Interpretive Summary: Computer vision algorithms to help decision-making in cattle production
By: P Guarnido-Lopez, Y Pi, J Tao, E D M Mendes, L O Tedeschi
Implications:
- Computer vision represents a valuable tool for helping cattle producers make decisions.
- Deep-learning algorithms, especially neural networks such as convolutional neural networks, conduct image classification, segmentation, object detection, and feature extraction.
- Computer vision helps to estimate intake, body weight and gain, body condition score, health status, and reproductive performance of cattle.
- The main goal for the future is to set up computer vision on-farm to execute real-time algorithms.
In recent years, integrating computer vision technologies into precision livestock farming (PLF) management systems have the potential to transform how cattle producers collect, monitor, analyze, and optimize animal production. Livestock production, particularly in cattle farming, encompassing beef and dairy under intensive and extensive production systems, faces numerous challenges ranging from optimizing feeding practices to detecting and managing diseases. Traditional monitoring and assessment methods often rely on manual labor and subjective evaluations, leading to inefficiencies and inaccuracies. Computer vision (CV) algorithms, leveraging the power of artificial intelligence (AI), machine learning (ML), and deep learning (DL) offer unprecedented capabilities in automating tasks, extracting meaningful insights, and improving animal management in different production systems and assisting in the decision-making processes such as 1) improving economic impact through solving inaccurate feed inefficiencies (Tedeschi, 2019), 2) decreasing productivity losses due to early disease´s detection in dairy cows (Miles, 2009), and 3) improving labor efficiency by automating tasks and providing real-time insights. Although CV is applicable to all livestock animals, most of the current development of algorithms for usage are conducted for swine production, given the availability of resources and the relatively straightforward CV application for recording indoor-housed animals.
Read more in Animal Frontiers.