Real-Time Bud Detection Using Yolov4 for Automatic Apple Flower Bud Thinning

Rashmi Sahu, Long He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Robotic bud thinning is an alternative technique of crop load management that regulates fruit bud density in the tree canopy. Flower bud detection in the natural environment is a key step for developing this robotic system to automatically remove flower buds. This study proposed a bud detection model using the You Only Look Once (YOLO) v4 deep learning algorithm. The detection performance of the YOLOv4 model was compared with those of the YOLOv5 and YOLOv7 models. The results show that under the same conditions, YOLOv4 performed better than YOLOv5 and YOLOv7 for buds'detection. On dataset-1 (stereo image dataset) the mean average precision (mAPs) of bud detection with YOLOv4, YOLOv5, and YOLOv7 were 98.9%, 75.5%, and 72.9% for one class, and 95.3%, 80.9%, and 76% for three classes respectively. The mAP from YOLOv4, YOLOv5, and YOLOv7 on dataset-2 (mobile images) was 94.1%, 79%, and 73% for one class, and 98.37%, 76.5%, and 66.9% for three classes respectively. The YOLOv4 model showed great potential for the real-time rapid detection of the apple bud. This model could lay the foundation for the machine vision unit of the robotic apple flower bud thinning system.

Original languageEnglish (US)
Title of host publication2023 ASABE Annual International Meeting
PublisherAmerican Society of Agricultural and Biological Engineers
ISBN (Electronic)9781713885887
DOIs
StatePublished - 2023
Event2023 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2023 - Omaha, United States
Duration: Jul 9 2023Jul 12 2023

Publication series

Name2023 ASABE Annual International Meeting

Conference

Conference2023 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2023
Country/TerritoryUnited States
CityOmaha
Period7/9/237/12/23

All Science Journal Classification (ASJC) codes

  • Agronomy and Crop Science
  • Bioengineering

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