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Fruit tree canopy segmentation from UAV orthophoto maps based on a lightweight improved U-Net

journal contribution
posted on 2024-01-04, 15:43 authored by Zhikai Li, Xiaoling Deng, Yubin Lan, Cunjia LiuCunjia Liu, Jiajun Qing

Segmenting fruit tree canopies from drone remote sensing images is a prerequisite for achieving accurate agricultural monitoring and precision aerial spraying at the individual tree (instance) level. However, current instance segmentation algorithms for canopy segmentation require repeated sampling of digital orthophoto maps (DOMs), resulting in high computational complexity and poor performance in densely planted orchards. Therefore, we propose a canopy labeling method suitable for U-Net and a lightweight segmentation network, where a lightweight backbone network, an attention mechanism, and a focal loss function are introduced to improve the U-Net decoder, greatly reducing the computational complexity required for large-scale canopy segmentation. The feasibility and effectiveness of the proposed method were validated using datasets from two seasons of two different lychee orchards. The average recognition rate of the improved U-Net was 90.98 %, 28.59 % higher than the basic model U-Net, and the floating-point operations (FLOPs) amounted to 50.86 GFLOPS, 27.67 % lower than the basic model U-Net. Under the same experimental conditions, the proposed model outperformed mainstream semantic segmentation models such as Deeplabv3 + and ResNet50-U-Net, and it was more efficient than previous instance segmentation methods based on YOLACT, as it does not require repeated sampling of the same region. For the same area, the number of sampled small images decreased from 194 to 78, resulting in a 148 % overall efficiency improvement while achieving better segmentation results. Thus, the proposed model can be used to extract and locate the crown of a lychee orchard, aiding in accurate management of a lychee orchard and in a differentiated agricultural analysis and decision-making for individual lychee trees.

Funding

Cooperative data analysing for citrus greening monitoring based on UAV remote sensing

Key-Area Research and Development Program of Guangdong Province, grant number 2023B0202090001

National Natural Science Foundation of China, grant number 32371984

Key-Area Research and Development Program of Guangdong Province, grant number 2019B020214003

Royal Society International Exchanges programme under the grant No. IEC\NSFC\191320

Laboratory of Lingnan Modern Agriculture Project, grant number NT2021009

Key-Area Research and Development Program of Guangzhou, grant number 202103000090

Key-Areas of Artificial Intelligence in General Colleges and Universities of Guangdong Province, grant number 2019KZDZX1012

The leading talents of Guangdong province program, grant number 2016LJ06G689

China Agriculture Research System, grant number CARS-15–23

The 111 Project, grant number D18019

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Computers and Electronics in Agriculture

Volume

217

Publisher

Elsevier BV

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier B.V.

Publisher statement

This paper was accepted for publication in the journal Computers and Electronics in Agriculture and the definitive published version is available at https://doi.org/10.1016/j.compag.2023.108538

Acceptance date

2023-12-10

Publication date

2023-12-22

Copyright date

2023

ISSN

0168-1699

eISSN

1872-7107

Language

  • en

Depositor

Prof Cunjia Liu. Deposit date: 29 December 2023

Article number

108538

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