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基于機器學習的小麥收獲機掉頭軌跡識別
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國家精準農業(yè)應用項目(JZNYYY001)


Identifying Turning Trajectories of Wheat Harvester Based on Machine Learning
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    摘要:

    識別小麥收獲機運行軌跡是分析農業(yè)機械活動,、提高作業(yè)效率的重要手段。本文針對小麥收獲機田內作業(yè)場景,,提出一種基于機器學習的收獲機掉頭軌跡識別算法,。首先通過兩步Kmeans聚類與三步修正識別出X形掉頭軌跡點、作業(yè)異常軌跡點與作業(yè)軌跡點,;為進一步從作業(yè)軌跡中分類出U形掉頭軌跡點,,構建了基于支持向量機模型(Support vector machine,SVM)的U形掉頭軌跡識別算法,,并對初步識別結果進行三步修正,;最終識別出小麥收獲機的田內X形掉頭、作業(yè)異常,、U形掉頭與作業(yè)軌跡點,,識別結果的F1值為94%,時間間隔為1~5s的數據的F1值在90%以上,,實現田內軌跡的細致劃分,。基于去除掉頭軌跡與異常軌跡后獲得的有效作業(yè)軌跡,,可通過距離算法計算獲得農田面積,,結果相比使用原始軌跡的計算誤差可降低12.76%。該研究可為基于海量農機軌跡的作業(yè)精細化管理提供參考,。

    Abstract:

    Identifying the trajectories of wheat harvester in the field is an important means to analyze the activities of agricultural machinery and improve the working efficiency. A machine learning based algorithm for recognizing the turning trajectories of wheat harvester was proposed. Identifing X-turn, abnormal working, and working trajectory through two-step K-means iterative clustering and three-step correction method: the first step (M1) was performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) was based on the direction change of the “turning” and “abnormal working” trajectories. The third correction step (M3) was based on the operating characteristics to specify the start and stop positions of the turning. In order to further classify U-turn trajectories from working trajectories, identifying X-turn, abnormal working, U-turn and working trajectories through SVM model and three-step correction method, firstly, the correction of U-turn boundary based on trajectory curvature (S1) was carried out. Secondly, based on the time difference between X-turn and U-turn, the misidentification as a U-turn was corrected (S2). Thirdly, the correction was based on the change of the angle before and after the U-turn (S3). The F1score of the four trajectories recognition results was 94%. The accuracy, recall, and F1 scores of data recognition results at different time intervals of 1~5s were all above 85%, indicating that the algorithm performed well on trajectory data at 1~5s intervals. When the time interval was extended to 10s and 15s,the U-turn trajectory would not be recognized, indicating that the algorithm cannot be applied to overly sparse trajectory data. The effective working trajectories were obtained after removing the X-turn trajectories, U-turn trajectories and abnormal working trajectories of the positioning track data in a field. The error of calculating the farmland area by the distance algorithm can be reduced by 12.76% compared with the calculation error of using the original data. The research result can provide a reference for fine management of farmland operations.

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楊麗麗,王新鑫,李元博,常孟帥,翟衛(wèi)欣,吳才聰.基于機器學習的小麥收獲機掉頭軌跡識別[J].農業(yè)機械學報,2023,54(9):27-34. YANG Lili, WANG Xinxin, LI Yuanbo, CHANG Mengshuai, ZHAI Weixin, WU Caicon. Identifying Turning Trajectories of Wheat Harvester Based on Machine Learning[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(9):27-34.

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  • 收稿日期:2023-03-20
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  • 在線發(fā)布日期: 2023-09-10
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