ArticleFrontiers in artificial intelligence2024
Intelligent weight prediction of cows based on semantic segmentation and back propagation neural network.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Beef Cattle Body Weight Estimation Based on Dual-View RGB Images.Animals : an open access journal from MDPI · 2026Article
- Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture.Sensors (Basel, Switzerland) · 2026Review
- Recent Advances in Vision-Based Beef Cattle Body Measurement Technologies.Animals : an open access journal from MDPI · 2026Review
- Light Attention Encoder-Decoder for Cattle Body Segmentation and Body Weight Estimation.Animals : an open access journal from MDPI · 2026Article
- Early prediction of final body weight in Hanwoo steers using machine and deep learning models.Animal bioscience · 2026Article
- Multi-Scale Feature Fusion and Attention-Enhanced R2U-Net for Dynamic Weight Monitoring of Chicken Carcasses.Animals : an open access journal from MDPI · 2026Article
- SALC-Net: A lightweight contour-preserving segmentation network for yak body segmentation in complex grazing environments.PloS one · 2026Article
- Article
- Evaluation of hybrid models based on image segmentation and inference for pig weight estimation.Frontiers in artificial intelligence · 2026Article
- Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism.Animals : an open access journal from MDPI · 2025Article
- Article
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4 authors.
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Abstract
Accurate prediction of cattle weight is essential for enhancing the efficiency and sustainability of livestock management practices. However, conventional methods often involve labor-intensive procedures and lack instant and non-invasive solutions. This study proposed an intelligent weight prediction approach for cows based on semantic segmentation and Back Propagation (BP) neural network. The proposed semantic segmentation method leveraged a hybrid model which combined ResNet-101-D with the Squeeze-and-Excitation (SE) attention mechanism to obtain precise morphological features from cow images. The body size parameters and physical measurements were then used for training the regression-based machine learning models to estimate the weight of individual cattle. The comparative analysis methods revealed that the BP neural network achieved the best results with an MAE of 13.11 pounds and an RMSE of 22.73 pounds. By eliminating the need for physical contact, this approach not only improves animal welfare but also mitigates potential risks. The work addresses the specific needs of welfare farming and aims to promote animal welfare and advance the field of precision agriculture.
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