostrado efetivas para resolver problemas complexos usando a heurística dos sistemas biológicos. Recentemente o numero de aplicação dos métodos da IA em sistemas zootécnicos tem aumentado significativamente. O objetivo deste artigo é explicar os princípios básicos da resolução de problemas usando heurística e demonstrar como a IA pode ser aplicada para construir um sistema especialista para resolver problemas na área de zootecnia.Biological systems are surprising flexible in processing information in the real world. Some biological organisms have a central unit processing named brain. The human's brain, consisting of 10(11) neurons, realizes intelligent information processing based on exact and commonsense reasoning. Artificial intelligence (AI) has been trying to implement biological intelligence in computers in various ways, but is still far from real one. Therefore, there are approaches like Symbolic AI, Artificial Neural Network and Fuzzy system that partially successful in implementing heuristic from biological intelligence. Many recent applications of these approaches show an increased interest in animal science research. The main goal of this article is to explain the principles of heuristic problem-solving approach and to demonstrate how they can be applied to building knowledge-based systems for animal science problem solving

Computational classification of animals for a highway detection system

As colisões entre veículos e animais representam um sério problema na infraestrutura rodoviária. Para evitar tais acidentes, medidas mitigatórias têm sido aplicadas em diferentes regiões do mundo. Neste projeto é apresentado um sistema de detecção de animais em rodovias utilizando visão computacional e algoritmo de aprendizado de máquina. Os modelos foram treinados para classificar dois grupos de animais: capivaras e equídeos. Foram utilizadas duas variantes da rede neural convolucional chamada Yolo (você só vê uma vez) — Yolov4 e Yolov4-tiny (versão mais leve da rede) — e o treinamento foi realizado a partir de modelos pré-treinados. Testes de detecção foram realizados em 147 imagens e os resultados de precisão obtidos foram de 84,87% e 79,87% para Yolov4 e Yolov4-tiny, respectivamente. O sistema proposto tem o potencial de melhorar a segurança rodoviária reduzindo ou prevenindo acidentes com animais.Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals

An exposure assessment model of the prevalence of Salmonella spp. along the processing stages of Brazilian beef

Beef cattle carrying Salmonella spp. represents a risk for contamination of meat and meat products. This study aimed to build an exposure assessment model elucidating the changes in Salmonella prevalence in Brazilian beef along the processing stages. To this effect, the results of a number of published studies reporting Salmonella incidences were assembled in order to model conversion factors based on beta distributions representing the effect of every production stage on the Salmonella incidence on beef carcasses. A random-effects meta-analysis modelled the hide-to-carcass transfer of Salmonella contamination. The Monte Carlo simulation estimated the Salmonella prevalence in beef cuts from processing plants to be â∼1/46.1% (95% CI: 1.4-17.7%), which was in reasonable agreement with a pool (n = 105) of surveys' data of Salmonella in Brazilian beef cuts (mean 4.9%; 95% CI: 1.8-11.5%) carried out in commercial establishments. The results not only underscored the significant increase in Salmonella prevalence that can occur during evisceration/splitting and boning but also reinforced that, when hygienic slaughter procedures are properly implemented, the load of Salmonella can be reduced at dehiding, rinsing and chilling. As the model was based on a systematic review and meta-analysis, it synthesised all available knowledge on the incidence of Salmonella in Brazilian beef.Gonzales-Barron wishes to acknowledge the financial support provided by Portuguese Foundation for Science and Technology (FCT) through the award of a five-year Investigator Fellowship (IF) in the mode of Development Grants (IF/00570).info:eu-repo/semantics/publishedVersio

Autonomous remote gas sensor network platforms with applications in landfill, wastewater treatment and ambient air quality measurement

Carbon dioxide (CO2) and methane (CH4) are produced by anaerobes on decaying matter. This gas production is present in landfill sites and in anaerobic lagoons in waste water treatment plants (WWTP). Monitoring gas production is important as CO2 can collect in low lying areas and asphyxiates, CH4 is flammable in the 5%-15% v/v gas/air region. Both CO2 and CH4 are greenhouse gases, CH4 having 25 times the global warming potential of CO2. At landfill site perimeters, CO2 and CH4 must not exceed the EPA thresholds of 1.5% and 1.0% respectively. Gas production is infrequently measured on individual wells due to expense and labour-intensity. In WWTPs, the monitoring of gas emissions from anaerobic lagoons can enable the bio-digestion process to be optimised and ensure they remain in safe levels. Gas levels can be reduced by modifying the chemistry of the process and by water agitation. Typically measuring gas emissions requires a handheld device to be brought on site and connected to the gas source at each point of interest. This is expensive, time consuming and results in infrequent data, sometimes as long as one month between samples. To address the issue of infrequent sampling rates and to provide the plant managers with near real time data from multiple points on site autonomous wireless gas sensing platforms have been developed, multiples of which can be deployed across a landfill/WWTP to sample gas and pressure up to 12 times per day. Data is sent via GSM to the cloud and can be accessed via an online portal

