A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle

Abstract

The monitoring of farm animals is important as it allows farmers keeping track of the performance indicators and any signs of health issues, which is useful to improve the production of milk, meat, eggs and others. In Europe, bovine identification is mostly dependent upon the electronic ID/RFID ear tags, as opposed to branding and tattooing. The RFID based ear-tagging approach has been called into question because of implementation and management costs, physical damage and animal welfare concerns. In this paper, we conduct a case study for individual identification of Holstein cattle, characterized by black, brown and white patterns, in collaboration with the Dairy campus in Leeuwarden. We use a FLIR E6 thermal camera to collect an infrared and RGB image of the side view of each cow just after leaving the milking station. We apply a fully automatic pipeline, which consists of image processing, computer vision and machine learning techniques on a data set containing 1237 images and 136 classes (i.e. individual animals). In particular, we use the thermal images to segment the cattle from the background and remove horizontal and vertical pipes that occlude the cattle in the station, followed by filling the blank areas with an inpainting algorithm. We use the segmented image and apply transfer learning to a pre-trained AlexNet convolutional neural network. We apply five-fold cross-validation and achieve an average accuracy rate of 0.9754 ± 0.0097. The results obtained suggest that the proposed non-invasive approach is highly effective in the automatic recognition of Holstein cattle from the side view. In principle, this approach is applicable to any farm animals that are characterized by distinctive coat patterns.

Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning

“…exploiting manually-delineated features extracted on the coat [26] (similar to a later work by Li, W. et al [25]), which was outperformed by a deep approach using convolutional neural networks extracting features from entire image sequences [27,28,29], similar to [48,49]. More recently, there have been works that integrate multiple views of cattle faces for identification [45], utilise thermal imagery for background subtraction as a pre-processing technique for a standard CNN-based classification pipeline [50], and detect cattle presence from UAV-acquired imagery [45]. In this work we continue to exploit dorsal biometric features from coat patterns exhibited by Holstein and Holstein-Friesian breeds as they provably provide sufficient distinction across populations.…”

The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence

“…On the other hand, body recognition methods have been developed to identify cows within a herd using computer vision and deep learning techniques. Within the proposed methods are cattle recognition from the side (Bhole et al, 2019), from behind (Qiao et al, 2019), different angles (de Lima Weber et al, 2020) or from the top (Andrew et al, 2019). The latter was proposed to identify and recognize Holstein and Friesian cattle using an unmanned aerial vehicle (UAV) (Andrew et al, 2017(Andrew et al, , 019, 2020a(Andrew et al, , 2020b.…”

“…The latter was proposed to identify and recognize Holstein and Friesian cattle using an unmanned aerial vehicle (UAV) (Andrew et al, 2017(Andrew et al, , 019, 2020a(Andrew et al, , 2020b. Bhole et al (2019) proposed an extra step for cow recognition from the side by recording IRTIs to ease the image segmentation and remove the background.…”

Characteristics in the Spatiotemporal Variation and Periodic Evolution of Groundwater in the Xining Area of China, Eastern Qinghai–Tibet Plateau

“…Non-touch identity recognition We investigated the primary research methods in the field of cattle recognition. From the retina [1] and iris [2] of the cattle's eyes to the nose print and the overall individual [3,4], existing algorithms are predominantly focused on artificially captivity environments, making them challenging to apply in the large-scale wild environment. Due to various environmental factors in the wild, such as different lighting conditions and diverse orientations of cattle faces, the quality of cattle images captured in wild environments is suboptimal, and the background is highly intricate.…”