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- W4304208822 abstract "Meat production needs of accurate measurement of livestock weight. In lambs, traditional scales are still used to weigh live animals, which is a tedious process for the operators and stressful for the animal. In this paper, we propose a method to estimate the weight of live lambs automatically, fast, non-invasive and affordably. The system only requires a camera like those that can be found in mobile phones. Our approach is based on the use of a known Convolutional Neural Network architecture (Xception) pre-trained on the ImageNet dataset. The acquired knowledge during training is used to estimate the weight, which is known as transfer learning. The best results are achieved with a model that receives the image, the sex of the lamb and the height from where the image is taken. A mean absolute error (MAE) of 0.58 kg and an $$R^{2}$$ of 0.96 were obtained, improving on current techniques. Only one image and two values specified by the user (sex and height) allow to estimate with a minimum error the optimal weight of a lamb, maximising the economic profit." @default.
- W4304208822 created "2022-10-11" @default.
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- W4304208822 date "2022-10-12" @default.
- W4304208822 modified "2023-09-30" @default.
- W4304208822 title "Estimation of Lamb Weight Using Transfer Learning and Regression" @default.
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- W4304208822 doi "https://doi.org/10.1007/978-3-031-18050-7_3" @default.
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