Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition

Reconocimiento automático de la actividad de vacunos en pastoreo

The use of collars, pedometers or activity tags is expensive to record cattle's behavior in short periods (e.g. 24h). Under this particular situation, the development of low-cost and easy-to-use technologies is relevant. Similar to smartphone apps for human activity recognition, which analyzes data from embedded triaxial accelerometer sensors, we develop an Android app to record activity in cattle. Four main steps were followed: a) data acquisition for model training, b) model training, c) app deploy, and d) app utilization. For data acquisition, we developed a system in which three components were used: two smartphones and a Google Firebase account for data storage. For model training, the generated database was used to train a recurrent neural network. The performance of training was assessed by the confusion matrix. For all actual activities, the trained model provided a high prediction (> 96 %). The trained model was used to deploy an Android app by using the TensorFlow API. Finally, three cell phones (LG gm730) were used to test the app and record the activity of six Holstein cows (3 lactating and 3 non-lactating). Direct and non-systematic observations of the animals were made to contrast the activities recorded by the device. Our results show consistency between the direct observations and the activity recorded by our Android app.El uso de podómetros o collares para registrar el comportamiento del ganado en períodos cortos de tiempo (e.g. 24 h) es costoso. En esta situación particular, el desarrollo de tecnologías de bajo costo y fáciles de usar es relevante. Al igual que las aplicaciones de teléfonos inteligentes para el reconocimiento de la actividad humana, las cuales analizan datos de sensores de aceleración integrados, en este trabajo desarrollamos una aplicación de Android para registrar la actividad del ganado. Para el desarrollo de esta aplicación, se siguieron cuatro pasos principales: a) adquisición de datos para el entrenamiento del modelo, b) entrenamiento del modelo, c) desarrollo de la aplicación y d) utilización de la aplicación. Para la adquisición de datos, desarrollamos un sistema en el que se utilizaron tres componentes: dos teléfonos inteligentes (uno en la vaca y otro para el observador) y una cuenta de Google Firebase para el almacenamiento de datos. Para el entrenamiento del modelo, la base de datos generada se utilizó para entrenar una red neuronal recurrente. El rendimiento del entrenamiento se evaluó mediante la matriz de confusión. Para todas las actividades, el modelo entrenado proporcionó una predicción alta (> 96 %). El modelo entrenado se utilizó para desarrollar una aplicación de Android con la API de TensorFlow. Finalmente, se utilizaron tres teléfonos celulares (LG gm730) para registrar la actividad de seis vacas Holstein (3 en producción y 3 secas). Se realizaron observaciones directas y no sistemáticas de los animales para contrastar las actividades registradas por el dispositivo. Los resultados mostraron coherencia entre las observaciones directas y la actividad registrada por el dispositivo

An Optimized Kappa Architecture for IoT Data Management in Smart Farming

peer reviewedAgriculture 4.0 is a domain of IoT in full growth which produces large amounts of data from machines, robots, and sensors networks. This data must be processed very quickly, especially for the systems that need to make real-time decisions. The Kappa architecture provides a way to process Agriculture 4.0 data at high speed in the cloud, and thus meets processing requirements. This paper presents an optimized version of the Kappa architecture allowing fast and efficient data management in Agriculture. The goal of this optimized version of the classical Kappa architecture is to improve memory management and processing speed. the Kappa architecture parameters are fine tuned in order to process data from a concrete use case. The results of this work have shown the impact of parameters tweaking on the speed of treatment. We have also proven that the combination of Apache Samza with Apache Druid offers the better performances

Chapter 3: Herdsman+: artificial intelligence enabled systems and services for livestock farming : Herdsman+ artificial intelligence enabled systems and services for livestock farming

The application of artificial intelligence coupled with the growth in the availability of cost- effective low power computing platforms, has accelerated the adoption of on-farm technologies that support the decision making of farmers. An exemplar of the evolution is encapsulated by the development of activity monitors for dairy cattle, migrating from simple step counting devices designed to identify the onset of oestrus to systems that continuously monitor individual cattle and provide insights into the time spent eating, ruminating, calving and other key welfare events such as lameness and mastitis. The Chapter illustrates how the use of digital technologies has brought benefit to the livestock farming industry, presenting the current state-of-the-art with emphasis on accentuating the potential for cloud based platforms to support the integration of multiple on-farm data streams, the foundation for the provision of a mix of data-driven animal-centric services that bring further benefits to the livestock community

