数字农科院2.0

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

文献类型： 外文期刊

作者： Beibei Xu;Yifan Mao;Wensheng Wang;Guipeng Chen

作者机构：

关键词： computer vision;machine learning;precision farming;semantic segmentation;weight prediction

期刊名称： Frontiers in Artificial Intelligence

ISSN： 2624-8212

年卷期： 2024 年 7 卷

页码：

收录情况： 无来源刊（2024版）

摘要： 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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