Detection of coffee fruits on tree branches using computer vision

Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

The use of Artificial Intelligence in predicting Respiratory Syncytial Virus-inhibiting flavonoids / O uso da Inteligência Artificial na predição de flavonoides inibidores do Vírus Sincicial Respiratório

Human Respiratory Syncytial Virus (hRSV) infection results in death and hospitalization of thousands of people worldwide each year. Unfortunately, there are no vaccines or specific treatments for hRSV infections. Screening hundreds or even thousands of promising molecules is a challenge for science. We integrated biological, structural, and physicochemical properties to train and to apply the concept of artificial intelligence (AI) able to predict flavonoids with potential anti-hRSV activity. During the training and simulation steps, the AI produced results with hit rates of more than 83%. The better AIs were able to predict active or inactive flavonoids against hRSV. In the future, in vitro and/or in vivo evaluations of these flavonoids may accelerate trials for new anti-RSV drugs, reduce hospitalizations, deaths, and morbidity caused by this infection worldwide, and be used as input in these networks to determine which parameter is more important for their decision.

A infecção pelo Vírus Sincicial Respiratório Humano (hRSV) resulta na morte e hospitalização de milhares de pessoas em todo o mundo a cada ano. Infelizmente, não existem vacinas ou tratamentos específicos para tais infecções. A testagem de centenas, ou mesmo milhares, de moléculas promissoras é um desafio para a ciência. Neste trabalho, nós integramos propriedades biológicas, estruturais e físico-químicas para treinar e aplicar o conceito de inteligência artificial (IA) capaz de prever flavonoides com potencial atividade anti-hRSV. Durante as etapas de treinamento e simulação, a IA produziu resultados com taxas de acerto superiores a 83%, sendo capaz de prever flavonoides ativos ou inativos contra o hRSV. No futuro, avaliações in vitro e/ou in vivo desses flavonoides poderão acelerar os testes de novas drogas anti-RSV, reduzir hospitalizações, mortes e morbidade causadas por essa infecção. Além disso, a validação futura destes dados poderá determinar qual parâmetro tem maior peso na decisão da inteligência.

Artificial neural networks for the management of poultry industry: a simulation based on the broiler production chain / Redes neurais artificiais para o gerenciamento da indústria avícola: uma simulação baseada na cadeia de produção de frangos de corte

The aim of this study was to predict production indicators and to determine their potential economic impact on a poultry integration system using artificial neural networks (ANN) models. Forty zootechnical and production parameters from broiler breeder farms, one hatchery, broiler production flocks, and one slaughterhouse were selected as variables. The ANN models were established for four output variables: "saleable hatching", "weight at the end of week 5," "partial condemnation," and "total condemnation" and were analyzed in relation to the coefficient of multiple determination (R2), correlation coefficient (R), mean error (E), mean squared error (MSE), and root mean square error (RMSE). The production scenarios were simulated and the economic impacts were estimated. The ANN models were suitable for simulating production scenarios after validation. For "saleable hatching", incubator and egg storage period are likely to increase the financial gains. For "weight at the end of the week 5" the lineage (A) is important to increase revenues. However, broiler weight at the end of the first week may not have a significant influence. Flock sex (female) may influence the "partial condemnation" rates, while chick weight at first day may not. For "total condemnation", flock sex and type of chick may not influence condemnation rates, but mortality rates and broiler weight may have a significant impact.

O objetivo deste trabalho foi predizer os indicadores de produção e determinar o seu potencial impacto econômico em um sistema de integração utilizando as redes neurais artificiais (RNA). Quarenta parâmetros zootécnicos e de produção de granjas de matrizes e de frango de corte, um incubatório e um abatedouro foram selecionados como variáveis. Os modelos de RNA foram estabelecidos para quatro variáveis de saída ("eclosão vendável", "peso ao final da quinta semana", "condenações parciais" e "condenações totais") e foram analisados em relação ao coeficiente de determinação múltipla (R2), coeficiente de correlação (R), erro médio (E), erro quadrático médio (EQM) e raiz do erro quadrático médio (REQM). Os cenários produtivos foram simulados e os impactos foram estimados. Os modelos de RNA gerados foram adequados para simular diferentes cenários produtivos após o treinamento. Para "eclosão vendável", o modelo de incubadora e o período de incubação aumentaram os ganhos financeiros. Para "peso ao final da quinta semana", a linhagem também demonstrou influencia no retorno financeiro, o que não aconteceu com o peso ao final da primeira semana. O sexo do lote possui influência nas taxas de "condenação parcial", ao contrário do peso do frango no primeiro dia. As taxas de mortalidade e o peso do frango apresentaram influência na "condenação total", mas o sexo do lote e o tipo de pinto não tiverem influência.

