Automatic recognition of lactating sow behaviors through depth image processing

Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shifting) in farrowing crate. The system consisted of a low-cost 3D camera that simultaneously acquires digital and depth images and a software program that detects and identifies the sow’s behaviors. This paper describes the computational algorithm for the analysis of depth images and presents its performance in recognizing the sow’s behaviors as compared to manual recognition. The images were acquired at 6 s intervals on three days of a 21-day lactation period. Based on analysis of the 6 s interval images, the algorithm had the following accuracy of behavioral classification: 99.9% in lying, 96.4% in sitting, 99.2% in standing, 78.1% in kneeling, 97.4% in feeding, 92.7% in drinking, and 63.9% in transitioning between behaviors. The lower classification accuracy for the transitioning category presumably stemmed from insufficient frequency of the image acquisition which can be readily improved. Hence the reported system provides an effective way to automatically process and classify the sow’s behavioral images. This tool is conducive to investigating behavioral responses and time budget of lactating sows and their litters to farrowing crate designs and management practices

Infrared proximity measurement system development and validation for classifying sow posture

The rapidly progressing field of precision livestock farming is becoming increasingly dependent on the utilization of camera technology. Integration of camera technology involves substantial intellectual input and computational power to acquire, process, and interpret images in real-time. Further, cameras and the necessary computational power can be cost-prohibitive and subsequently, become a constraint for application in a commercial livestock and poultry production systems. The purpose of this study is to develop an infrared proximity sensor based system to serve as a substitute a camera system to perform real-time monitoring of sow posture in farrowing stalls for a potentially lower cost and computational power. Monitoring sow posture can provide producers an indicator of farrowing and aid in evaluating sow demeanor during lactation. During the development of this system the long range infrared (IR) proximity sensors were individually calibrated, a sow posture algorithm was developed, and the IR-Sow Posture Detection System (IR-SoPoDS) system was evaluated in a commercial setting to a Kinect V2® camera for a range of sow postures. Average accuracy of the sow posture algorithm on the training data was found to be 96%. The overall accuracy of the IR-SoPoDS system across the three sow frame sizes were:87% (small), 90% (medium), and 89% (large). This IR-SoPoDS system shows a strong promise for further development for sow posture and behavior detection in the farrowing stall environment

Sow lying behaviors before, during and after farrowing

Piglet pre-weaning mortality remains a considerable challenge for the swine industry, representing one of the key areas where animal well-being and economical interest coincide. Sows and piglets carry out a complex series of behaviors during the farrowing/lactation period. These behaviors during the first few days after parturition are extremely important for piglet survival, and they can be greatly impacted by the farrowing system, environment, and/or management. The risk of sow crushing is much greater for piglets when the sow changes her postures. Limited studies have investigated the effects of environment on sow’s posture changes or basic understanding of sow’s lying or other behavior patterns. Using a computer vision and analysis system, this study aims to characterize sows’ postural behaviors before, during and after farrowing to ultimately reduce pre-weaning piglet mortality and to understand the relationship between placement of localized heat source (heat lamp) and its impact on sows’ lying preference, if any. Analysis of data with 15 sows thus far reveals the following preliminary observations. The sows do not seem to have a preference of lying on one side vs. the other before farrowing regardless of absence or presence of a heat lamp on the side. However, heat lamp in the creep area significantly affects the sows’ lying side in the first 3 days after farrowing. Interestingly, the lactating sows demonstrated the postural behavior of facing more of her backside toward the heat lamp relative to before farrowing. Such a behavior would not be in the best interest of the piglets’ well-being. The presence of heat lamp during the lactation period seemed to have some carryover effect on the sow’s lying posture when the heat lamp was tuned off with elder piglets. Sows change their behaviors (lying, sitting, standing, and movement) over the farrowing cycle. In particular, sow’s behaviors change sharply 24 h prior to farrowing, making it is possible to predict farrowing time by analyzing the behavioral changes with the automatic tracking system. More data are being collected

Supervisión en continuo de porcino en cebo mediante sistema multi-sensor: patrones de comportamiento

