CORF3D contour maps with application to Holstein cattle recognition using RGB and thermal images

Livestock management involves the monitoring of farm animals by tracking certain physiological and phenotypical characteristics over time. In the dairy industry, for instance, cattle are typically equipped with RFID ear tags. The corresponding data (e.g. milk properties) can then be automatically assigned to the respective cow when they enter the milking station. In order to move towards a more scalable, affordable, and welfare-friendly approach, automatic non-invasive solutions are more desirable. Thus, a non-invasive approach is proposed in this paper for the automatic identification of individual Holstein cattle from the side view while exiting a milking station. It considers input images from a thermal-RGB camera. The thermal images are used to delineate the cow from the background. Subsequently, any occluding rods from the milking station are removed and inpainted with the fast marching algorithm. Then, it extracts the RGB map of the segmented cattle along with a novel CORF3D contour map. The latter contains three contour maps extracted by the Combination of Receptive Fields (CORF) model with different strengths of push-pull inhibition. This mechanism suppresses noise in the form of grain type texture. The effectiveness of the proposed approach is demonstrated by means of experiments using a 5-fold and a leave-one day-out cross-validation on a new data set of 3694 images of 383 cows collected from the Dairy Campus in Leeuwarden (the Netherlands) over 9 days. In particular, when combining RGB and CORF3D maps by late fusion, an average accuracy of was obtained for the 5-fold cross validation and for the leave–one day–out experiment. The two maps were combined by first learning two ConvNet classification models, one for each type of map. The feature vectors in the two FC layers obtained from training images were then concatenated and used to learn a linear SVM classification model. In principle, the proposed approach with the novel CORF3D contour maps is suitable for various image classification applications, especially where grain type texture is a confounding variable

Holstein Cattle Recognition

The data set consists of 1237 pairs of thermal and RGB (640 x 320 pixels and 320 x 240 pixels) images with 136 classes (i.e. 136 different cows) with a mean of 9 images per class/cow. Each folder name is the collar id of the cattle and contains its respective thermal and RGB images. The data set was collected at the Dairy Campus in Leeuwarden, The Netherlands. In order to explore the temperature values of the thermal images, FLIR tools can be used by installing the software (https://www.flir.com/products/flir-tools/). Due to the large number of files the data set is provided in 5 zip files. The first 4 zip files contain the data of 30 cows each and the 5th zip file contains the data of 16 cows. (201-06-13

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

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

Recognition of Holstein Cattle with Thermal and RGB images

This data set was collected from the Dairy Campus in Leeuwarden (The Netherlands) with a FLIR E6 thermal camera over a period of 9 days. It consists of 3694 images of 383, with each cow represented with an average of 9 images. Each snapshot created two images; 1) RGB and ii) Temperature. The image filenames are in the format [cow_id-4 digits]_[day no-1 digit]_[counter-1 digit]. The timestamp.xlsx file indicates the day number (day 1 to day 9) of when an image in the data set was collected. This allows to design and run leave-one day-out cross validation, the same as we did in our paper. Here is the link to the scripts that reproduce the results reported in the paper, and the following is the link to the GitHub repository that contains all the script

Recognition of Holstein Cattle with Thermal and RGB images

This data set was collected from the Dairy Campus in Leeuwarden (The Netherlands) with a FLIR E6 thermal camera over a period of 9 days. It consists of 3694 images of 383, with each cow represented with an average of 9 images. Each snapshot created two images; 1) RGB and ii) Temperature. The image filenames are in the format [cow_id-4 digits]_[day no-1 digit]_[counter-1 digit]. The timestamp.xlsx file indicates the day number (day 1 to day 9) of when an image in the data set was collected. This allows to design and run leave-one day-out cross validation, the same as we did in our paper. Here is the link to the scripts that reproduce the results reported in the paper, and the following is the link to the GitHub repository that contains all the script