Otimização de Modelos de Regressão para Predição de Peso em Pequenos Ruminantes Utilizando Medidas Corporais
Resumo
A ovinocaprinocultura no Brasil está em expansão, mas enfrenta desafios para equiparar-se à produtividade da bovinocultura. Em pequenas fazendas, a ausência de balanças e os altos custos associados à sua implementação dificultam o controle zootécnico preciso. Este estudo buscou superar esses desafios ao estimar o peso corporal de ovinos e caprinos usando técnicas de regressão otimizadas com Grid Search. Com medidas morfológicas como entradas, o modelo SVR apresentou resultados robustos: um MAE de 3.83, RMSE de 5.43, e R² de 85.0%, competindo diretamente com os principais trabalhos da literatura. Esses resultados promissores contribuem para um manejo mais eficiente e sustentável dos rebanhos, potencializando o setor no país.Referências
Abbas, A., Ullah, M. A., and Waheed, A. (2021). Body weight prediction using different data mining algorithms in thalli sheep: A comparative study. Veterinary World, 14(9):2332.
Bentéjac, C., Csörgő, A., and Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54:1937–1967.
Burman, P. (1989). A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3):503–514.
Candia, J. and Tsang, J. S. (2019). enetxplorer: an r package for the quantitative exploration of elastic net families for generalized linear models. BMC bioinformatics, 20:1–11.
Carneiro, T. C., Rocha, P. A., Carvalho, P. C., and Fernández-Ramírez, L. M. (2022). Ridge regression ensemble of machine learning models applied to solar and wind forecasting in brazil and spain. Applied Energy, 314:118936.
Chicco, D., Warrens, M. J., and Jurman, G. (2021). The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation. Peerj computer science, 7:e623.
Costa, J. A. A., Reis, F. A., and de Lucena, C. C. (2018). Boletim do centro de inteligência e mercado de caprinos e ovinos [recurso eletrônico]. Dados eletrônicos.
FAO. (2020). World cattle inventory: Ranking of countries. FAO.
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer.
He, C., Qiao, Y., Mao, R., Li, M., and Wang, M. (2023). Enhanced litehrnet based sheep weight estimation using rgb-d images. Computers and Electronics in Agriculture, 206:107667.
Ibrahim, A., Artama, W. T., Budisatria, I. G. S., Yuniawan, R., Atmoko, B. A., and Widayanti, R. (2021). Regression model analysis for prediction of body weight from body measurements in female batur sheep of banjarnegara district, indonesia. Biodiversitas Journal of Biological Diversity, 22(7).
Iqbal, M. (2013). Prediction of body weight through body measurements in beetal goats. Pakistan Journal of Science, 65(4).
James, G., Witten, D., Hastie, T., Tibshirani, R., et al. (2013). An introduction to statistical learning, volume 112. Springer.
Kramer, O. and Kramer, O. (2013). K-nearest neighbors. Dimensionality reduction with unsupervised nearest neighbors, pages 13–23.
Liashchynskyi, P. and Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: a big comparison for nas. arXiv preprint arXiv:1912.06059.
Martins, B., Mendes, A., Silva, L., Moreira, T., Costa, J., Rotta, P., Chizzotti, M., and Marcondes, M. (2020). Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livestock science, 236:104054.
Menesatti, P., Costa, C., Antonucci, F., Steri, R., Pallottino, F., and Catillo, G. (2014). A low-cost stereovision system to estimate size and weight of live sheep. Computers and Electronics in Agriculture, 103:33–38.
Muthukrishnan, R. and Rohini, R. (2016). Lasso: A feature selection technique in predictive modeling for machine learning. In 2016 IEEE international conference on advances in computer applications (ICACA), pages 18–20. Ieee.
NÓBREGA, A. and VERGNE, M. (2018). Novo censo agropecuário mostra crescimento de efetivo de caprinos e ovinos no nordeste. Empresa Brasileira de Pesquisa Agropecuária: Embrapa Caprinos e Ovinos.
Oliveira, M. (2019). Produção da pecuária municipal 2018. Catalog of the Instituto Brasileiro de Geografia e Estatística, 84(01014234):1–8.
Sabbioni, A., Beretti, V., Superchi, P., and Ablondi, M. (2020). Body weight estimation from body measures in cornigliese sheep breed. Italian Journal of Animal Science, 19(1):25–30.
Samperio, E., Lidón, I., Rebollar, R., Castejón-Limas, M., and Álvarez-Aparicio, C. (2021). Lambs’ live weight estimation using 3d images. Animal, 15(5):100212.
Sant’Ana, D. A., Pache, M. C. B., Martins, J., Soares, W. P., de Melo, S. L. N., Garcia, V., de Moares Weber, V. A., da Silva Heimbach, N., Mateus, R. G., and Pistori, H. (2021). Weighing live sheep using computer vision techniques and regression machine learning. Machine Learning with Applications, 5:100076.