Retrieval of leaf chlorophyll content in Gannan navel orange based on fusing hyperspectral vegetation indices using machine learning algorithms / Recuperação do teor de clorofila de folhas em laranja do umbigo Gannan com base na fusão de índices de vegetação hiperespectral usando algoritmos de inteligência artificial

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive. The literature base regarding optical indices (particularly chlorophyll indices) is wide ranging and extensive. However, it is without much consensus regarding robust indices for Gannan navel orange. To address this problem, this study investigated the performance of 19 published indices using RDS (raw data spectrum), FDS (first derivative data spectrum) and SDS (second derivative data spectrum) for the estimation of chlorophyll content in navel orange leaves. The single spectral index and combination of multiple spectral indices were compared for their accuracy in predicting chlorophyll a content (Chla), chlorophyll b content (Chlb) and total chlorophyll content (Chltot) content in navel orange leaves by using partial least square regression (PLSR), adaboost regression (AR), random forest regression (RFR), decision tree regression (DTR) and support vector machine regression (SVMR) models. Through the comparison of the above data in three datasets, the optimal modeling data set is RDS data, followed by FDS data, and the worst is SDS data. In modeling with multiple spectral indices, good results were obtained for Chla (NDVI750, NDVI800), Chlb (Datt, DD, Gitelson 2) and Chltot (Datt, DD, Gitelson2) by corresponding index combinations. Overall, we can find that the AR is also the best regression method judging by prediction performance from the results of single spectral index models and multiple spectral indices models. In comparison, result of multiple spectral indices models is better than single spectral index models in predicting Chla and Chltot content using FDS data and SDS data, respectively.

Estimar os teores de clorofila foliar através de espectros de refletância foliar é eficiente e não destrutivo, a base da literatura sobre índices ópticos (principalmente índices de clorofila) é ampla e extensa. No entanto, não há muito consenso sobre índices robustos para a laranja de Gannan. O estudo investigou o desempenho de 19 índices publicados usando RDS (espectro de dados brutos), FDS (espectro de dados de primeira derivada) e SDS (espectro de dados de segunda derivada) para a estimativa do teor de clorofila em folhas de laranja de umbigo. Os índices espectrais foram comparados quanto à sua precisão na previsão do teor de clorofila a (Chla), teor de clorofila b (Chlb) e teor de clorofila total (Chltot) em folhas de laranja de umbigo usando regressão dos mínimos quadrados parcial (PLSR), regressão adaboost (AR), modelos de regressão de floresta aleatória (RFR), regressão de árvore de decisão (DTR) e regressão de máquina de vetor de suporte (SVMR). Através da comparação dos dados acima em três conjuntos de dados, o conjunto de dados de modelagem ideal são os dados RDS, seguidos pelos dados FDS, e o pior são os dados SDS. Na modelagem com vários índices espectrais, bons resultados foram obtidos para Chla (NDVI750, NDVI800), Chlb (Datt, DD, Gitelson 2) e Chltot (Datt, DD, Gitelson2) por combinações de índices correspondentes. No geral, podemos descobrir que o AR também é o melhor método de regressão a julgar pelo desempenho de previsão dos resultados de modelos de índice espectral único e modelos de índices espectrais múltiplos. Em comparação, o resultado de modelos de índices espectrais múltiplos é melhor do que os modelos de índices espectrais únicos na previsão do conteúdo de Chla e Chltot usando dados FDS e dados SDS, respectivamente.

The embryo non-invasive pre-implantation diagnosis era: how far are we?

Advancements in assisted reproduction (AR) methodologies have allowed significant improvements in live birth rates of women who otherwise would not be able to conceive. One of the tools that allowed this improvement is the possibility of embryo selection based on genetic status, performed via preimplantation genetic testing (PGT). Even though the widespread use of PGT from TE biopsy helped to decrease the interval from the beginning of the AR intervention to pregnancy, especially in older patients, in AR, there are still many concerns about the application of this invasive methodology in all cycles. Therefore, recently, researchers started to study the use of cell free DNA (cfDNA) released by the blastocyst in its culture medium to perform PGT, in a method called non-invasive PGT (niPGT). The development of a niPGT would bring the diagnostics power of conventional PGT, but with the advantage of being potentially less harmful to the embryo. Its implementation in clinical practice, however, is under heavy discussion since there are many unknowns about the technique, such as the origin of the cfDNA or if this genetic material is a true representative of the actual ploidy status of the embryo. Available data indicates that there is high correspondence between results observed in TE biopsies and the ones observed from cfDNA, but these results are still contradictory and highly debatable. In the present review, the advantages and disadvantages of niPGT are presented and discussed in relation to tradition TE biopsy-based PGT. Furthermore, there are also presented some other possible non-invasive tools that could be applied in the selection of the best embryo, such as quantification of other molecules as quality biomarkers, or the use artificial intelligence (AI) to identify the best embryos based on morphological and/or morphokitetic parameters.(AU)

