INCORPORATING MACHINE VISION IN PRECISION DAIRY FARMING TECHNOLOGIES

The inclusion of precision dairy farming technologies in dairy operations is an area of increasing research and industry direction. Machine vision based systems are suitable for the dairy environment as they do not inhibit workflow, are capable of continuous operation, and can be fully automated. The research of this dissertation developed and tested 3 machine vision based precision dairy farming technologies tailored to the latest generation of RGB+D cameras. The first system focused on testing various imaging approaches for the potential use of machine vision for automated dairy cow feed intake monitoring. The second system focused on monitoring the gradual change in body condition score (BCS) for 116 cows over a nearly 7 month period. Several proposed automated BCS systems have been previously developed by researchers, but none have monitored the gradual change in BCS for a duration of this magnitude. These gradual changes infer a great deal of beneficial and immediate information on the health condition of every individual cow being monitored. The third system focused on automated dairy cow feature detection using Haar cascade classifiers to detect anatomical features. These features included the tailhead, hips, and rear regions of the cow body. The features chosen were done so in order to aid machine vision applications in determining if and where a cow is present in an image or video frame. Once the cow has been detected, it must then be automatically identified in order to keep the system fully automated, which was also studied in a machine vision based approach in this research as a complimentary aspect to incorporate along with cow detection. Such systems have the potential to catch poor health conditions developing early on, aid in balancing the diet of the individual cow, and help farm management to better facilitate resources, monetary and otherwise, in an appropriate and efficient manner. Several different applications of this research are also discussed along with future directions for research, including the potential for additional automated precision dairy farming technologies, integrating many of these technologies into a unified system, and the use of alternative, potentially more robust machine vision cameras

Universal Bovine Identification via Depth Data and Deep Metric Learning

This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, and training weights for immediate reproducibility. An increase in herd size skews the cow-to-human ratio at the farm and makes the manual monitoring of individuals more challenging. Therefore, real-time cattle identification is essential for the farms and a crucial step towards precision livestock farming. Underpinned by our previous work, this paper introduces a deep-metric learning method for cattle identification using depth data from an off-the-shelf 3D camera. The method relies on CNN and MLP backbones that learn well-generalised embedding spaces from the body shape to differentiate individuals -- requiring neither species-specific coat patterns nor close-up muzzle prints for operation. The network embeddings are clustered using a simple algorithm such as $k$-NN for highly accurate identification, thus eliminating the need to retrain the network for enrolling new individuals. We evaluate two backbone architectures, ResNet, as previously used to identify Holstein Friesians using RGB images, and PointNet, which is specialised to operate on 3D point clouds. We also present CowDepth2023, a new dataset containing 21,490 synchronised colour-depth image pairs of 99 cows, to evaluate the backbones. Both ResNet and PointNet architectures, which consume depth maps and point clouds, respectively, led to high accuracy that is on par with the coat pattern-based backbone

