TY - JOUR
T1 - Apple orchard production estimation using deep learning strategies
T2 - A comparison of tracking-by-detection algorithms
AU - Villacrés, Juan
AU - Viscaino, Michelle
AU - Delpiano, José
AU - Vougioukas, Stavros
AU - Auat Cheein, Fernando
N1 - Funding Information:
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, and ANID PFCHA/Doctorado Nacional/2020-21200684.
Funding Information:
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, and ANID PFCHA/Doctorado Nacional/2020-21200684.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2023/1
Y1 - 2023/1
N2 - The automated detection and counting of fruit in tree canopies is a key component of yield estimation systems, which are indispensable for the precision management of modern orchards. Detection and counting tasks in agricultural environments are not trivial because of challenges such as characteristics of the tree canopies, occlusion caused by leaves and the lighting conditions, among other factors. With the aim of identifying which algorithm is more suitable for yield estimation, we present a comprehensive comparison of tracking-by-detection algorithms, applied to apple counting. The tracking strategies evaluated were Kalman Filter, Kernelized Correlation Filter, Simple Online Real-Time Tracking, Multi Hypothesis Tracking, and Deep Simple Online Real-Time Tracking. The five tracking algorithms were further assessed on two novel databases constructed for this research in Multiple Object Tracking MOT format. After a sensitivity analysis of the trackers, the results show that the most robust approach is the Multiple Hypothesis Tracking, followed by the Deep Simple Online Realtime (DeepSORT), with a MOT accuracy of 97.00% and 93.00%, respectively, when having perfect detection. However, in an application case including a deep learning-based detection stage, the DeepSORT tracker obtains the lowest counting error, which on average for all videos is 20.07% and 31.52% when using YoloV5 and Faster R-CNN as detection strategies. Statistically similar results were obtained using the Kalman Filter with a counting error of 20.5% and 31.9% when detecting fruit with YoloV5 and Faster R-CNN.
AB - The automated detection and counting of fruit in tree canopies is a key component of yield estimation systems, which are indispensable for the precision management of modern orchards. Detection and counting tasks in agricultural environments are not trivial because of challenges such as characteristics of the tree canopies, occlusion caused by leaves and the lighting conditions, among other factors. With the aim of identifying which algorithm is more suitable for yield estimation, we present a comprehensive comparison of tracking-by-detection algorithms, applied to apple counting. The tracking strategies evaluated were Kalman Filter, Kernelized Correlation Filter, Simple Online Real-Time Tracking, Multi Hypothesis Tracking, and Deep Simple Online Real-Time Tracking. The five tracking algorithms were further assessed on two novel databases constructed for this research in Multiple Object Tracking MOT format. After a sensitivity analysis of the trackers, the results show that the most robust approach is the Multiple Hypothesis Tracking, followed by the Deep Simple Online Realtime (DeepSORT), with a MOT accuracy of 97.00% and 93.00%, respectively, when having perfect detection. However, in an application case including a deep learning-based detection stage, the DeepSORT tracker obtains the lowest counting error, which on average for all videos is 20.07% and 31.52% when using YoloV5 and Faster R-CNN as detection strategies. Statistically similar results were obtained using the Kalman Filter with a counting error of 20.5% and 31.9% when detecting fruit with YoloV5 and Faster R-CNN.
KW - Apple counting
KW - Faster R-CNN
KW - Multi-object tracking
KW - Tracking-by-detection
KW - YoloV5
UR - http://www.scopus.com/inward/record.url?scp=85144398919&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2022.107513
DO - 10.1016/j.compag.2022.107513
M3 - Article
AN - SCOPUS:85144398919
SN - 0168-1699
VL - 204
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 107513
ER -