Detecting the presence and location of cows in aerial images

This study aims to use object detection techniques for the purpose of identifying and detecting the location of cows on a field. The motivation for this study is to identify any cows that exhibit abnormal behavior such as self-isolation from the herd or lack of movement, which may indicate sickness or injury. Early detection of such behavior is crucial for swift intervention mitigating the risk of further injury or fatality. The proposed solution to this problem is to use an automated drone that periodically flies over the field taking pictures of the cattle. These pictures can then be analyzed to determine the precise location of all cows within the image. To identify all cows within aerial images, two methods that work together are developed and used. The first method focuses on color image processing and aims to identify all cows within the image by locating the highest concentration of cow related pixels within a given area. The second method uses the bag of features machine learning model and categorizes image pixels into ‘cow’ and ‘grass’ subcategories. These methods complement each other as the first provides a general center location of cows in an image, aiding in training the machine learning model with correctly identified cow images. The bag of features model refines this by not only identifying cows but also approximating their general shape and size. Together, these techniques demonstrate promising results in efficient and accurate cow detection in aerial field images.