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To solve the problem of orchard environmental perception, a 2D LiDAR sensor was used to scan fruit trees on both sides of a test platform to obtain their position. Firstly, the two-dimensional iterative closest point (2D-ICP) algorithm was used to obtain the complete point cloud data of fruit trees on both sides. Then, combining the lightning connection algorithm (LAPO) and the density-based clustering algorithm (DBSCAN), a fruit tree detection method based on density-based lightning connection clustering (LAPO-DBSCAN) was proposed. After obtaining the point cloud data of fruit trees on both sides of the test platform using the 2D-ICP algorithm, the LAPO-DBSCAN algorithm was used to obtain the position of fruit trees. The experimental results show that the positive detection rate was 96.69%, the false detection rate was 3.31%, and the average processing time was 1.14 s, verifying the reliability of the algorithm. Therefore, this algorithm can be used to accurately find the position of fruit trees, meaning that it can be applied to orchard navigation in a later stage.

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In the scene of paddy field rotary tillage, a real-time detection method of rotary tillage condition based on machine vision is proposed, and the quality of rotary tillage is evaluated by the index of residual stubble. The residual root stubble is selected as the research object, and the root stubble detection method based on the standard deviation of Y component in YCrCb space is proposed to determine the residual root stubble of soil after rotary tillage, which is divided into three levels: less root stubble, medium root stubble, and more root stubble. Finally, the accuracy of the algorithm is verified by field test and questionnaire survey. On the basis of manual evaluation, the accuracy rate of the working condition is 83.6 %, which provides a more accurate basis for the real-time adjustment of the control strategy for the unmanned operation of agricultural machinery in the field, and realizes the rotary tillage quality from qualitative evaluation to quantitative evaluation, and lays the foundation for the data of rotary tillage quality.