# Learning-based estimation of cattle weight gain and its influencing factors

arXiv: https://arxiv.org/abs/2502.06906 · HTML: https://mlsanity.com/paper/2502.06906/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 85 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [Machine Learning Techniques for Estimating Soil Moisture from Mobile Captured Images](https://mlsanity.com/paper/2303.11527/) (2023)

## Built on by (most cited first, 3 of 3)

- [Mob-based cattle weight gain forecasting using ML models](https://mlsanity.com/paper/2509.12615/) (2025)
- [Agreement-Driven Multi-View 3D Reconstruction for Live Cattle Weight Estimation](https://mlsanity.com/paper/2601.17791/) (2026)
- [Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems](https://mlsanity.com/paper/2608.06001/) (2026)

## Abstract (arXiv)

Many cattle farmers still depend on manual methods to measure the live weight gain of cattle at set intervals, which is time consuming, labour intensive, and stressful for both the animals and handlers. A remote and autonomous monitoring system using machine learning (ML) or deep learning (DL) can provide a more efficient and less invasive method and also predictive capabilities for future cattle weight gain (CWG). This system allows continuous monitoring and estimation of individual cattle live weight gain, growth rates and weight fluctuations considering various factors like environmental conditions, genetic predispositions, feed availability, movement patterns and behaviour. Several researchers have explored the efficiency of estimating CWG using ML and DL algorithms. However, estimating CWG suffers from a lack of consistency in its application. Moreover, ML or DL can provide weight gain estimations based on several features that vary in existing research. Additionally, previous studies have encountered various data related challenges when estimating CWG. This paper presents a comprehensive investigation in estimating CWG using advanced ML techniques based on research articles (between 2004 and 2024). This study investigates the current tools, methods, and features used in CWG estimation, as well as their strengths and weaknesses. The findings highlight the significance of using advanced ML approaches in CWG estimation and its critical influence on factors. Furthermore, this study identifies potential research gaps and provides research direction on CWG prediction, which serves as a reference for future research in this area.