Version 1
: Received: 31 March 2022 / Approved: 1 April 2022 / Online: 1 April 2022 (12:34:02 CEST)
Version 2
: Received: 1 April 2022 / Approved: 4 April 2022 / Online: 4 April 2022 (13:35:43 CEST)
How to cite:
Xiong, Y.; Zeng, X.; Chen, Y.; Liao, J.; Lai, W.; Zhu, M. An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing. Preprints2022, 2022040007. https://doi.org/10.20944/preprints202204.0007.v2
Xiong, Y.; Zeng, X.; Chen, Y.; Liao, J.; Lai, W.; Zhu, M. An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing. Preprints 2022, 2022040007. https://doi.org/10.20944/preprints202204.0007.v2
Xiong, Y.; Zeng, X.; Chen, Y.; Liao, J.; Lai, W.; Zhu, M. An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing. Preprints2022, 2022040007. https://doi.org/10.20944/preprints202204.0007.v2
APA Style
Xiong, Y., Zeng, X., Chen, Y., Liao, J., Lai, W., & Zhu, M. (2022). An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing. Preprints. https://doi.org/10.20944/preprints202204.0007.v2
Chicago/Turabian Style
Xiong, Y., Weiqian Lai and Mingyong Zhu. 2022 "An Approach to Detecting and Mapping Individual Fruit Trees Integrated YOLOv5 with UAV Remote Sensing" Preprints. https://doi.org/10.20944/preprints202204.0007.v2
Abstract
The location and number data of individual fruit trees are critical for planting area investigation, fruit yield prediction, and smart orchard management and planning. These data are conventionally obtained through manual investigation and statistics with time-consuming and laborious effort. Object detection models in deep learning used widely in computer vision could provide an opportunity for accurate detection of individual fruit trees, which is essential for rapidly obtaining the data and reducing human operations errors. This study proposes an approach to detecting individual fruit trees and mapping their spatial distribution by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing collected high-resolution true-color images of fruit trees in the experimental pomelo tree orchards in Meizhou city, South China. An image dataset of deep learning samples of individual pomelo trees (IPTs) was constructed through visual interpretation and field investigation based on the fruit tree images captured by UAV remote sensing. Four different scales of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) for object detection were selected to train, validate, and test on the image dataset of pomelo trees. The results show that the average precision (AP@0.5) of the four YOLOv5 models for validation reach 87.8%, 88.5%, 89.1%, and 90.7%, respectively. The larger the model scale, the higher the average accuracy of the detection result of validation. It suggests that YOLOv5x is a preferred high-accuracy model among the YOLOv5 family and is suitable to realize the detection of IPTs. The number of the IPTs in the study area was counted using YOLOv5x, and their spatial distribution map was made using the non-maximum suppression method and ArcGIS software. This study will provide primary data and technical support for smart orchard management in Meizhou city and other fruit-producing areas.
Keywords
individual fruit tree (IFT); individual pomelo tree (IPT) detection; deep learning; transfer learning; YOLOv5; remote sensing; unmanned aerial vehicle (UAV); spatial distribution
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
4 April 2022
Commenter:
Yongzhu Xiong
Commenter's Conflict of Interests:
Author
Comment:
1. Corrected some known mistakes of grammar and expressions in section Abstract and the text. 2. Modified the Fig. 1b and added the Fig. 13. 3. Add Section 3.5. PomeloNet validation and test on images captured in the other two pomelo orchards. 4. Replace the abstract figure with a new one, Fig.2.
Commenter: Yongzhu Xiong
Commenter's Conflict of Interests: Author
2. Modified the Fig. 1b and added the Fig. 13.
3. Add Section 3.5. PomeloNet validation and test on images captured in the other two pomelo orchards.
4. Replace the abstract figure with a new one, Fig.2.