TY - GEN
T1 - A GP approach for precision farming
AU - Abbona, Francesca
AU - Vanneschi, Leonardo
AU - Bona, Marco
AU - Giacobini, Mario
N1 - info:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FCCI-CIF%2F29877%2F2017/PT#
info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT#
info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT#
info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT#
Abbona, F., Vanneschi, L., Bona, M., & Giacobini, M. (2020). A GP approach for precision farming. In 2020 IEEE Congress on Evolutionary Computation, CEC : 2020 Conference Proceedings (pp. 1-8). [9185637] (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185637
PY - 2020/7
Y1 - 2020/7
N2 - Livestock is increasingly treated not just as food containers, but as animals that can be susceptible to stress and diseases, affecting, therefore, the production of offspring and the performance of the farm. The breeder needs a simple and useful tool to make the best decisions for his farm, as well as being able to objectively check whether the choices and investments made have improved or worsened its performance. The amount of data is huge but often dispersive: it is therefore essential to provide the farmer with a clear and comprehensible solution, that represents an additional investment. This research proposes a genetic programming approach to predict the yearly number of weaned calves per cow of a farm, namely the measure of its performance. To investigate the efficiency of genetic programming in such a problem, a dataset composed by observations on representative Piedmontese breedings was used. The results show that the algorithm is appropriate, and can perform an implicit feature selection, highlighting important variables and leading to simple and interpretable models.
AB - Livestock is increasingly treated not just as food containers, but as animals that can be susceptible to stress and diseases, affecting, therefore, the production of offspring and the performance of the farm. The breeder needs a simple and useful tool to make the best decisions for his farm, as well as being able to objectively check whether the choices and investments made have improved or worsened its performance. The amount of data is huge but often dispersive: it is therefore essential to provide the farmer with a clear and comprehensible solution, that represents an additional investment. This research proposes a genetic programming approach to predict the yearly number of weaned calves per cow of a farm, namely the measure of its performance. To investigate the efficiency of genetic programming in such a problem, a dataset composed by observations on representative Piedmontese breedings was used. The results show that the algorithm is appropriate, and can perform an implicit feature selection, highlighting important variables and leading to simple and interpretable models.
KW - Cattle Breeding
KW - Genetic Programming
KW - Piedmontese Bovines
KW - Precision Livestock Farming
UR - http://www.scopus.com/inward/record.url?scp=85089242350&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000703998201027
U2 - 10.1109/CEC48606.2020.9185637
DO - 10.1109/CEC48606.2020.9185637
M3 - Conference contribution
AN - SCOPUS:85089242350
T3 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
SP - 1
EP - 8
BT - 2020 IEEE Congress on Evolutionary Computation, CEC
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020
Y2 - 19 July 2020 through 24 July 2020
ER -