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Fruit tree canopy segmentation from UAV orthophoto maps based on a lightweight improved U-Net
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.
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
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Published inComputers and Electronics in Agriculture
- AM (Accepted Manuscript)
Rights holder© Elsevier B.V.
Publisher statementThis 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