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Taoism-Net: A Fruit Tree Segmentation Model Based on Minimalism Design for UAV Camera

Authors :
Yanheng Mai
Jiaqi Zheng
Zefeng Luo
Chaoran Yu
Jianqiang Lu
Caili Yu
Zuanhui Lin
Zhongliang Liao
Source :
Agronomy, Vol 14, Iss 6, p 1155 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The development of precision agriculture requires unmanned aerial vehicles (UAVs) to collect diverse data, such as RGB images, 3D point clouds, and hyperspectral images. Recently, convolutional networks have made remarkable progress in downstream visual tasks, while often disregarding the trade-off between accuracy and speed in UAV-based segmentation tasks. The study aims to provide further valuable insights using an efficient model named Taoism-Net. The findings include the following: (1) Prescription maps in agricultural UAVs requires pixel-level precise segmentation, with many focusing solely on accuracy at the expense of real-time processing capabilities, being incapable of satisfying the expectations of practical tasks. (2) Taoism-Net is a refreshingly segmented model, overcoming the challenges of complexity in deep learning, based on minimalist design, which is used to generate prescription maps through pixel level classification mapping of geodetic coordinates (the lychee tree aerial dataset in Guangdong is used for experiments). (3) Compared with mainstream lightweight models or mature segmentation algorithms, Taoism-Net achieves significant improvements, including an improvement of at least 4.8% in mIoU, and manifested a superior performance in the accuracy–latency curve. (4) “The greatest truths are concise” is a saying widely spread by ancient Taoism, indicating that the most fundamental approach is reflected through the utmost minimalism; moreover, Taoism-Net expects to a build bridge between academic research and industrial deployment, for example, UAVs in precision agriculture.

Details

Language :
English
ISSN :
14061155 and 20734395
Volume :
14
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Agronomy
Publication Type :
Academic Journal
Accession number :
edsdoj.17e4c5dc84d64013adae107cfb712599
Document Type :
article
Full Text :
https://doi.org/10.3390/agronomy14061155