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Operation Status Recognition Method and Experiment Based on Multidimensional Features of Agricultural Machinery Spatial Track
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Baozhong LI1, 2, Weipeng WANG2, Guomin ZHOU3, 4, Pei XU2, Ji SHEN2
Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12) : 150 - 157
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Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12): 150-157
• Special Column of Key Technologies for Intelligent Agricultural Power Equipment •
Operation Status Recognition Method and Experiment Based on Multidimensional Features of Agricultural Machinery Spatial Track
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Baozhong LI1, 2, Weipeng WANG2, Guomin ZHOU3, 4, Pei XU2, Ji SHEN2
Affiliations
  • 1.Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • 2.Luoyang Smart Agricultural Equipment Institute Co., Ltd., Luoyang 471000, China
  • 3.National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
  • 4.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Published: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.013
Outline
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The operation state of agricultural machinery is a key indicator for assessing the efficiency of agricultural mechanization and precise management. To realize the precise monitoring of the operation state of farm machinery by the data management platform, an operation state identification method was proposed based on the multidimensional features of the spatial track of farm machinery. Firstly, based on the big data management platform supported by the modern information technology of Internet of Things, the potential characteristics of the trajectory points in the operating space of agricultural machinery were studied, and the distribution laws of the characteristics such as speed, acceleration, steering rate and distribution density of trajectory points were analyzed. Secondly, based on the distribution characteristics of each feature and the demand for operation state recognition, a multi-strategy split-box processing method was used to quantitatively divide the features, and weight of evidence (WOE) and information value (IV) methods were introduced to quantify the influence weight of different features on the operation state of the farm machinery, so as to assess the impact on operation state the key features of the recognition ability were evaluated. Finally, based on the multidimensional key features of the spatial trajectory points of the farm machinery, the fusion algorithm of BP neural network and AdaBoost was combined to recognize the operation state of the farm machinery. The experimental results showed that the accuracy of the proposed algorithm model in the prediction of the operation state of agricultural machinery was as high as 97.3%, indicating that the recognition method based on the multidimensional features of agricultural machinery can accurately recognize the operation state of agricultural machinery.

agricultural machinery  /  spatial track  /  multi-strategy binning  /  operation status recognition  /  BP-AdaBoost
李保忠, 王伟鹏, 周国民, 徐培, 申及. 基于农机运行轨迹点多维特征的作业状态辨识方法与试验. 农业机械学报, 2025 , 56 (12) : 150 -157 . DOI: 10.6041/j.issn.1000-1298.2025.12.013
Baozhong LI, Weipeng WANG, Guomin ZHOU, Pei XU, Ji SHEN. Operation Status Recognition Method and Experiment Based on Multidimensional Features of Agricultural Machinery Spatial Track[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 150 -157 . DOI: 10.6041/j.issn.1000-1298.2025.12.013
Year 2025 volume 56 Issue 12
201
90
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Article Info
doi: 10.6041/j.issn.1000-1298.2025.12.013
  • Receive Date:2025-04-11
  • Online Date:2026-09-30
  • Published:2025-12-25
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History
  • Received:2025-04-11
  • Revised:2025-07-01
Funding
Affiliations
    1.Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2.Luoyang Smart Agricultural Equipment Institute Co., Ltd., Luoyang 471000, China
    3.National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China
    4.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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