El manejo de las explotaciones ganaderas ha de conjugar criterios de rentabilidad con aspectos relativos al bienestar y a la salud animal, estando obligados a llegar a soluciones de compromiso cada vez más complejas. En esta situación el sector busca soluciones para recabar y manejar datos e información sobre sus instalaciones y animales, tendentes a la ganadería de precisión. Surge así el concepto de fenotipado masivo de animales, en el que se busca el registro de parámetros significativos (temperatura, movimientos, sonidos, etc.) relacionados con aspectos de bienestar, salud o productividad.En este trabajo se ha seguido un periodo de cebo (81 días) de un total de 30 cerdos Landrace repartidos en dos boxes de un núcleo perteneciente a Hendrix Genetics en Villatobas (Castilla-La Mancha). La supervisión individual de cada individuo ha consistido en el registro cada 3 minutos de la temperatura superficial mediante un logger-sensor (Ibutton) colocado en el crotal, y en el registro de la ingesta y el peso del animal en cada visita a una estación automatizada con báscula de pesaje. Las condiciones ambientales se han monitorizado mediante 6 registradores Ibutton dotados con sensores de temperatura y humedad relativa distribuidos en los boxes.Complementariamente, y para verificar cualquier anomalía que pudiese producirse durante el periodo analizado, se instaló una cámara de bajo coste para el registro de imágenes RGB, infrarrojas y de profundidad con una frecuencia de 60 segundos. La cámara se instaló a 4 metros de altura para conseguir la vista completa de uno de los boxes.En la serie temporal completa de las temperaturas superficiales se ha observado una relación negativa entre la media y la desviación típica (r >0.8): los animales con valores altos de temperatura muestran menor variabilidad térmica. El análisis no supervisado de estas series temporales ha identificado 5 grupos basados en esta relación. Se han identificado también diferentes patrones entre animales en las pautas de alimentación evaluando ingesta por visita, duración de la visita y ciclos día-noche.El análisis conjunto de los registros de temperatura y los proporcionados por las estaciones de alimentación han permitido la identificación de patrones distintos característicos de tipologías de animales o de situaciones anómalas, como una subida repentina de la temperatura ambiental. Todo lo cual, integrado en las bases de datos habituales en las explotaciones, es una contribución relevante a los protocolos de fenotipado masivo

Multi-Pig Part Detection and Association with a Fully-Convolutional Network

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download

Tracking agonistic behaviors in pigs

Master of ScienceDepartment of Animal Sciences and IndustryLindsey E HulbertModern day animal production is intensively increasing to meet global demand for animal products. Producers must balance the increased demand for animal product and instill trust in consumers. Pigs raised in intensive production system display more fighting and unresolved conflict than wildtype pigs. This conflict is called “agonistic interactions”. These undesired behaviors occur mainly at the finishing stage of pigs when resources (water, food, space etc.) becomes limited or when animals meet unfamiliar pen mates. Chronic stress from unresolved conflict is an indication of poor animal welfare and may lead to reduced product quality. The first step in reducing the conflict is finding an efficient system to detect and track pigs at the individual level. Precision animal management is the incorporation of information technology into animal production to monitor animals online, which are supported with artificial intelligence to collect and analyze data that will help to sustainably improve livestock farming. While many systems exist, visual tracking has a great potential for commercial application because it is the least invasive. These systems will, therefore, be useful to producers by providing an early detection of agonistic behaviors in herd, provide timely intervention to compromised animals thereby increasing economic gains

Automatic recognition of lactating sow behaviors through depth image processing

Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shifting) in farrowing crate. The system consisted of a low-cost 3D camera that simultaneously acquires digital and depth images and a software program that detects and identifies the sow’s behaviors. This paper describes the computational algorithm for the analysis of depth images and presents its performance in recognizing the sow’s behaviors as compared to manual recognition. The images were acquired at 6 s intervals on three days of a 21-day lactation period. Based on analysis of the 6 s interval images, the algorithm had the following accuracy of behavioral classification: 99.9% in lying, 96.4% in sitting, 99.2% in standing, 78.1% in kneeling, 97.4% in feeding, 92.7% in drinking, and 63.9% in transitioning between behaviors. The lower classification accuracy for the transitioning category presumably stemmed from insufficient frequency of the image acquisition which can be readily improved. Hence the reported system provides an effective way to automatically process and classify the sow’s behavioral images. This tool is conducive to investigating behavioral responses and time budget of lactating sows and their litters to farrowing crate designs and management practices.This article is from Computers and Electronics in Agriculture 125 (2016): 56–62, doi:10.1016/j.compag.2016.04.026.</p

Evaluation of swine gestation-farrowing facility space and management for improving production, welfare, and infectious disease containment