Equine body weight estimation using three-dimensional images

Includes bibliographical references.2015 Summer.Accurately estimating the body weight (BW) of a horse is important in order to make appropriate management and treatment decisions. Most field equine veterinarians and experienced equine people, however, visually estimate BW because large animal scales are impractical for field use due to the weight (>80 kg), size (length >200 cm), and cost (>$1,000). There are some alternative BW estimation methods such as a weight tape or BW estimation using a combination of heart girth and body length measurements. These methods, however, have 5 - 15% or even higher margin of error. According to human studies, there is a high correlation between BW and body volume (BV). Correlation coefficient (R) between these two variables is 0.996-0.998. Our study was designed to develop methods to estimate the BW of horses by using 3D image based BV measurement. 3D imaging technology allows easy and accurate measurement of diverse indices of an object, including the volume. Recent development of Structure-light 3D scanning technology allows 3D scanning of an object as large as 3 by 3 square meter in a short time. In this study, 3D images of 22 and 11 horses were obtained by using 3D scanning (3DScan) and photogrammetry (2Dto3D), respectively. BV and trunk volume (TV) of the horses were measured from the obtained 3D images. Measurements of BW using five conventional methods (visual estimation, 2 weight tapes (Purina, Shell), estimated BW by using heart girth and body length (Carroll’s formula), and a large animal scale) were also conducted, and the data of body condition score (BCS), sex, coat color, and coat type of the horses were collected. Linear regression models to estimate the BW of the horse based on the volume and other independent variables were developed using regression model stepwise selection procedures (P<0.05). Variables selected in 3DScan method were BV, sex, and coat type, and, in 2Dto3D method, BV (TV) was selected. The coefficient of determination of the developed regression models were 0.95 and 0.78-0.82, respectively, and the average percent errors of the predicted BW compared to the true BW of horses were 2.07 % and 2.67 %, respectively. The accuracy of the 3DScan method was significantly more accurate than WT, Carroll’s formual, and VE (P<0.05). 3D image based BW measurement method had higher accuracy and convenience compared to conventional alternative BW measuring methods. Accurate and easy determination of BW using 3D images will allow for regular BW measurement in the field and allow optimal equine health management by equine stakeholders and practitioners. The 3D images obtained in this study were highly detailed. Further graphical analysis of the obtained 3D images will make it possible to use this technology on automatic evaluation of body condition score, equine conformation evaluation, breed registration, and the study of pharmacokinetics and dynamics of newly developed drugs. This research findings may also have utility for application to wild or zoo animals such as the elephant, rhinoceros, or even the tiger where hands on collection of body weight would be challenging

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

Animal Welfare Assessment

This Special Issue provides a collection of recent research and reviews that investigate many areas of welfare assessment, such as novel approaches and technologies used to evaluate the welfare of farmed, captive, or wild animals. Research in this Special Issue includes welfare assessment related to pilot whales, finishing pigs, commercial turkey flocks, and dairy goats; the use of sensors or wearable technologies, such as heart rate monitors to assess sleep in dairy cows, ear tag sensors, and machine learning to assess commercial pig behaviour; non-invasive measures, such as video monitoring of behaviour, computer vision to analyse video footage of red foxes, remote camera traps of free-roaming wild horses, infrared thermography of effort and sport recovery in sport horses; telomere length and regulatory genes as novel biomarkers of stress in broiler chickens; the effect of environment on growth physiology and behaviour of laboratory rare minnows and housing system on anxiety, stress, fear, and immune function of laying hens; and discussions of natural behaviour in farm animal welfare and maintaining health, welfare, and productivity of commercial pig herds

2020 Program and Abstracts for the Celebration of Student Scholarship

Program and Abstracts from the Celebration of Student Scholarship held in the Spring of 2020

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Ganadería de precisión en vacuno de carne

La ganadería de precisión es el conjunto de herramientas que permiten la automatización de las labores de granja y brindan información útil para la toma de decisiones orientadas a la eficiencia productiva del ganado. Esta revisión sistemática identificó las diferentes herramientas de ganadería de precisión probadas en vacuno de carne. Se utilizaron palabras claves que permitieran abarcar las diferentes herramientas existentes en las bases de datos en inglés de Web of Science (WoS) y ProQuest (PQ), utilizándose el gestor bibliográfico EndNote online. De los registros encontrados, se hizo una selección de trabajos relevantes en base al título y el resumen y se accedió posteriormente al trabajo completo de aquellos pre-seleccionados a través del acceso desde la biblioteca de la Universidad de Zaragoza o de búsquedas directas en Google. Finalmente, las 97 publicaciones que se encontraron se clasificaron según la utilidad que ofrecen las herramientas al ganadero en: identificación electrónica, reproducción, peso automático, medidas corporales, rastreo del animal, vallado virtual, monitorización de la salud, bienestar animal, alimentación, rumia, medio ambiente y granjas inteligentes. Según los resultados se pudo concluir que la ganadería de precisión ayuda al ganadero a resolver problemas particulares o más globales de la producción de carne. Sin embargo, es necesario el desarrollo de más estudios para ampliar la información enfocada en ganado vacuno de carne, y desarrollar más herramientas de precisión a nivel comercial o mejorar las existentes, para incentivar la implementación de tecnología en la granja ganadera y que le ayude a producir de manera más sostenible.<br /