Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

Authors

  • Weixin Zhai 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Zhi Xu 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Jinming Liu 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Xiya Xiong 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
  • Jiawen Pan 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Sunok chung 3. Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
  • Dionysis Bochtis 4. Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology-Hellas (CERTH), Thessaloniki 57001, Greece
  • Caicong Wu 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

Keywords:

road-field trajectory classification, efficientNet, feature deformation network, multi-range feature enhancement, agricultural machinery operation mode recognition

Abstract

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory research. In the present study, to effectively identify agricultural machinery operation mode, a feature deformation network with multi-range feature enhancement was proposed. First, a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory data. Second, to further enrich the representation of trajectories, a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature maps. Then, EfficientNet-B0 was used to extract features of different scales and depths from the feature map, select features highly relevant to the results, and finally accurately predict the mode of each trajectory point. To validate the effectiveness of the proposed method, experiments were conducted to compare the results with those of other methods on a dataset of real agricultural trajectories. On the corn and wheat harvester trajectory datasets, the model achieved accuracies of 96.88% and 96.68%, as well as F1 scores of 93.54% and 94.19%, exhibiting improvements of 8.35% and 9.08% in accuracy and 20.99% and 20.04% in F1 score compared with the current state-of-the-art method. Key words: road-field trajectory classification; efficientNet; feature deformation network; multi-range feature enhancement; agricultural machinery operation mode recognition DOI: 10.25165/j.ijabe.20241704.8831 Citation: Zhai W X, Xu Z, Liu J M, Xiong X Y, Pan J W, Chung S, et al. Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification. Int J Agric & Biol Eng, 2024; 17(4): 265–275.

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Published

2024-09-06

How to Cite

Zhai, W., Xu, Z., Liu, J., Xiong, X., Pan, J., chung, S., … Wu, C. (2024). Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification. International Journal of Agricultural and Biological Engineering, 17(4), 265–275. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/8831

Issue

Vol. 17 No. 4 (2024): IJABE

Section

Information Technology, Sensors and Control Systems

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