Apple orchard production estimation using deep learning strategies: A comparison of tracking-by-detection algorithms

Juan Villacrés, Michelle Viscaino, José Delpiano, Stavros Vougioukas, Fernando Auat Cheein*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Article number107513
JournalComputers and Electronics in Agriculture
Early online date20 Dec 2022
Publication statusPublished - Jan 2023


  • Apple counting
  • Faster R-CNN
  • Multi-object tracking
  • Tracking-by-detection
  • YoloV5

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture


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