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

Behavior Classification of A Grazing Goat in the Argentine Monte Desert by Using Inertial Sensors

The knowledge generated by animal behavior studies has been gaining importance due to it can be used to improve the efficiency of animal production systems. In recent years, sensor-based approaches for animal behavior classification has emerged as a promising alternative for analyzing animals grazing patterns. In the present article it is proposed the use of a classification system based on inertial sensors for identifying a goat’s grazing behavior in the Argentine Monte Desert. The data acquisition system is based on commercial off-the-self devices. It is used to create a reliable dataset for performing the animal behavior predictions. By fixing the system on the head of a goat it was possible to log its movements when it was grazing in a natural pasture. A preliminary version of the dataset is evaluated using a classical statistical learning algorithm. Results show that goat activities can be predicted with an average precision value above 85% and a recall of 84%.Sociedad Argentina de Informática e Investigación Operativ

Behavior Classification of A Grazing Goat in the Argentine Monte Desert by Using Inertial Sensors

The knowledge generated by animal behavior studies has been gaining importance due to it can be used to improve the efficiency of animal production systems. In recent years, sensor-based approaches for animal behavior classification has emerged as a promising alternative for analyzing animals grazing patterns. In the present article it is proposed the use of a classification system based on inertial sensors for identifying a goat’s grazing behavior in the Argentine Monte Desert. The data acquisition system is based on commercial off-the-self devices. It is used to create a reliable dataset for performing the animal behavior predictions. By fixing the system on the head of a goat it was possible to log its movements when it was grazing in a natural pasture. A preliminary version of the dataset is evaluated using a classical statistical learning algorithm. Results show that goat activities can be predicted with an average precision value above 85% and a recall of 84%.Sociedad Argentina de Informática e Investigación Operativ

Intelligent strategies for sheep monitoring and management

With the growth in world population, there is an increasing demand for food resources and better land utilisation, e.g., domesticated animals and land management, which in turn brought about developments in intelligent farming. Modern farms rely upon intelligent sensors and advanced software solutions, to optimally manage pasture and support animal welfare. A very significant aspect in domesticated animal farms is monitoring and understanding of animal activity, which provides vital insight into animal well-being and the environment they live in. Moreover, “virtual” fencing systems provide an alternative to managing farmland by replacing traditional boundaries. This thesis proposes novel solutions to animal activity recognition based on accelerometer data using machine learning strategies, and supports the development of virtual fencing systems via animal behaviour management using audio stimuli. The first contribution of this work is four datasets comprising accelerometer gait signals. The first dataset consisted of accelerometer and gyroscope measurements, which were obtained using a Samsung smartphone on seven animals. Next, a dataset of accelerometer measurements was collected using the MetamotionR device on 8 Hebridean ewes. Finally, two datasets of nine Hebridean ewes were collected from two sensors (MetamotionR and Raspberry Pi) comprising of accelerometer signals describing active, inactive and grazing activity of the animal. These datasets will be made publicly available as there is limited availability of such datasets. In respect to activity recognition, a systematic study of the experimental setup, associated signal features and machine learning methods was performed. It was found that Random Forest using accelerometer measurements and a sample rate of 12.5Hz with a sliding window of 5 seconds provides an accuracy of above 96% when discriminating animal activity. The problem of sensor heterogeneity was addressed with transfer learning of Convolutional Neural Networks, which has been used for the first time in this problem, and resulted to an accuracy of 98.55%, and 96.59%, respectively, in the two experimental datasets. Next, the feasibility of using only audio stimuli in the context of a virtual fencing system was explored. Specifically, a systematic evaluation of the parameters of audio stimuli, e.g., frequency and duration, was performed on two sheep breeds, Hebridean and Greyface Dartmoor ewes, in the context of controlling animal position and keeping them away from a designated area. It worth noting that the use of sounds is different to existing approaches, which utilize electric shocks to train animals to adhere within the boundaries of a virtual fence. It was found that audio signals in the frequencies of 125Hz-440Hz, 10kHz-17kHz and white noise are able to control animal activity with accuracies of 89.88%, and 95.93%, for Hebridean and Greyface Dartmoor ewes, respectively. Last but not least, the thesis proposes a multifunctional system that identifies whether the animal is active or inactive, using transfer learning, and manipulates its position using the optimized sound settings achieving a classification accuracy of over 99.95%

