A Non-Contact Cow Estrus Monitoring Method Based on the Thermal Infrared Images of Cows

Milk production plays an essential role in the global economy. With the development of herds and farming systems, the collection of fine-scale data to enhance efficiency and decision-making on dairy farms still faces challenges. The behavior of animals reflects their physical state and health level. In recent years, the rapid development of the Internet of Things (IoT), artificial intelligence (AI), and computer vision (CV) has made great progress in the research of precision dairy farming. Combining data from image, sound, and movement sensors with algorithms, these methods are conducive to monitoring the behavior, health, and management practices of dairy cows. In this review, we summarize the latest research on contact sensors, vision analysis, and machine-learning technologies applicable to dairy cattle, and we focus on the individual recognition, behavior, and health monitoring of dairy cattle and precise feeding. The utilization of state-of-the-art technologies allows for monitoring behavior in near real-time conditions, detecting cow mastitis in a timely manner, and assessing body conditions and feed intake accurately, which enables the promotion of the health and management level of dairy cows. Although there are limitations in implementing machine vision algorithms in commercial settings, technologies exist today and continue to be developed in order to be hopefully used in future commercial pasture management, which ultimately results in better value for producers.

Section: • Estrus Behaviormentioning

confidence: 99%

Section: • Estrus Behaviormentioning

confidence: 99%

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

Liu,

Qi,

et al. 2023

“…In 2003, research related to Precision Livestock Farming (PLF) was first compiled during the European Conference on Precision Livestock Farming. The conference primarily focused on animal physiological identification [2][3][4] and monitoring [5,6]. The aim was to optimize individual animal contributions, achieving efficiency in livestock farming at low costs and environmental footprints, while ensuring the quality and safety of livestock products [7].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

Ma,

Qi,

Sun

et al. 2024

Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.

“…Computer Vision Technology (CVT) has been widely used in pasture monitoring as a non-contact intelligent technology [9][10][11][12], and the rapid development of deep-learning technology has enabled CVT-based methods to obtain individual animal information and scene information quickly, accurately, and efficiently, and improve animal welfare [13,14], which is of great significance for the management decision-making of animal farming. Most studies were conducted under normal lighting conditions, ignoring the performance of the proposed methods to achieve target monitoring in low-light or night-time conditions.…”

Section: Introductionmentioning

confidence: 99%

Improved Lightweight Zero-Reference Deep Curve Estimation Low-Light Enhancement Algorithm for Night-Time Cow Detection

Yu,

Guo,

Zhang

et al. 2024

With the advancement of agricultural intelligence, dairy-cow farming has become a significant industry, and the application of computer vision technology in the automated monitoring of dairy cows has also attracted much attention. However, most of the images in the conventional detection dataset are high-quality images under normal lighting, which makes object detection very challenging in low-light environments at night. Therefore, this study proposed a night-time detection framework for cows based on an improved lightweight Zero-DCE (Zero-Reference Deep Curve Estimation) image enhancement network for low-light images. Firstly, the original feature extraction network of Zero-DCE was redesigned with an upsampling structure to reduce the influence of noise. Secondly, a self-attention gating mechanism was introduced in the skip connections of the Zero-DCE to enhance the network’s attention to the cow area. Then, an improved kernel selection module was introduced in the feature fusion stage to adaptively adjust the size of the receptive field. Finally, a depthwise separable convolution was used to replace the standard convolution of Zero-DCE, and an Attentive Convolutional Transformer (ACT) module was used to replace the iterative approach in Zero-DCE, which further reduced the computational complexity of the network and speeded up the inference. Four different object-detection models, YOLOv5, CenterNet, EfficientDet, and YOLOv7-tiny, were selected to evaluate the performance of the improved network and were tested on the night-time dataset before and after enhancement. Experimental results demonstrate that the detection performance of all models is significantly improved when processing night-time image samples through the enhanced Zero-DCE model. In summary, the improved lightweight Zero-DCE low-light enhancement network proposed in this study shows excellent performance, which can ensure that various object-detection models can quickly and accurately identify targets in low-light environments at night and are suitable for real-time monitoring in actual production environments.