Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition

Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4–0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement

Monitoring Foraging Behavior in Ruminants in a Diverse Pasture

Symposium mini revie

An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle

The growth of the world population expected for the next decade will increase the demand for products derived from cattle (i.e., milk and meat). In this sense, precision livestock farming proposes to optimize livestock production using information and communication technologies for monitoring animals. Although there are several methodologies for monitoring foraging behavior, the acoustic method has shown to be successful in previous studies. However, there is no online acoustic method for the recognition of rumination and grazing bouts that can be implemented in a low-cost device. In this study, an online algorithm called bottom-up foraging activity recognizer (BUFAR) is proposed. The method is based on the recognition of jaw movements from sound, which are then analyzed by groups to recognize rumination and grazing bouts. Two variants of the activity recognizer were explored, which were based on a multilayer perceptron (BUFAR-MLP) and a decision tree (BUFAR-DT). These variants were evaluated and compared under the same conditions with a known method for offline analysis. Compared to the former method, the proposed method showed superior results in the estimation of grazing and rumination bouts. The MLP-variant showed the best results, reaching F1-scores higher than 0.75 for both activities. In addition, the MLP-variant outperformed a commercial rumination time estimation system. A great advantage of BUFAR is the low computational cost, which is about 50 times lower than that corresponding to the former method. The good performance and low computational cost makes BUFAR a highly feasible method for real-time execution in a low-cost embedded monitoring system. The advantages provided by this system will allow the development of a portable device for online monitoring of the foraging behavior of ruminants.Fil: Chelotti, Jose Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Vanrell, Sebastián Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Martínez Rau, Luciano Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Galli, Julio Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; ArgentinaFil: Planisich, Alejandra. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; ArgentinaFil: Utsumi, Santiago A.. Michigan State University; Estados UnidosFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Utilization of information and communication technologies to monitor grazing behaviour in sheep

This thesis is a contribution on the study of feeding behaviour of grazing sheep and its general goal was to evaluate the effectiveness of a tri-axial accelerometer based sensor in the discrimination of the main activities of sheep at pasture, the quantification of the number of bites and the estimation of intake per bite. Based on the literature, it has been observed that feed intake at pasture is a difficult parameter to measure with direct observation, for this reason automated systems for monitoring the activities of free-ranging animals have became increasingly important and common. Among these systems, tri-axial accelerometers showed a good precision and accuracy in the classification of behavioural activities of herbivores, but they do not yet seem able to discriminate jaw movements, which are of great importance for evaluating animal grazing strategies in different pastures and for estimating the daily herbage intake. Thus, the main objective of this research was to develop and test a tri-axial accelerometer based sensor (BEHARUM) for the study of the feeding behaviour of sheep and for the estimation of the bite rate (number of bites per min of grazing) on the basis of acceleration variables. The thesis is organized in 4 main chapters. Chapter 1. This introduction section reports a literature review on the importance of studying the feeding behaviour of ruminants and on the measuring techniques developed over the years for its detection, with specific emphasis on accelerometer based sensors, which showed a good precision and accuracy in the classification of behavioural activities of herbivores. Chapter 2. This chapter describes the results of short tests performed in grazing conditions to discriminate three behavioural activities of sheep (grazing, ruminating and resting) on the base of acceleration data collected with the BEHARUM device. The multivariate statistical analysis correctly assigned 93.0% of minutes to behavioural activities. Chapter 3. This part evaluates the effectiveness of the BEHARUM in discriminating between the main behaviours (grazing, ruminating and other activities) of sheep at pasture and to identify the epoch setting (5, 10, 30, 60, 120, 180 and 300 s) with the best performance. Results show that a discriminant analysis can accurately classify important behaviours such as grazing, ruminating and other activities in sheep at pasture, with a better performance in classifying grazing behaviour than ruminating and other activities for all epochs; the most accurate classification in terms of accuracy and Coehn’s k coefficient was achieved with the 30 s epoch length. Chapter 4. This section illustrates the results of a study that aimed to derive a model to predict sheep behavioural variables like number of bites, bite mass, intake and intake rate, on the basis of variables calculated from acceleration data recorded by the BEHARUM. The experiment was carried out using micro-swards of Italian ryegrass (Lolium multiflorum L.), alfalfa (Medicago sativa L.), oat (Avena sativa L.), chicory (Cichorium intibus L.) and a mixture (Italian ryegrass and alfalfa). The sheep were allowed to graze the micro-swards for 6 minutes and the results show that the BEHARUM can accurately estimate with high to moderate precision (r2=0.86 and RMSEP=3%) the number of bites and the herbage intake of sheep short term grazing Mediterranean forages. Finally, the dissertation ends with a summary of the main implications and findings, and a general discussion and conclusions