Development of an open-source algorithm based on inertial measurement units (IMU) of a smartphone to detect cattle grass intake and ruminating behaviors

In this paper, an open algorithm was developed for the detection of cattle’s grass intake and rumination activities. This was done using the widely available inertial measurement unit (IMU) from a smartphone, which contains an accelerometer, a gyroscope, a magnetometer and location sensors signals sampled at 100 Hz. This equipment was mounted on 19 grazing cows of different breeds and daily video sequences were recorded on pasture of different forage allowances. After visually analyzing the cows’ movements on a calibration database, signal combinations were selected and thresholds were determined based on 1-s time windows, since increasing the time window did not increase the accuracy of detection. The final algorithm uses the average value and standard deviation of two signals in a two-step discrimination tree: the gravitational acceleration on x-axis (Gx) expressing the cows’ head movements and the rotation rate on the same x-axis (Rx) expressing jaw movements. Threshold values encompassing 95% of the normalized calibrated data gave the best results. Validation on an independent database resulted in an average detection accuracy of 92% with a better detection for rumination (95%) than for grass intake (91%). The detection algorithm also allows for characterization of the diurnal feeding activities of cattle at pasture. Any user can make further improvements, for data collected at the same way as the iPhone’s IMU has done, since the algorithm codes are open and provided as supplementary data.AgricultureIsLife 1

Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data

peer-reviewedPrecision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming

Digital tillsynsteknik i djurhållning utomhus

I enlighet med Jordbruksverkets förfrågan behandlar denna rapport tre områden för digital teknik vid övervakning och kontroll av djur som vistas utomhus på stora ytor: (1) kamerateknologi, t.ex. användning av drönare, (2) positioneringsteknologi som GPS och (3) teknologi för att styra djurens rörelser, som drivning med drönare och användning av s.k. virtuella stängsel. De tre teknikområdena överlappar delvis varandra. Digital tillsyn av utegående djur är beroende av att sensorer mäter det man tror att de mäter med tillräcklig noggrannhet och att data kan överföras och bearbetas till information som lagras och analyseras på ett säkert och korrekt sätt. Sådana teknologier benämns med samlingsnamnet ’Precision Livestock Farming’ (PLF). Användningen av informationen är avgörande för teknikens användbarhet i tillsyns- och djurskyddsarbete. Tillämpningarna är till viss del reglerade av gällande lagstiftning, exempelvis genom krav på tillsyn, begränsad användning av elektricitet för att styra djurs beteende, användning av obemannade luftfarkoster, d.v.s. drönare, samt åtgärder för att förhindra att utrustning skadar djuren eller påverkar deras hälsa och beteende. Inom PLF används en rad olika sensorer som direkt eller indirekt kan mäta djurens miljö och djurens beteende och fysiologiska tillstånd. Den teknologiska utvecklingen har främst varit inriktad på mjölkkor, fjäderfän och grisar och endast i liten utsträckning berört häst, får och get. För djur på bete är överföringen av data från en enhet på eller