Determination of the Live Weight of Farm Animals with Deep Learning and Semantic Segmentation Techniques

Abstract

In cattle breeding, regularly taking the animals to the scale and recording their weight is important for both the performance of the enterprise and the health of the animals. This process, which must be carried out in businesses, is a difficult task. For this reason, it is often not performed regularly or not performed at all. In this study, we attempted to estimate the weights of cattle by using stereo vision and semantic segmentation methods used in the field of computer vision together. Images of 85 animals were taken from different angles with a stereo setup consisting of two identical cameras. The distances of the animals to the camera plane were calculated by stereo distance calculation, and the areas covered by the animals in the images were determined by semantic segmentation methods. Then, using all these data, different artificial neural network models were trained. As a result of the study, it was revealed that when stereo vision and semantic segmentation methods are used together, live animal weights can be predicted successfully.

Analyzing Data Modalities for Cattle Weight Estimation Using Deep Learning Models

“…Class activation mapping supported the development of efficient network heads embracing visual explanation and applicability in practical natural livestock environments. Guvenog et al [ 27 ] estimated the weight of cattle by using stereo vision and semantic segmentation methods [ 28 ]. Images of animals were captured from various angles with a stereo setup.…”

Comparative Analysis of Statistical Regression Models for Prediction of Live Weight of Korean Cattle during Growth

“…Third, various studies have explored methods for predicting livestock weight using machine learning, deep learning, and image processing techniques. These studies include predictions of live weight and carcass characteristics through 3D imaging and machine learning algorithms [17], shape feature extraction from 3D images for weight prediction using linear regression [18], a review of computer vision and machine learning-based weight prediction methods with a discussion of their strengths and weaknesses [1], comparisons between traditional linear regression models and different machine learning algorithms for weight prediction in cattle [19], a novel live weight prediction model based on 3D cloud augmentation and deep learning image regression [4], a comparative examination of machine learning techniques for predicting live cattle weight [20], automatic Korean cattle weight prediction using the Bayesian ridge algorithm and body characteristic extraction from RGB-D images [21], weight prediction performance comparisons on 3D cow image data using various supervised learning techniques [22], and cow weight estimation using semantic segmentation and stereo vision in computer vision [23].…”

Filter Pruning with Convolutional Approximation Small Model Framework

“…The use of convolutional neural networks (CNNs) for the analysis of human issues is widespread throughout both the commercial and academic domains. They are particularly helpful in computer vision for problems such as image classification [1][2][3], object recognition [4][5][6], object detection [7,8], semantic segmentation [9][10][11], etc. CNNs have demonstrated remarkable effectiveness in recent years.…”