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

Comportamento ingestivo e conforto térmico de bovinos em sistemas em integração : avaliação visual e bioacústica

Orientadora : Profª. Drª. Maity ZopollattoCoorientadora : Drª. Fabiana Villa AlvesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Zootecnia. Defesa: Curitiba, 22/02/2017Inclui referências : f. 65-73Área de concentração : Nutrição e alimentação animalResumo; Entre os vários benefícios dos sistemas em integração lavoura-pecuária-floresta (ILPF), destaca-se o maior conforto térmico aos animais. O comportamento ingestivo, aliado às variáveis e aos índices microclimáticos, permite inferir sobre este estado. Porém, a observação visual, comumente utilizada para a mensuração do comportamento, apresenta limitações que podem comprometer a qualidade dos dados. Por isso, a bioacústica tornou-se alvo de estudos por potencialmente minimizar e/ou eliminar tais problemas. Objetivou-se avaliar o uso do método acústico para a mensuração do comportamento ingestivo e o conforto térmico de 38 novilhas Nelore (Bos taurus indicus) em dois sistemas de produção em integração. Os sistemas avaliados foram: sistema em integração lavoura-pecuária (ILP) com 5 árvores/ha remanescentes do Cerrado, e sistema ILPF com Eucalyptus urograndis de oito anos de idade dispostos em linhas de 22 m x 2 m, resultando em densidade de 227 árvores/ha; ambos com pastagem de Brachiaria brizantha cv. BRS Piatã. O microclima dos sistemas foi avaliado por meio de termohigrômetros dataloggers, alocados ao sol e à sombra, no qual registravam temperaturas ambiente, de globo negro e de ponto de orvalho e umidade relativa do ar a cada hora. A velocidade do vento, mensurada de forma manual com anemômetro digital, também foi realizada a cada hora. Para a avaliação do comportamento ingestivo por meio de bioacústica, nove novilhas Nelore foram equipadas com gravador de áudio e microfone de lapela. Simultaneamente, realizou-se observação visual instantânea das atividades comportamentais (pastejo, ruminação e outras atividades), das 8h00 às 16h00 (GMT +4h00), em intervalos de dez minutos. Para a avaliação do desempenho, os animais foram pesados a cada 30 dias. O período experimental compreendeu janeiro a maio de 2016, em dois dias consecutivos de cada mês. O componente arbóreo reduz a temperatura de globo negro, a carga térmica radiante e a velocidade do vento, porém não altera as demais variáveis climáticas. Novilhas Nelore no sistema em ILPF, em relação à ILP, despendem maior tempo pastejando e menor tempo ruminando, assim como utilizam locais de sol e sombra de maneira semelhante. Não houve diferença significativa de desempenho animal entre os sistemas, exceto para a maior taxa de lotação do sistema em ILP. Em relação à bioacústica, os tempos médios, em minutos, das atividades de pastejo, ruminação e outras atividades, obtidos pelos métodos visual (334,8; 62,52; 82,69, respectivamente) e acústico (311,4; 62,46; 106,2, respectivamente), foram semelhantes entre si (p<0,05). A bioacústica possui precisão igual ao método visual e pode ser utilizada para a avaliação do comportamento ingestivo de bovinos à pasto por períodos maiores que oito horas. Palavras-chave: ambiência, bem-estar animal, ILPF, pastejo, ruminação, somAbstract: Among the many benefits of the integrated crop-livestock-forestry systems (ICLF), the greatest thermal comfort to animals stands out. The ingestive behavior associated with variables and microclimatic indexes allows inferring about this state. However, visual observation, generally used for measure behavior has limitations that can compromise data quality. Therefore, bioacoustics began to be study for potentially minimize or eliminate such limitations. The objective of this study was to evaluate the use of the acoustic method to measure the ingestive behavior and thermal comfort of 38 Nellore (Bos taurus indicus) heifers in two integrated systems production. The systems evaluated were: integrated crop-livestock system (ICL) with 5 trees ha-1 Cerrado's remnants, and ICLF system with Eucalyptus urograndis, eight years old, arranged in lines of the 22 m x 2 m resulting in density of 227 trees ha-1; Both with pasture of Brachiaria brizantha cv. BRS Piatã. The system's microclimate was evaluated by thermohygrometer dataloggers located in the sun and in the shade. These measured the ambient, the black globe and the dew point temperatures and relative humidity every hour. The wind speed, measured manually by digital anemometer, also was evaluated every hour. For the evaluation of ingestive behavior by bioacoustics, nine Nellore heifers were equipped with audio recorder and lapel microphone. Simultaneously, visual observation of behavioral activities (grazing, rumination and other activities) was performed from 8:00 am to 4:00 pm (GMT +4h00) at ten minutes intervals. For the performance evaluation, the animals were weighed every 30 days. The experimental period occurred from January to May 2016, on two consecutive days of each month. The arboreal component reduces the black globe temperature, radiant heat load and wind speed but not change the other climatic variables. Nellore heifers in ICLF system, in relation to the ICL, spend more time grazing and less time ruminating, as well as uses the sun and shade equally. There was no significant difference in animal performance between systems, except for the higher stocking rate in ICL system. In relation to bioacoustics, the average times in minutes on grazing, rumination and other activities obtained by visual (334.8, 62.52, 82.69, respectively) and acoustic methods (311.4, 62.46, 106.2, respectively) were similar (p<0.05). Bioacoustics has accuracy equal to the visual method and can be used for the evaluate cattle ingestive behavior at pasture in periods greater than eight hours. Keywords: ambience, animal welfare, grazing, ICLF, rumination, soun