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

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)

Fertilidade e suscetibilidade à criopreservação espermática: avanços e desafios / Fertility and susceptibility to sperm cryopreservation: advances and challenges

Estudos sobre a fisiologia espermática demonstram que padrões de fertilidade e funcionalidade espermática pós-criopreservação possuem relação importante com a eficiência do metabolismo energético destas células, bem como com a sua capacidade de manter a homeostase oxidativa. Os conhecimentos sobre a relação entre perfil fisiológico de espermatozoides e fertilidade foram e, ainda estão sendo, aprimorados, com o uso de análises de perfis moleculares, com destaque para a metabolômica. As análises moleculares permitiram a identificação de classes de metabólitos importantes na fisiologia espermática, bem como de potenciais biomarcadores de fertilidade, inclusive em bovinos. No entanto, ainda não há disponível uma avaliação isolada capaz de estimar o padrão de fertilidade de amostras seminais. Há vários desafios a serem superados para a validação de biomarcadores de fertilidade, principalmente considerando-se as diferenças entre perfis metabólicos de raças distintas de touros e a heterogeneidade dos ejaculados. A superação destes desafios pode ser iniciada com um maior aproveitamento dos resultados já obtidos e futuros, com a aplicação de análises mais avançadas e com eficácia em elevado número de dados. Para tal, podem ser utilizados modelos estatísticos de inteligência artificial, cuja aplicação pode aumentar a acurácia das observações obtidas, bem como aproximá-las da aplicação pelo setor de produção animal.(AU)

Studies on sperm physiology demonstrate that fertility outcomes and sperm post-cryopreservation have an important relation with sperm energy metabolism efficiency and ability to maintain oxidative homeostasis. Knowledge on sperm physiology has been enhanced, continually, with the application of molecular profiling analysis, focusing on metabolomics. Molecular analysis allowed the identification of important classes of metabolites in sperm physiology, as well as potential fertility biomarkers, including in bovine. Despite all developments, there is still no isolated assessment available capable of estimating fertility on sperm samples. There are several challenges to be overcome for the validation of fertility biomarkers, especially considering the metabolic profiles differences between bull breeds and the ejaculate heterogeneity. Overcoming these challenges could start with the application of more advanced and effective data analysis from research database already obtained, as well as future ones. To this end, statistical models of artificial intelligence can be used, whose application can increase the accuracy of the observations obtained, as well as bringing them closer to the application by the animal production sector.(AU)

Artificial intelligence applied to enzymatic hydrolysis of lactose: improving the control of industrial processes / Inteligência artificial aplicada a hidrólise enzimática da lactose: melhorando o controle dos processos industriais

Lactose is the main carbohydrate in milk, and its absorption occurs via enzymatic hydrolysis, generating glucose and galactose. Lactose intolerance is the reduction of intestinal hydrolysis capacity due to hypolactasia, which results in the need to consume dairy foods with low levels of this carbohydrate. ß-galactosidase enzymes are used in dairy industries to hydrolyze lactose, thereby allowing intolerant consumers access to dairy products without the negative health implications. Alternative and official analytical methods are used to quantify the carbohydrate content resulting from enzymatic hydrolysis. The objective of this study was to evaluate the enzymatic hydrolysis of two distinct industrial enzymes produced by the microorganisms Bacillus licheniformis and Kluyveromyces lactis using three analytical methods: enzymatic method, cryoscopy, and high performance liquid chromatography (HPLC) using artificial intelligence to improve the control of the industrial processes. After adding the enzymes to skim milk, time kinetics was performed by collecting samples at time 0, every 10 min for 1 h, and every 30 min until the end of 5 h of hydrolysis. In 97% of the cases, a decrease in lactose concentration was observed by HPLC, followed by the deepening of the cryoscopic point. Glucose measurements by absorbance and HPLC quantification were correlated (r = 0.79; p < 0.01) but not concordant (p < 0.01). It was concluded that by means of artificial intelligence, it was possible to indirectly estimate lactose concentration using an algorithm that associates cryoscopy and glucose concentration.(AU)