The convolutional neural network as a tool to classify electroencephalography data resulting from the consumption of juice sweetened with caloric or non-caloric sweeteners

Sweetener type can influence sensory properties and consumer’s acceptance and preference for low-calorie products. An ideal sweetener does not exist, and each sweetener must be used in situations to which it is best suited. Aspartame and sucralose can be good substitutes for sucrose in passion fruit juice. Despite the interest in artificial sweeteners, little is known about how artificial sweeteners are processed in the human brain. Here, we applied the convolutional neural network (CNN) to evaluate brain signals of 11 healthy subjects when they tasted passion fruit juice equivalently sweetened with sucrose (9.4 g/100 g), sucralose (0.01593 g/100 g), or aspartame (0.05477 g/100 g). Electroencephalograms were recorded for two sites in the gustatory cortex (i.e., C3 and C4). Data with artifacts were disregarded, and the artifact-free data were used to feed a Deep Neural Network with tree branches that applied a Convolutions and pooling for different feature filtering and selection. The CNN received raw signal as input for multiclass classification and with supervised training was able to extract underling features and patterns from the signal with better performance than handcrafted filters like FFT. Our results indicated that CNN is an useful tool for electroencephalography (EEG) analyses and classification of perceptually similar tastes

Enfoque metodológico para cuantificar los efectos cognitivos en el análisis sensorial de alimentos = A methodological approach to quantify the cognitive effects in sensorial analysis of food

Este trabajo tuvo como objetivo formular un modelo de análisis sensorial que permita cuantificar la acción del estímulo gustativo en el contexto cognitivo, a partir de la actividad eléctrica cerebral. Los experimentos fueron realizados en dos etapas: (a) Determinación del umbral de percepción del sabor y (b) investigación de la percepción de sabor bajo el umbral de la actividad consciente, utilizando electroencefalograma (EEG). El procesamiento digital de señales en los voluntarios fue realizado usando el análisis tiempo-frecuencia por el método AGR (Adaptative Gaussian Representation). Este método evalúa cómo la información de la señal evoluciona en el espacio tiempo-frecuencia usando coeficientes representativos de este espacio. Se pudo verificar que el 3er coeficiente de AGR se destacó por el ruido y por tanto representó mejor los resultados del EEG. Así fue posible verificar que el coeficiente presentó separación lineal de la concentración de sacarosa, es decir, se detectó en el comportamiento tiempo-frecuencia del EEG la separación entre las concentraciones de sacarosa, independientemente de la manifestación del sujeto experimental. Esos resultados sugieren que la metodología descrita en este artículo puede ser utilizada como una herramienta complementaria al análisis sensorial

Magnetic Fields in Food Processing Perspectives, Applications and Action Models

Magnetic fields (MF) are increasingly being applied in food processing to preserve food quality. They can be static (SMF), oscillating (OMF) or pulsed (PMF) depending on the type of equipment. The food characteristics can be influenced by several configurations of the applied magnetic field as its flux density, frequency, polarity and exposure time. Several mechanisms have been proposed to explain the effects of magnetic fields on foods. Some of them propose interactions at the subatomic particle level that show quantum behavior, such as the radical pair and cyclotron resonance mechanisms. Other proposals are at the level of DNA, compounds, subcellular organelles and cells. The interactions between food and magnetic fields are addressed in a general way in this work, highlighting the applications and action models involved and their effects on the physicochemical, enzymatic and microbiological characteristics of food

SISTEMA TELEMÉTRICO DE MONITORAÇÃO DE TEMPERATURA PARA BOVINOS / DEVELOPMENT OF A TELEMETRIC TEMPERATURE MONITORING SYSTEM FOR BOVINE APPLICATION

Este artigo apresenta um sistema completo de instrumentação para monitoração detemperatura, desenvolvido para auxiliar na coleta de dados para estudo de estresse térmico em bovinos. Nesse equipamento o elemento sensor pode ser implantado no animal. O sistema utiliza tecnologia de sensores sem fio e possui capacidade de gerenciamento através de um programa de computador. Os resultados obtidos mostram que o sistema desenvolvido é capaz de monitorar a temperatura de bovinos a cada cinco minutos, durante 30 dias, com precisão de 0.0625o C. </p