Farm Animals’ Behaviors and Welfare Analysis with AI Algorithms: A Review

peer reviewedNumerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors

Custom wireless sensor for monitoring grazing of free-range cattle

Scope and Method of Study: The purpose of this study was to develop a wireless sensor device capable of sensing cattle grazing activity. This included design and build of a miniaturized PCB, sensor specification, data processing, and experimental validation. Experiments were conducted in cooperation with the Oklahoma State University, Animal Science and Biosystems Engineering departments. The primary objective of this study was to provide information supporting the use of an accelerometer sensor for monitoring free-range cattle grazing activity. A wireless sensor platform was also developed for sensor and wireless communication development needs. Secondary objectives included exploring alternative applications, such as monitoring cattle waste excretion events, and identifying wireless network functionality for agricultural environments.Findings and Conclusions: During this study, parameters for using an accelerometer based grazing sensor were established relative to the head motion of grazing cattle. Initially, a survey of literature and video analysis of foraging livestock animals were conducted, where 0.5-8 bites/sec was confirmed as animal bite rate range. The preliminary video analysis provided guidelines for establishing a sensing strategy. Sensor data processing algorithm development and sampling rate selection were driven by video provided characteristics and sensor platform capability. The Fast Fourier Transform (FFT) was selected as the core component of the sensor's algorithm. The FFT was able to characterize grazing motions because of the animal's near-continuous periodic head movements. At least five bite cycles and a 32 Hz sampling rate were required for proper algorithm implementation. A sample size of 256 data points were collected for each accelerometer axis, and proved to be adequate for the FFT computations. A revised sample rate of 21.74 Hz was presented once the FFT was implemented in firmware. This new rate retained well performing FFT calculations based on the understanding that bite rates faster than 4 bites/sec were due to nibbling and partial bites. The FFT's Spectral power was binned and stored for the purpose of data compression and reduced wireless transmissions.The wireless sensor device platform was built using the CC1010 microcontroller/transceiver IC. The CC1010 provided integrated features commendable for fast FFT processing and conservative PCB layout design. The radio was configured for robust operation by using a 915 MHz carrier frequency, Manchester encoding, and 64 kHz frequency spread. A small, helical, and omnidirectional antenna was mounted directly to the PCB. Link budget was estimated to be 81 dBm, which equated to a 282 m (925 ft) transmission distance in optimum conditions. The device's dimensions were 19.6 mm (0.77 in) X 71.8 mm (2.83 in) X 11.0 mm (0.43 in). A custom PVC enclosure was used to house the device. For deploying experiments, the enclosure was fastened to a standard nylon turnout halter. A miniature GPS logger was also attached to the halter, which allowed for constructing grazing maps.Additionally, the proposed wireless sensor device was used to detect cattle urination and defecation events. This was accomplished by attaching the device to an animal's tail and sensing its elevated movements. Tilt measurements in the z-axis (front-to-back) direction provided the most prominent evidence of a distinct tail movement pattern during excretion events. A pattern recognition strategy was shown as a viable sensing method.An outline for a multilevel-networked system was also generated. This included cellular and internet communications, along with a customized application software for base/node management

Practical Experiences of a Smart Livestock Location Monitoring System leveraging GNSS, LoRaWAN and Cloud Services.

Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the Northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration and number of devices. The results are analyzed and discussed for packe delivery ratio, energy consumption, localization accuracy, battery discharge measurement and delay

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

The Tallgrass Prairie Soundscape; Employing an Ecoacoustic Approach to Understand Grassland Response to Prescribed Burns and the Spatial and Temporal Patterns of Nechrophilous Invertebrate Communities

Tallgrass prairies are rapidly vanishing biodiversity hotspots for native and endemic species, yet little is known regarding how spatial and temporal variation of prairie soundscapes relates to seasonal changes, disturbance patterns and biological communities. Ecoacoustics, the study of environmental sounds using passive acoustics as a non-invasive tool for investigating ecological complexity, allows for long-term data to be captured without disrupting biological communities. Two studies were carried out by employing ecoacoustic methodology to study grassland carrion food webs and to capture the phenology of a grassland soundscape following a prescribed burn. Both studies were conducted at the Nature Conservancy’s Tallgrass Prairie Preserve (3650’N, 9625’W) and used six acoustic indices to quantify the ratio of technophony to biophony, acoustic complexity, diversity, evenness, entropy, and biological acoustic diversity from over 70,000 sound recordings. Acoustic index values were used to determine the relationship between Nicrophorus burying beetle species composition and the prairie soundscape (Chapter 1) and to determine if prescribed burning changes the composition of the soundscape over time (Chapter 2). In Chapter 1, I found that associations between Nicrophorus burying beetles and the soundscape were unique to particular species, acoustic indices and times of day. For example, N. americanus trap rates showed a positive correlation to areas of increased acoustic complexity specifically at dawn. In addition to positive associations with the soundscape, we found that N. marginatus was consistently negatively correlated to higher levels of biophony, while N. tomentosus was consistently positively correlated to places with higher levels of biophony. Although reproduction of all species examined is dependent upon securing small carrion for reproduction, I found that known habitat and activity segregation of five Nicrophorus beetle species may be reflective of the soundscape. Finally, I show that favorable habitat for a critically endangered necrophilous insect, the American burying beetle (Nicrophorus americanus) can be identified by the acoustic signature extracted from a short temporal window of its grassland ecosystem soundscape. Using the same suite of acoustic indices from Chapter 1, in Chapter 2 I examined acoustic recordings at a much larger time scale to determine distinctive acoustic events driven by biophony and geophony across a 23-week period. In addition to examining acoustic changes over time, I examined differences between 11 burned and unburned pastures. Results from this study indicate that prescribed burning does alter the soundscape, especially early in the post-burn period, but the effects are ameliorated by a significant increase in biophony as the growing and breeding season progressed into the warmer summer months. Both studies demonstrate that passive acoustic recording is a reliable method to assess relationships to acoustic communities over space and time