O principal carboidrato do leite é a lactose e a sua absorção ocorre devido à hidrólise enzimática, gerando glicose e galactose. A intolerância à lactose é a redução da capacidade de hidrólise intestinal devido à hipolactasia, gerando a necessidade do consumo de alimentos lácteos com baixo teor deste carboidrato. As enzimas ß-galactosidase são utilizadas nas indústrias de laticínios para hidrolisar a lactose, proporcionando ao consumidor intolerante a possibilidade de ingerir os produtos lácteos sem prejuízos à saúde. Para quantificar o conteúdo de carboidratos resultante da hidrólise enzimática, são utilizados métodos analíticos alternativos e oficiais. O objetivo deste estudo foi avaliar a hidrólise enzimática de duas enzimas industriais distintas produzidas pelos microrganismos Bacillus licheniformis e Kluyveromyces lactis, por meio de três métodos analíticos: método enzimático, crioscopia e HPLC. A inteligência artificial foi utilizada para melhorar o controle dos processos industriais. Após a adição das enzimas ao leite desnatado, foi realizada a cinética de tempo coletando as amostras no tempo 0, a cada 10 minutos, até completar 1 hora de reação, e a cada 30 minutos até serem atingidas 5 horas de reação de hidrólise. Em 97% dos casos, a diminuição da concentração de lactose por HPLC acompanhou o aprofundamento do ponto crioscópico. As medições de glicose por absorbância e HPLC foram correlacionadas (r = 0,79; p < 0,01), mas não concordantes (p < 0,01). Concluiu-se que, por meio da inteligência artificial, é possível estimar indiretamente a concentração de lactose a partir de um algoritmo que associa a crioscopia e a concentração de glicose.(AU)

Artificial Neural Networks to Predict Egg-Production Traits in Commercial Laying Breeder Hens

In recent years, egg production has had an intense growth in Brazil, and Brazilian egg consumption per capita has significantly increased in the last decade. To reduce sanitary and financial risks, decisions regarding the production and health status of the flock must be made based on objective criteria. Our aim was to determine the main "input" variables for the prediction of egg production performance in commercial laying breeder flocks using an ANN model. The software NeuroShellClassifier and NeuroShell Predictor were used to build the ANN. A total of 26 egg-production traits were selected as input variables and eight as output variables. A database of 44,120 Excel cells was generated. For the training and validation of the models, 74.9% and 25.1% of the data were used, respectively. The accuracy of the ANN models was calculated and compared using the analysis of coefficient of multiple determination (R2), mean squared error (MSE), and an assessment of uniform scatter in the residual plots. The models for the outputs "weekly egg production," "weekly incubated egg,", "accumulated commercial egg," and "viability" showed an R2 greater than 0.8. Other models yielded R2 values lower than 0.8. The ANN predicts adequately eight egg-production traits in the breeders of commercial laying hens. The method is an option for data management analysis in the egg industry, providing estimates of the relative contribution of each input variable to the outputs.(AU)

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.(AU)

Morphological and physiological changes during maturation of okra seeds evaluated through image analysis

This study was performed to associate specific morphological parameters, defined by X-ray images, with seed performance of okra (Abelmoschus esculentus (L.) Moench) during maturation. Fruits of cultivar Santa Cruz 47 at different developmental stages were collected at five-day intervals (from 5 to 65 days after anthesis) and water content, dry matter, germination and vigor were determined in seeds extracted immediately after each harvest or after temporary storage for seven days. X-ray tests were also performed after each harvest and the images were analyzed by ImageJ® software to produce data of aspect ratio (relation between major and minor axes of the ellipse surrounding the seed perimeter) and percentage of free space area in the inner seed cavity. Physiological maturity (maximum accumulation of dry matter) was reached at 30 days after anthesis (DAA), when seed water content was 56.6 %. Seed germination and vigor increased during maturation, achieving the maximum at 50 DAA. Seeds showed morphological changes during maturation, becoming more spherical (aspect ratio near 1.0); at the same time, the free space available in the inner cavity of the seed decreased. This parameter can be successfully used as a marker of physiological maturity when values equal or lower than 5 % are reached.

