An image processing pipeline to segment iris for unconstrained cow identification system

One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.Fil: Larregui, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Cazzato, Dario. : University Of Luxembourg; Luxemburgo. Interdisciplinary Centre For Security Reliability And T; LuxemburgoFil: Castro, Silvia Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin

South Dakota Farm and Home Research

Director’s Comment [p] 2Agricultural Engineering [p] 3Animal and Range Sciences [p] 6Biology [p] 10Diary Science [p] 12Economics [p] 15Home Economics [p] 17Horticulture, Forestry, landscape and Parks [p] 19Microbiology [p] 21Plant Science [p] 24Rural sociology [p] 28Station Biochemistry [p] 30Veterinary science [p] 32Wildlife and fisheries Sciences [p] 32Wildlife and fisheries Sciences [p 3499th Annual report [p] 37https://openprairie.sdstate.edu/agexperimentsta_sd-fhr/1140/thumbnail.jp

Insights from Animal Reproduction

The chapters in this volume of "Insights from Animal Reproduction" address several, particular hot topics in the field of reproduction. The book begins with a comprehensive overview of the cryopreservation of sheep-produced embryos. The following chapter revises the assisted reproductive techniques available for South American wild mammals. Chapter 3 presents the technical procedures necessary to produce transgenic goats. Chapter 4 provides a comprehensive revision of the major molecular determinants of litter size in prolific species. Chapter 5 examines the germ cell determinant transmission, segregation, and function using the zebrafish as a model for germ cell specification in the embryo. Chapter 6 summarizes the current understanding of the molecular and cellular mechanisms regulating the early stages of folliculogenesis. Chapter 7 examines the sperm motility regulatory proteins as a tool to enhance sperm quality in cryopreservation processes. Chapter 8 discusses contemporary knowledge on the effects of extremely low frequency magnetic fields (ELF-MF) on male reproductive function in rodents. Chapter 9 highlights the importance of the cytogenetic evaluation in searching for causes of infertility of phenotypically normal animals, as well as individuals with an abnormal sex development. The last chapter provides evidence that other uterine diseases may be hidden behind the clinical diagnosis of pyometra that in some case may have a poor outcome

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

Statistical perspectives on dependencies between genomic markers

To study the genetic impact on a quantitative trait, molecular markers are used as predictor variables in a statistical model. This habilitation thesis elucidated challenges accompanied with such investigations. First, the usefulness of including different kinds of genetic effects, which can be additive or non-additive, was verified. Second, dependencies between markers caused by their proximity on the genome were studied in populations with family stratification. The resulting covariance matrix deserved special attention due to its multi-functionality in several fields of genomic evaluations

3D video based detection of early lameness in dairy cattle

Lameness is a major issue in dairy cattle and its early and automated detection offers animal welfare benefits together with potentially high commercial savings for farmers. Current advancements in automated detection have not achieved a sensitive measure for classifying early lameness; it remains to be a key challenge to be solved. The state-of-the-art also lacks behind on other aspects e.g. robust feature detection from a cow's body and the identification of the lame leg/side. This multidisciplinary research addresses the above issues by proposing an overhead, non-intrusive and covert 3-Dimensional (3D) video setup. This facilitates an automated process in order to record freely walking Holstein dairy cows at a commercial farm scale, in an unconstrained environment.The 3D data of the cow's body have been used to automatically track key regions such as the hook bones and the spine using a curvedness feature descriptor which operates at a high detection accuracy (100% for the spine, >97% for the hooks). From these tracked regions, two locomotion traits have been developed. First, motivated by a novel biomechanical approach, a proxy for the animal's gait asymmetry is introduced. This dynamic proxy is derived from the height variations in the hip joint (hooks) during walking, and extrapolated into right/left vertical leg motion signals. This proxy is evidently affected by minor lameness and directly contributes in identifying the lame leg. Second, back posture, which is analysed using two cubic-fit curvatures (X-Z plane and X-Y plane) from the spine region. The X-Z plane curvature is used to assess the spine's arch as an early lameness trait, while the X-Y plane curvature provides a novel definition for localising the lame side. Objective variables were extracted from both traits to be trained using a linear Support Vector Machine (SVM) classifier. Validation is made against ground truth data manually scored using a 1–5 locomotion scoring (LS) system, which consist of two datasets, 23 sessions and 60 sessions of walking cows. A threshold has been identified between LS 1 and 2 (and above). This boundary is important as it represents the earliest point in time at which a cow is considered lame, and its early detection could improve intervention outcome, thereby minimising losses and reducing animal suffering. The threshold achieved an accuracy of 95.7% with a 100% sensitivity (detecting lame cows), and 75% specificity (detecting non-lame cows) on dataset 1 and an accuracy of 88.3% with an 88% sensitivity and 92% specificity on dataset 2. Thereby outperforming the state-of-the-art at a stricter lameness boundary. The 3D video based multi-trait detection strives towards providing a comprehensive locomotion assessment on dairy farms. This contributes to the detection of developing lameness trends using regular monitoring which will improve the lack of robustness of existing methods and reduce reliance on expensive equipment and/or expertise in the dairy industry

Agroforestry Opportunities for Enhancing Resilience to Climate Change in Rainfed Areas,

Not AvailableAgroforestry provides a unique opportunity to achieve the objectives of enhancing the productivity and improving the soil quality. Tree systems can also play an important role towards adapting to the climate variability and important carbon sinks which helps to decrease the pressure on natural forests. Realizing the importance of the agroforestry in meeting the twin objectives of mitigation and adaptation to climate change as well as making rainfed agriculture more climate resilient, the ICAR-CRIDA has taken up the challenge in pursuance of National Agroforestry Policy 2014, in preparing a book on Agroforestry Opportunities for Enhancing Resilience to Climate Change in Rainfed Areas at ICAR-CRIDA to sharpen the skills of all stakeholders at national, state and district level in rainfed areas to increase agricultural productivity in response to climate changeNot Availabl