vid djuret till en mottagare särskilt problematisk p.g.a. stora avstånd, men det sker en snabb teknisk utveckling mot effektivare överföring. PLF-teknologin innebär i de flesta fall att djuren övervakas kontinuerligt och att avvikelser i t.ex. deras hälsotillstånd och välfärd i princip kan upptäckas i realtid, vilket ska ställas mot nuvarande lagkrav på tillsyn minst en eller två gånger dagligen. Sensorer kan ge information om ett stort antal fysiologiska tillstånd och beteenden. En av de vanligaste teknikerna är sensorer för aktivitet. Indirekt kan de också ge information om idissling, liggtid, stegantal och ättid och utlösa larm om exempelvis brunst, hälsoproblem, hälta och kalvning. Sensorer kan även placeras i förmagen hos idisslare (s.k. våmbolus) där de mäter våm-pH och kan larma om störningar i magfunktionen, eller utformas som termometrar som kan larma om hälsostörningar, kalvning och vattenintag eller mikrofoner som kan mäta idissling och larma om brunst, kalvning och onormalt idisslingsmönster. Med kamerateknik kan man mäta aktivitet, kroppsform och hudtemperatur, vilket kan ge information om ketosstatus, hull, hälta och juverhälsa. Kameror monterade på drönare kan användas för att lokalisera och räkna djur, bestämma deras position, habitatval och till viss del deras beteende, särskilt när djuren rör sig över stora arealer. Det finns flera elektroniska positioneringsteknologier varav passiv ’Radio Frequency Identification RFID’ är den vanligaste. Räckvidden är kort med denna teknik men den kan vara användbar om man t.ex. vill mäta hur ofta djuren besöker en vattenpost. Andra teknologier kan med hjälp av antenner följa djurens positioner i realtid. GPS-enheter monterade i halsband kan regelbundet registrera djurens geografiska position. Användningen av GPS har blivit relativt vanlig i renskötseln vilket tycks ha lett till en förbättrad arbetssituation för renskötarna. Positionering med GPS ger inte alltid exakta uppgifter men tekniken har visat sig användbar för studier av habitatval, sociala interaktioner och gruppdynamik. Med positionerna från GPS har man också kunnat styra djur till områden med bättre betestillgång. Med en tillräckligt frekvent bestämning av position med hjälp av GPS (ca en gång per minut) är det möjligt att bestämma betestiden för nötkreatur på ett tillförlitligt sätt. En användning av drönare i djurskötsel och djurtillsyn kan vara att med hjälp av kamera lokalisera djuren över stora ytor. Denna användning begränsas dock av nuvarande bestämmelser om att föraren måste ha ögontakt med drönaren. I renskötseln har drönare börjat användas för att förflytta djur men denna tillämpning är ännu inte juridiskt reglerad. Virtuella stängsel är strukturer som bestäms med kartkoordinater eller elektronisk sändare på marken. Stängslen fungerar som inhägnader, hinder eller gränser. Djuren mottar signaler (vanligen ljud) och stimuli (vanligen elstötar från ett halsband) som gör det möjligt för dem att lära sig var stängslet finns. I vetenskapliga studier har man med varierande framgång lyckats lära djuren att associera ljudsignaler och elstötar med en gräns som inte får passeras. Förmågan att lära sig skiljer mellan olika djurslag, liksom mellan individer. Det finns fortfarande många obesvarade frågeställningar om hur djur kan anpassa sig till virtuella stängselsystemet, liksom hur de påverkas, både under inlärningsfas och bruksfas