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

Livestock vocalisation classification in farm soundscapes

Livestock vocalisations have been shown to contain information related to animal welfare and behaviour. Automated sound detection has the potential to facilitate a continuous acoustic monitoring system, for use in a range Precision Livestock Farming (PLF) applications. There are few examples of automated livestock vocalisation classification algorithms, and we have found none capable of being easily adapted and applied to different species' vocalisations. In this work, a multi-purpose livestock vocalisation classification algorithm is presented, utilising audio-specific feature extraction techniques, and machine learning models. To test the multi-purpose nature of the algorithm, three separate data sets were created targeting livestock-related vocalisations, namely sheep, cattle, and Maremma sheepdogs. Audio data was extracted from continuous recordings conducted on-site at three different operational farming enterprises, reflecting the conditions of real deployment. A comparison of Mel-Frequency Cepstral Coefficients (MFCCs) and Discrete Wavelet Transform-based (DWT) features was conducted. Classification was determined using a Support Vector Machine (SVM) model. High accuracy was achieved for all data sets (sheep: 99.29%, cattle: 95.78%, dogs: 99.67%). Classification performance alone was insufficient to determine the most suitable feature extraction method for each data set. Computational timing results revealed the DWT-based features to be markedly faster to produce (14.81 - 15.38% decrease in execution time). The results indicate the development of a highly accurate livestock vocalisation classification algorithm, which forms the foundation for an automated livestock vocalisation detection system

Methods to Evaluate Ruminant Animal Production Responses

In experiment 1, 80 steers (197.0 kg initial body weight; BW for fall, 116.9 kg for spring), were stocked at 2.45 and 4.1 calves/ha in fall and spring, respectively in 16 tall fescue pastures [fall ergovaline (EV) = 1,475 ppb and spring EV = 1,173 ppb] under 2 treatments, mineral (MIN) (n = 8) and cumulative management (CM) (n = 8). Forage allowance did not differ (P = 0.76) between CM and MIN during fall but differed during spring (P ≤ 0.05, 2.55 vs. 3.22 kg DM/kg BW, for MIN and CM, respectively). For fall, average daily gain (ADG) resulted in 0.41 × EV for MIN and 1.05 × EV for CM. For spring, ADG resulted in 0.80 × EV for MIN and 0.94 EV for CM resulting in an increase of ADG for CM as the level of EV increased. In experiment 2, steers (n = 3) were fitted with a device (Icetag; IceRobotics) strapped to left metatarsus that measured motion activity while on varying levels of EV toxicity. Initial lying bouts for CM were 18.4 but decreased by 0.9 bouts for every 1,000 ppb EV increase. Period 2 resulted in standing time for MIN calves of 858.01 min/day (14.3 h/d) whereas CM calves spent 792.01 min/day (13.2 h/d) standing and CM calves took 20% more steps daily than MIN calves. For every 1,000 ppb increase in EV, steps decreased by 275. In experiment 3, calves (n = 4) grazed long sward regrowth (LSR) or short sward regrowth (SSR) tall fescue and alfalfa paddocks for forage quality, visual observations, rumen volatile fatty acids and diet selectivity measurements. No differences in these behavior measurements were observed for either forage (P \u3c 0.05). Within fescue paddocks, ruminal ammonia, total volatile fatty acids (VFA), acetate, and the branch-chain VFA were greater from SSR vs. LSR (P \u3c 0.05), but these differences were not observed (P ≥ 0.11) on alfalfa paddocks. In summary, the effect of combined management strategies offers potential to cope with toxicity in tall fescue pastures. Grazing activities of cattle grazing tall fescue or alfalfa may influence intake, but further research is needed to determine these behavioral modifications when differences in sward height are small