Morphological and physiological changes during maturation of okra seeds evaluated through image analysis

This study was performed to associate specific morphological parameters, defined by X-ray images, with seed performance of okra (Abelmoschus esculentus (L.) Moench) during maturation. Fruits of cultivar Santa Cruz 47 at different developmental stages were collected at five-day intervals (from 5 to 65 days after anthesis) and water content, dry matter, germination and vigor were determined in seeds extracted immediately after each harvest or after temporary storage for seven days. X-ray tests were also performed after each harvest and the images were analyzed by ImageJ® software to produce data of aspect ratio (relation between major and minor axes of the ellipse surrounding the seed perimeter) and percentage of free space area in the inner seed cavity. Physiological maturity (maximum accumulation of dry matter) was reached at 30 days after anthesis (DAA), when seed water content was 56.6 %. Seed germination and vigor increased during maturation, achieving the maximum at 50 DAA. Seeds showed morphological changes during maturation, becoming more spherical (aspect ratio near 1.0); at the same time, the free space available in the inner cavity of the seed decreased. This parameter can be successfully used as a marker of physiological maturity when values equal or lower than 5 % are reached.(AU)

Artificial neural networks on eggs production data management

Background: Eggs have acquired a greater importance as an inexpensive and high-quality protein. The Brazilian eggindustry has been characterized by a constant production expansion in the last decade, increasing the number of housedanimals and facilitating the spread of many diseases. In order to reduce the sanitary and financial risks, decisions regarding the production and the health status of the flock must be made based on objective criteria. The use of Artificial NeuralNetworks (ANN) is a valuable tool to reduce the subjectivity of the analysis. In this context, the aim of this study was atvalidating the ANNs as viable tool to be employed in the prediction and management of commercial egg production flocks.Materials, Methods & Results: Data from 42 flocks of commercial layer hens from a poultry company were selected. Thedata refer to the period between 2010 and 2018 and it represents a total of 600,000 layers. Six parameters were selectedas “output” data (number of dead birds per week, feed consumption, number of eggs, weekly weight, weekly egg production and flock uniformity) and a total of 13 parameters were selected as “input” data (flock age, flock identification, totalhens in the flock, weekly weight, flock uniformity, lineage, weekly mortality, absolute number of dead birds, eggs/hen,weekly egg production, feed consumption, flock location, creation phase). ANNs were elaborated by software programsNeuroShell Predictor and NeuroShell Classifier. The programs identified input variables for the assembly of the networksseeking the prediction of the variables called outgoing that are subsequently validated. This validation goes through thecomparison between the predictions and the real data present in the database that was the basis for the work. Validation ofeach ANN is expressed by the specific statistical parameters multiple determination (R2) and Mean Squared Error...

Artificial neural networks on eggs production data management

Background: Eggs have acquired a greater importance as an inexpensive and high-quality protein. The Brazilian eggindustry has been characterized by a constant production expansion in the last decade, increasing the number of housedanimals and facilitating the spread of many diseases. In order to reduce the sanitary and financial risks, decisions regarding the production and the health status of the flock must be made based on objective criteria. The use of Artificial NeuralNetworks (ANN) is a valuable tool to reduce the subjectivity of the analysis. In this context, the aim of this study was atvalidating the ANNs as viable tool to be employed in the prediction and management of commercial egg production flocks.Materials, Methods & Results: Data from 42 flocks of commercial layer hens from a poultry company were selected. Thedata refer to the period between 2010 and 2018 and it represents a total of 600,000 layers. Six parameters were selectedas “output” data (number of dead birds per week, feed consumption, number of eggs, weekly weight, weekly egg production and flock uniformity) and a total of 13 parameters were selected as “input” data (flock age, flock identification, totalhens in the flock, weekly weight, flock uniformity, lineage, weekly mortality, absolute number of dead birds, eggs/hen,weekly egg production, feed consumption, flock location, creation phase). ANNs were elaborated by software programsNeuroShell Predictor and NeuroShell Classifier. The programs identified input variables for the assembly of the networksseeking the prediction of the variables called outgoing that are subsequently validated. This validation goes through thecomparison between the predictions and the real data present in the database that was the basis for the work. Validation ofeach ANN is expressed by the specific statistical parameters multiple determination (R2) and Mean Squared Error...(AU)

Milk fraud by the addition of whey using an artificial neural network / Identificação de fraude de leite por adição de soro de leite usando redes neurais artificiais