GRAZING MANAGEMENT STRATEGIES ADAPTED TO DAIRY CATTLE ON PASTURE IN THE ECUADORIAN SIERRA

The Ecuadorian inter-Andean valley maintains large agricultural areas called haciendas whose main activity is milk production, surrounding these, we find small and medium producers also considered ranchers. Although both are oriented towards the same markets and implement a clear trend towards intensive production systems, they show a marked difference in the dynamics of productive activity despite sharing similar constraints in term namely of altitude and marked slopes for their pastures. To enhance productive yields, the most extensive and effective application of improved management is sought for by farmers. Options to reach this objective include the composition of herds, the size of the paddocks, the stocking rate and resting times of the meadows, the use of fertilizers, an efficient combination between agricultural crops and pasture renewal and stoking management methods, the latter being possibly one low cost short-term action lever to act upon in order to potentiate dairy farming productivity. However, it is difficult to predict the efficiency and profitability of such efforts, particularly when there is such a distant economic and cultural gap between ranchers in the same country. To the best of our knowledge, the link between grazing management and milk productivity has not been documented in high-relief situations. Our thesis aims to analyze the impact of stocking management methods on the productive performance of grazing cows, in intensive milk production in the Ecuadorian highlands. In addition to analyzing the influence of the relief in the decision making for the conformation of paddocks, in the context of different degrees of slope on the properties. We hypothesized that a grazing management system can be found that is better adapted to the organizational practices of dairy systems in the Ecuadorian tropical highlands, as well as identify some practices that better compensate for the detrimental effects of slopes on animal productivity. To do this, first, 42 milk-producing farms were characterized in different cantons of the rural area of the province of Pichincha (Quito, Mejía, Rumiñahui and Cayambe) of Ecuador. Through a questionnaire to identify the productive and management activities in the herds and evaluate the average slope of the pastures of the farms based on GIS data. The results showed that the farms had an average area of 40 ha, the herds were composed of 60 ± 63 milking cows, predominantly of the Holstein Friesian breed (65 %), and the daily production of cows in milk reached 15.1 ± 3.4 kg. The highest productivity was found in the farms using rotational stocking with high intensity of instantaneous grazing with very short occupation times (< 12 h), cultural tasks in the meadows (reseeding, resting time, equalization cuts, soil aeration, fertilization, manure dispersion) and a flat topography of the pastures (p < 0.05). The steepness of the slopes was not a limitation to establish pastures for grazing animals since pastures were observed in the entire range of slopes, including very steep ones (up to 55 %). The daily production of individual cows was negatively correlated (r = - 0.323, p = 0.037) with the average slope of the surveyed farms. Subsequently, we conducted two grazing experiments to determine the ingestive behavior of dairy cows, under the types of pasture rotation mostly used by farmers in the survey (from rotational stocking with long occupation time to grazing with very short occupation times of 3 hours), to test the relevance of rotations with shorter occupation times on the performance of the system. A first experiment was done on flat paddocks applying three rotational stocking contrasting treatments ranging from very short to long occupation times: three hours, 24 hours and seven days respectively of 7 days. Cows in the long occupancy time treatment spent more time eating, tended to have a higher average speed during forage intake, attributed to a greater displacement per exploration of the entire area assigned for the experimentation time. In the 3-h treatment, greater inactivity was perceived in anticipation of the opening of new areas for grazing during the day. Despite these differences in activity, milk production did not differ in quantity or quality (ie, fat, protein, non-fat solids, total solids). Showing that under grazing conditions with an intermediate forage allocation on flat paddocks and with low producing cows, the application of a labor-intensive stocking method that requires opening new areas every 3 hours does not lead to a significant increase in the production. Next, we carried out a second grazing experiment in which two stocking methods (long occupancy and very short occupation) on a terrain with moderate relief and with up and downhill displacement of cows on the pastures to harvest the forage. Results showed that cows that grazed the very short time treatment moved more during meals than those placed in the long occupation time treatment. This is explained by the fact that the sub-paddocks 3 hours were designed horizontally to favor lateral walking and avoid the effect of the slope on displacement. While the herd that had freedom of movement throughout the paddock (long occupation time), traveled less (-27 %), leaving higher stubble height in postgrazing (7 cm). Higher volumes and concentration in solids were found in milk for the herd that grazed in the treatment with assignment of new subpaddocks every three hours. In conclusion, the combination of grazing management systems with operations that better compensate for the detrimental effects of slopes promote productive yields in dairy farms in the Ecuadorian highlands. The allocation of forage material must be based on a rotation with occupation times that adjust to the slope of the paddocks. Avoiding the unnecessary use of human and economic capital where it does not justify the implementation of shorter rotation times (flat paddocks), guaranteeing the optimization of resources, higher volumes and better quality of the milk produced. Finally, farmers can manage their agricultural processes using the proposals developed in this research according to the resources available in their environment

Simulación de la emisión de metano entérico en vacas Holstein en un sistema de producción de leche en el norte de Antioquia, Colombia

RESUMEN: En el presente trabajo se desarrolló una plataforma web para simular la producción de metano (CH 4 ) entérico en vacas Holstein en un sistema de producción de leche en el norte de Antioquia, Colombia. La plataforma integra tres modelos: El primero, predice la producción de pasto Kikuyo (Cenchrus clandestinus Hochst. ex Chiov) a partir de condiciones climáticas; el segundo, simula la dinámica de consumo en animales en pastoreo; y el tercero, estima la emisión de CH 4 a partir del consumo de alimento. La información necesaria para el desarrollo de la plataforma fue obtenida en la hacienda la Montaña de la Universidad de Antioquia. Para predecir la producción de pasto, se calibró un modelo existente: BASGRA (Basic Grass Model); en este proceso se utilizaron datos de producción de pasto y datos climáticos. Para simular el consumo, se desarrolló un Modelo Basado en Agentes en el programa Unity3D a partir de la observación del comportamiento ingestivo de las vacas. Para estimar la producción de CH 4 , se calibró un modelo de ecuaciones diferenciales, con datos de consumo y producción de CH 4 obtenidos en cámaras respirométricas. La validación de este modelo se realizó con una base de datos reconstruida a partir de artículos científicos. Los resultados indican que es posible desarrollar plataformas computacionales para simular la producción de CH 4 entérico en vacas Holstein en pastoreo, teniendo en cuenta la disponibilidad de pasto y el comportamiento ingestivo de los animales en pastoreo

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%