The United States (US) swine industry plays an important role in providing a safe and reliable source of animal proteins for a growing world population. As the industry evolves and society advances, producers face new and complex challenges such as optimizing animal production, welfare, and health. This dissertation contributes novel evidence-based knowledge to address current swine housing and management challenges in several key areas that formed the objectives of this dissertation, which were to: develop a computer vision system to monitor sow behavior in farrowing stalls (Chapter 2), evaluate the impacts of farrowing stall layout and number of heat lamps on sow and piglet productivity (Chapter 3) and behavior (Chapter 4), quantify the static and dynamic space usage of late gestation sows (Chapter 5), and determine supplemental heat requirements to implement ventilation shut down plus and virus inactivation (Chapter 6). The research presented in this dissertation contains the following discoveries. In Chapter 2, a large-scale computer vision system was established and implemented to simultaneously and continually monitor 60 farrowing stalls. The semi-automatic image processing algorithm achieved sow posture classification accuracies of \u3e99.2% (sitting: 99.4%, standing: 99.2%, kneeling: 99.7%, lying: 99.9%) and \u3e97% accuracy for sow behaviors (feeding: 97.0%, drinking: 96.8%, other: 95.5%). The computer vision system provided the foundation for carrying out the subsequent study concerning the impact of farrowing stall layout and management strategies. It was revealed in Chapter 3 that farrowing stall physical dimensions and number of heat lamps for localized heating did not significantly impact the percentage of pre-weaning mortality, overlay, number of piglets born alive, number weaned, average daily weight gain, or litter uniformity. Stall layout did significantly influence percent stillborn; however, the difference was not of practical significance. While experimental treatment did not significantly impact production outcomes, there were significant sow and piglet behavioral differences which are reported in Chapter 4. It was found that sows in wider stalls spend more time lying down and less time sitting. Piglets in stall layouts with expanded creep areas spent more time in the creep and less time near the sow compared to traditional stall layouts. Further, when two heat lamps were used sows spent significantly more time lying and piglets spent a greater proportion of time in the heated areas. Static and dynamic space usage of individually housed gestating sows was quantified and reported in Chapter 5. An average 228 kg sow requires stall dimensions of 196 × 115 × 93 cm (L × W × H) to provide uninhibited space. To accommodate average to 95th percentile (267 kg) sows, minimum stall dimensions need to be 204 × 112 × 95 cm. The 95th percentile sow space usage had a 4% decrease in length, 84% increase in width, and 5% decrease in height compared to typical gestation stall dimensions. Chapter 6 describes the development of a model to predict minimum supplemental heat requirements for ventilation shut down plus and virus inactivation (VSD+). Tables are presented with heating values needed to achieve greater than 95% mortality within 1 h of VSD onset, as well as for virus inactivation for African Swine Fever (ASF). Requirements of supplemental heat for various pig body weights, ambient conditions, facility air tightness, and stages of production are estimated. Overall, this dissertation provides information to fill knowledge gaps regarding current challenges in the US swine industry. Results can be used to guide producers as they strive to provide safe and reliable pork for the growing world population while safeguarding wellbeing of the animals

X Congreso Ibérico de Agroingeniería = X Congresso Ibérico de Agroengenharia : Libro de actas = Livro de atas

In 2017, the Food and Agriculture Organization (FAO) issued a report on the challenges that Agriculture is facing and will face into the 21st century, which can be summarized in one question: will we be able to sustainably and effectively feed everyone by 2050 and beyond, while meeting the additional demand for agricultural commodities due to non- food uses? Agricultural engineers can contribute in this process by releasing the biological and technical constraints on crop and animal productivity, reducing the contribution of the agricultural sector to environmental degradation, and enabling agricultural practices to adapt to environmental changes. To achieve optimal results for agribusiness and the society, the expertise of agricultural engineers must be integrated with expertise from other sciences: breakthrough technologies are needed for agricultural enterprises to meet the increasing list of standards and norms in the areas of energy, animal welfare, product quality, water, and volatile emissions. Recognition of trends in society and networking and participation in debates have thus become important activities for agricultural engineers. The Iberian Agroengineering Congress series brings together Spanish and Portuguese engineers, researchers, educators and practitioners to present and discuss innovations, trends, and solutions to the aforementioned challenges in the interdisciplinary field of Agricultural and Biosystems Engineering. This biennial congress, jointly organized by the Spanish Society of Agroengineering and the Specialized Section of Rural Engineering of the Sociedade de Ciências Agrárias de Portugal, has proven to be an excellent opportunity to network and discuss future developments. In its 10th edition, the Congress has been held from 3-6 September in Huesca (Spain), at the Escuela Politécnica Superior, located on the Huesca Campus of the University of Zaragoza. The topics of the Congress have included the main areas of Agricultural Engineering: mechanization; soils and water; animal production technology and aquaculture; rural constructions; energy; information technologies and process control; projects, environment, and territory; postharvest technology; and educational innovation in agroengineering. The Congress has received 123 participants, who have submitted 144 papers, 86 oral communications and 58 poster. 22 universities, 4 research centers and 8 companies/professional associations have been represented. The quality of the papers presented to the congress is endorsed not only by the long trajectory of the Iberian Agroengineering Congress, but also by the edition of a Special Issue of Agronomy journal (ISSN 2073-4395) entitled “Selected Papers form 10th Iberian Agroengineering Congress”