The adulteration of milk by the addition of whey is a problem that concerns national and international authorities. The objective of this research was to quantify the whey content in adulterated milk samples using artificial neural networks, employing routine analyses of dairy milk samples. The analyses were performed with different concentrations of whey (0, 5, 10, and 20%), and samples were analyzed for fat, non-fat solids, density, protein, lactose, minerals, and freezing point, totaling 164 assays, of which 60% were used for network training, 20% for network validation, and 20% for neural network testing. The Garson method was used to determine the importance of the variables. The neural network technique for the determination of milk fraud by the addition of whey proved to be efficient. Among the variables of highest relevance were fat content and density.(AU)

A adulteração do leite pela adição de soro de leite é um problema que diz respeito às autoridades nacionais e internacionais. O objetivo deste trabalho foi quantificar o teor de soro em amostras de leite adulterado por meio de redes neurais artificiais, usando como variáveis de entrada os resultados de análises rotineiras em amostras de leite. As análises foram realizadas com diferentes concentrações em relação à adição de soro de leite (0, 5, 10 e 20%), e as amostras foram analisadas quanto à gordura, sólidos não gordurosos, densidade, proteína, lactose, minerais e ponto de congelamento, totalizando 164 ensaios, dos quais 60% foram utilizados para treinamento em rede, 20% para validação de rede e 20% para teste de rede neural. O método de Garson foi utilizado para determinar a importância das variáveis. A técnica de redes neurais para a determinação da fraude ao leite por adição de soro provou ser eficiente. Entre as variáveis de maior relevância estavam o teor de gordura e a densidade.(AU)

Stability of the hypocotyl length of soybean cultivars using neural networks and traditional methods / Estabilidade do comprimento do hipocótilo de plantas de cultivares de soja por meio de redes neurais e metodologias tradicionais

The length of the hypocotyl has been highlighted as a potential descriptor of the soybean crop. However, there is no information available in the published literature about its behavior over several planting times. The present study aimed to identify soybean cultivars with stability and predictability of hypocotyl length behavior through neural networks and traditional adaptability and stability methodologies. We analyzed 16 soybean cultivars in 6 planting seasons under greenhouse conditions. In each season, a randomized block design with 4 replications was adopted. The experimental unit was composed of 3 plants. The plot mean was used in the analysis. Hypocotyl length data were analyzed by analysis of variance and Tukeys test. Then analyses were carried out using the Traditional Method, Plaisted and Peterson, Wricke, Eberhart and Russell, and Artificial Neural Networks. A significant effect (p<0.01 by the F test) was identified for Cultivars versus Planting Season and Planting Seasons and Cultivars. Cultivars BRS810C, BRSMG760SRR, TMG1175RR, and BMX Tornado RR showed lower averages, high stability, and general adaptability regarding soybean hypocotyl length whereas the cultivar BG4272 presented higher mean, high stability, and general adaptability. Identification of soybean cultivars of predictable and stable behavior as to hypocotyl length contributes to Soybean Improvement as it further our knowledge on the potential descriptor and the possibility of increasing the number of descriptors.(AU)

O comprimento do hipocótilo tem-se destacado como potencial descritor da cultura da soja, no entanto, não se tem informação sobre o seu comportamento ao longo de várias épocas de plantio. Diante disto, objetivou-se identificar cultivares de soja com estabilidade e previsibilidade de comportamento quanto ao comprimento do hipocótilo por meio de redes neurais e metodologias tradicionais de adaptabilidade e estabilidade. Analisou-se 16 cultivares de soja em seis épocas de plantio, em condições de casa de vegetação. Em cada época, adotou-se o delineamento em blocos casualizados com quatro repetições, sendo a unidade experimental composta por três plantas e usou-se a média da parcela na análise. Os dados de comprimento de hipocótilo foram analisados por meio da análise de variância e teste de Tukey e, posteriormente, procedeu-se análises por meio do Método Tradicional, Plaisted e Peterson, Wricke, Eberhart e Russell e Redes Neurais Artificiais. Identificou-se efeito significativo (p<0,01 pelo teste F) para Cultivares x Épocas, Épocas e Cultivares. As cultivares BRS810C, BRSMG760SRR, TMG1175RR e BMX Tornado RR apresentaram menores médias, alta estabilidade e adaptabilidade geral quanto ao comprimento do hipocótilo de soja; enquanto que, a cultivar BG4272 apresentou maior média, alta estabilidade e adaptabilidade geral. A identificação de cultivares de soja de comportamento previsível e estável, quanto ao comprimento do hipocótilo, contribui para o Melhoramento da Soja no tocante ao melhor conhecimento do potencial descritor e à possibilidade de incremento do número de descritores.(AU)

