Automatic heart girth measurement for cattle based on deep learning

Du, Ao; Guo, Hao; Lu, Jie; Su, Yang; Ruchay, Alexey; Pezzuolo, Andrea

doi:10.1109/metroagrifor52389.2021.9628696

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Moreover, the presented area can also be influenced by the distance between the camera equipment and the cows. Although the measurement of body size makes use of key areas or body parts like body width and height, heart girth, hip width, and height and thus achieves high accuracy, expensive equipment needs to be equipped at different aspects and angles accordingly, which is not applicable to housing farms (Qiao et al, 2019 ; Du et al, 2021 ; Zhang et al, 2021 ; Dang et al, 2022 ). Instead, the method integrating area and height takes advantage of three-dimensional size information of cows and becomes an accurate and reliable measurement, which is also consistent with the favorite indicators of experienced farmers for weight estimation artificially.…”

Section: Introductionmentioning

confidence: 99%

Intelligent weight prediction of cows based on semantic segmentation and back propagation neural network

Xu,

Mao,

Wang

et al. 2024

Front. Artif. Intell.

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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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