Song, X., Bokkers, E., Van der Tol, P., Koerkamp, P. G., and Van Mourik, S. (2018). Automated body weight prediction of dairy cows using 3-dimensional vision. Journal of dairy science, 101(5):4448–4459.
Taud, H. and Mas, J.-F. (2018). Multilayer perceptron (mlp). Geomatic approaches for modeling land change scenarios, pages 451–455.
Bentéjac, C., Csörgő, A., and Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54:1937–1967.
Burman, P. (1989). A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3):503–514.
Candia, J. and Tsang, J. S. (2019). enetxplorer: an r package for the quantitative exploration of elastic net families for generalized linear models. BMC bioinformatics, 20:1–11.
Carneiro, T. C., Rocha, P. A., Carvalho, P. C., and Fernández-Ramírez, L. M. (2022). Ridge regression ensemble of machine learning models applied to solar and wind forecasting in brazil and spain. Applied Energy, 314:118936.
Chicco, D., Warrens, M. J., and Jurman, G. (2021). The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation. Peerj computer science, 7:e623.
Costa, J. A. A., Reis, F. A., and de Lucena, C. C. (2018). Boletim do centro de inteligência e mercado de caprinos e ovinos [recurso eletrônico]. Dados eletrônicos.
FAO. (2020). World cattle inventory: Ranking of countries. FAO.
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer.
He, C., Qiao, Y., Mao, R., Li, M., and Wang, M. (2023). Enhanced litehrnet based sheep weight estimation using rgb-d images. Computers and Electronics in Agriculture, 206:107667.
Ibrahim, A., Artama, W. T., Budisatria, I. G. S., Yuniawan, R., Atmoko, B. A., and Widayanti, R. (2021). Regression model analysis for prediction of body weight from body measurements in female batur sheep of banjarnegara district, indonesia. Biodiversitas Journal of Biological Diversity, 22(7).
Iqbal, M. (2013). Prediction of body weight through body measurements in beetal goats. Pakistan Journal of Science, 65(4).
James, G., Witten, D., Hastie, T., Tibshirani, R., et al. (2013). An introduction to statistical learning, volume 112. Springer.
Kramer, O. and Kramer, O. (2013). K-nearest neighbors. Dimensionality reduction with unsupervised nearest neighbors, pages 13–23.
Liashchynskyi, P. and Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: a big comparison for nas. arXiv preprint arXiv:1912.06059.
Martins, B., Mendes, A., Silva, L., Moreira, T., Costa, J., Rotta, P., Chizzotti, M., and Marcondes, M. (2020). Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livestock science, 236:104054.
Menesatti, P., Costa, C., Antonucci, F., Steri, R., Pallottino, F., and Catillo, G. (2014). A low-cost stereovision system to estimate size and weight of live sheep. Computers and Electronics in Agriculture, 103:33–38.
Muthukrishnan, R. and Rohini, R. (2016). Lasso: A feature selection technique in predictive modeling for machine learning. In 2016 IEEE international conference on advances in computer applications (ICACA), pages 18–20. Ieee.
NÓBREGA, A. and VERGNE, M. (2018). Novo censo agropecuário mostra crescimento de efetivo de caprinos e ovinos no nordeste. Empresa Brasileira de Pesquisa Agropecuária: Embrapa Caprinos e Ovinos.
Oliveira, M. (2019). Produção da pecuária municipal 2018. Catalog of the Instituto Brasileiro de Geografia e Estatística, 84(01014234):1–8.
Sabbioni, A., Beretti, V., Superchi, P., and Ablondi, M. (2020). Body weight estimation from body measures in cornigliese sheep breed. Italian Journal of Animal Science, 19(1):25–30.
Samperio, E., Lidón, I., Rebollar, R., Castejón-Limas, M., and Álvarez-Aparicio, C. (2021). Lambs’ live weight estimation using 3d images. Animal, 15(5):100212.
Sant’Ana, D. A., Pache, M. C. B., Martins, J., Soares, W. P., de Melo, S. L. N., Garcia, V., de Moares Weber, V. A., da Silva Heimbach, N., Mateus, R. G., and Pistori, H. (2021). Weighing live sheep using computer vision techniques and regression machine learning. Machine Learning with Applications, 5:100076.
Song, X., Bokkers, E., Van der Tol, P., Koerkamp, P. G., and Van Mourik, S. (2018). Automated body weight prediction of dairy cows using 3-dimensional vision. Journal of dairy science, 101(5):4448–4459.
Taud, H. and Mas, J.-F. (2018). Multilayer perceptron (mlp). Geomatic approaches for modeling land change scenarios, pages 451–455.
Publicado
11/09/2024
Como Citar
ARAÚJO, Rafael Luz; SILVA, Lilian Rosalina Gomes; SARMENTO, José Lindenberg Rocha; VELOSO E SILVA, Romuere Rodrigues.
Otimização de Modelos de Regressão para Predição de Peso em Pequenos Ruminantes Utilizando Medidas Corporais. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 12. , 2024, Parnaíba/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 219-228.
DOI: https://doi.org/10.5753/ercemapi.2024.243763.