Redes neurais artificiais na estimação de variáveis biométricas de mudas de espécies florestais produzidas em diferentes substratos / Artificial neural networks for estimating the biometric variables of seedlings from forest species produced in different substrates

O objetivo do trabalho foi avaliar o crescimento em diâmetro do coleto e altura, e a produção de matéria seca total de mudas de Myracrodruon urundeuva, Jacaranda brasiliana e Mimosa caesalpiniaefolia. Concomitantemente, desenvolveu-se uma Rede Neural Artificial (RNA) do tipo Multilayer Perceptron que seria capaz de estimar a H e a MST das mudas das espécies estudadas. As mudas foram cultivadas em ambiente protegido com 50% de sombra. Assim, os tratamentos foram considerados com cinco proporções do material orgânico (0, 20, 40, 60 e 80% v/v) na composição do substrato final (solo da área desertificada). Aos 120 dias após a semeadura, as mudas foram coletadas para determinação das variáveis biométricas. A rede MLP foi utilizada empregando-se o algoritmo de treinamento Levenberg-Marquardat. As variáveis utilizadas como entrada da MLP para a estimação da altura e massa seca das mudas foram: diâmetro do coleto, diâmetro mínimo, médio e máximo do coleto, as espécies e fontes de resíduos orgânicos (esterco bovino, esterco caprino e palha de arroz), totalizando dez entradas. Foi utilizada a função de ativação tangente hiperbólica. Como resultados, recomenda-se a proporção 80:20% (esterco bovino e/ou esterco caprino:solo da área degradada) ao substrato de cultivo para o crescimento das mudas das espécies. A adição de doses de esterco bovino e esterco caprino influenciaram o DC do...

The aim of this study was to evaluate the stem growth in diameter and height as well as the production of total dry matter from seedlings of Myracrodruon urundeuva, Jacaranda brasiliana and Mimosa caesalpiniaefolia. Concurrently, an Artificial Neural Network (RNA) of Multilayer Perceptron type that would be able to estimate the H and the MST of the seedlings of the studied species was developed. The seedlings were cultivated in a protected environment with 50% shade. Thus, the treatments were considered with five proportions of the organic material (0, 20, 40, 60 and 80% v/v) in the final substrate composition (desertified area soil). At 120 days after sowing, the seedlings were collected to determine the biometric variables. The MLP network was used with help of the Levenberg-Marquardat training algorithm. The variables used as input of the MLP for height and dry mass estimation of the seedlings were: stem diameter, minimum, medium and maximum diameter of stem; and species and sources of organic residues (cattle manure, goat manure and rice straw), totaling ten entries. The hyperbolic tangent activation function was conducted. As a result, a 80:20% ratio (bovine manure and/or goat manure: soil from the degraded area) is recommended to be used in the growing substrate for seedling growth. The addition of bovine manure and goat manure doses influenced the Jacaranda brasiliana DC...

Redes neurais artificiais na estimação de variáveis biométricas de mudas de espécies florestais produzidas em diferentes substratos / Artificial neural networks for estimating the biometric variables of seedlings from forest species produced in different substrates

O objetivo do trabalho foi avaliar o crescimento em diâmetro do coleto e altura, e a produção de matéria seca total de mudas de Myracrodruon urundeuva, Jacaranda brasiliana e Mimosa caesalpiniaefolia. Concomitantemente, desenvolveu-se uma Rede Neural Artificial (RNA) do tipo Multilayer Perceptron que seria capaz de estimar a H e a MST das mudas das espécies estudadas. As mudas foram cultivadas em ambiente protegido com 50% de sombra. Assim, os tratamentos foram considerados com cinco proporções do material orgânico (0, 20, 40, 60 e 80% v/v) na composição do substrato final (solo da área desertificada). Aos 120 dias após a semeadura, as mudas foram coletadas para determinação das variáveis biométricas. A rede MLP foi utilizada empregando-se o algoritmo de treinamento Levenberg-Marquardat. As variáveis utilizadas como entrada da MLP para a estimação da altura e massa seca das mudas foram: diâmetro do coleto, diâmetro mínimo, médio e máximo do coleto, as espécies e fontes de resíduos orgânicos (esterco bovino, esterco caprino e palha de arroz), totalizando dez entradas. Foi utilizada a função de ativação tangente hiperbólica. Como resultados, recomenda-se a proporção 80:20% (esterco bovino e/ou esterco caprino:solo da área degradada) ao substrato de cultivo para o crescimento das mudas das espécies. A adição de doses de esterco bovino e esterco caprino influenciaram o DC do...(AU)

