Publications

@article{riego_del_castillo_estimation_2022,

title = {Estimation of lamb weight using transfer learning and regression},

author = {Virginia Riego del Castillo and Lidia Sánchez-González and Laura Fernández-Robles and Manuel Castejón-Limas and Rubén Rebollar},

url = {https://link.springer.com/chapter/10.1007/978-3-031-18050-7_3},

year  = {2022},

date = {2022-01-01},

journal = {International Workshop on Soft Computing Models in Industrial and Environmental Applications},

pages = {23–30},

abstract = {This paper proposes an automatic, non-invasive, and cost-effective method to estimate the weight of live lambs using a camera, such as those found in mobile phones. The approach employs a pre-trained Convolutional Neural Network (Xception) with transfer learning to estimate lamb weight based on an image, the lamb's sex, and the height from which the image is taken. The method achieved a mean absolute error (MAE) of 0.58 kg and an R² value of 0.96, offering improved accuracy over traditional methods, and providing a practical solution to estimate lamb weight with minimal input and error.},

note = {Publisher: Springer Nature Switzerland Cham},

keywords = {convolutional neural network, Lamb Weight Estimation, Non-invasive Measurement, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}