TY - JOUR
T1 - Detection and characterization of cherries
T2 - A deep learning usability case study in Chile
AU - Villacrés, Juan Fernando
AU - Cheein, Fernando Auat
N1 - Funding Information:
Funding: The authors would like to thank to the Advanced Center for Electrical and Electronic Engineering (AC3E), ANID Basal project FB0008 and FONDECYT grant 1201319. Authors would also like to thank to Universidad Técnica Federico Santa María, DGIIP, PIIC 25/2020, and ANID PFCHA/DoctoradoNacional/2020-21200684.
Publisher Copyright:
© 2020 by the authors.
PY - 2020/6
Y1 - 2020/6
N2 - Chile is one of the main exporters of sweet cherries in the world and one of the few in the southern hemisphere, being their harvesting between October and January. Hence, Chilean cherries have gained market in the last few years and positioned Chile in a strategic situation which motivates to undergo through a deep innovation process in the field. Currently, cherry crop estimates have an error of approximately 45%, which propagates to all stages of the production process. In order to mitigate such error, we develop, test and evaluate a deep neural-based approach, using a portable artificial vision system to enhance the cherries harvesting estimates. Our system was tested in a cherry grove, under real field conditions. It was able to detect cherries with up to 85% of accuracy and to estimate production with up to 25% of error. In addition, it was able to classify cherries into four sizes, for a better characterization of the production for exportation.
AB - Chile is one of the main exporters of sweet cherries in the world and one of the few in the southern hemisphere, being their harvesting between October and January. Hence, Chilean cherries have gained market in the last few years and positioned Chile in a strategic situation which motivates to undergo through a deep innovation process in the field. Currently, cherry crop estimates have an error of approximately 45%, which propagates to all stages of the production process. In order to mitigate such error, we develop, test and evaluate a deep neural-based approach, using a portable artificial vision system to enhance the cherries harvesting estimates. Our system was tested in a cherry grove, under real field conditions. It was able to detect cherries with up to 85% of accuracy and to estimate production with up to 25% of error. In addition, it was able to classify cherries into four sizes, for a better characterization of the production for exportation.
KW - Cherry detection
KW - Faster R-CNN
KW - Fruit characterization
UR - http://www.scopus.com/inward/record.url?scp=85086520795&partnerID=8YFLogxK
U2 - 10.3390/agronomy10060835
DO - 10.3390/agronomy10060835
M3 - Article
AN - SCOPUS:85086520795
SN - 2073-4395
VL - 10
JO - Agronomy
JF - Agronomy
IS - 6
M1 - 835
ER -