The aim of this study was to evaluate the stem growth in diameter and height as well as the production of total dry matter from seedlings of Myracrodruon urundeuva, Jacaranda brasiliana and Mimosa caesalpiniaefolia. Concurrently, an Artificial Neural Network (RNA) of Multilayer Perceptron type that would be able to estimate the H and the MST of the seedlings of the studied species was developed. The seedlings were cultivated in a protected environment with 50% shade. Thus, the treatments were considered with five proportions of the organic material (0, 20, 40, 60 and 80% v/v) in the final substrate composition (desertified area soil). At 120 days after sowing, the seedlings were collected to determine the biometric variables. The MLP network was used with help of the Levenberg-Marquardat training algorithm. The variables used as input of the MLP for height and dry mass estimation of the seedlings were: stem diameter, minimum, medium and maximum diameter of stem; and species and sources of organic residues (cattle manure, goat manure and rice straw), totaling ten entries. The hyperbolic tangent activation function was conducted. As a result, a 80:20% ratio (bovine manure and/or goat manure: soil from the degraded area) is recommended to be used in the growing substrate for seedling growth. The addition of bovine manure and goat manure doses influenced the Jacaranda brasiliana DC...(AU)

Emergências obstétricas em éguas / Obstetric emergencies in mares

Para entender a enfrentar os desafios do ato cirúrgico torna-se necessário a leitura obrigatória do livro “O século dos cirurgiões”, onde o autor descreveu os primórdios dos procedimentos antes e após o advento e descoberta das drogas anestésicas, as técnicas cruentas utilizadas na época, às amputações, a operação cesariana, a comprovação da contaminação bacteriana e os princípios da antissepsia Thorwald (2010). As dificuldades em improvisações enfrentadas pelos praticantes pioneiros da medicina veterinária equina estão historicamente relatadas no livro “Ars Veterinária” detalhadamente compiladas por Walker (1991), onde escreveu uma narrativa da importância do cavalo na história do ser humano e sua utilização como meio de transporte, lazer e atividade militar nas grandes batalhas principalmente dos exploradores e dos impérios greco-romano, contendo ilustrações do instrumental cirúrgico artesanal utilizado na época. Nós sabemos menos do que pensamos conhecer, principalmente com o advento a inteligência artificial e dos algoritmos com suas definições na informática e na matemática. “embora os desafios não tenham precedentes e as discordâncias sejam intensas o gênero humano pode se mostrar á altura do momento se mantivermos nossos temores sob controle e formos um pouco mais humildes quanto as nossas opiniões” Harari (2018). A despeito de todas as previsões para o século XXI nada ira superar o talento e a capacidade do médico veterinário, em especial dos cirurgiões. O objetivo desse texto será o de apresentar uma súmula das principais emergências obstétricas das éguas sem a pretensão de esgotar o assunto, mas sim deixar registrado uma parte da experiência pessoal deste profissional embasado na literatura especializada Prestes e Lourenção (2015).(AU)

In order to face the challenges of surgical act, reading Jürgen Thorwald's book “The Century of the Surgeon” (Das Jahrhundert der Chirurgen) is a must. In his book, the author describes the beginning of surgical procedures before and after the discovery of anaesthetic drugs, the bloody techniques used in those times, the amputations, the caesarean section, the evidence of bacterial contamination, and the principles of antisepsis Thorwald (2010). The difficulties and improvisations faced by the pioneers in equine medicine are historically reported in the book “ArsVeterinaria”, carefully reported by Walker (1991), who tells about the importance of horses in human history and about the use of those animals for transportation, leisure and military purposes, specially on great battles involving explorers and the Greek-Roman empire, including illustrations of the handmade surgical instruments used in that time. We usually know less than we think we know, especially with the artificial intelligence and algorithms advents, with their definitions in computer science and mathematics. “ Although the challenges are unprecedented and the discordances are intense, the human genre may prove to be timely if we keep our fears under control and if we can be a little more humble than our opinions” Harari (2018). Despite all the predictions for the 21 st century, nothing will outweigh the talent and the ability of the veterinarian, especially surgeons. The aim of this text is to present a summary of the main obstacles faced in mares reproductive surgery, with no pretension to exhaust the subject, but to report part of the personal experience of this professional, grounded in specialized literature. Prestes e Lourenção (2015).(AU)