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基于激光雷达传感器的喷雾机高度检测方法及其实验验证。

A LiDAR Sensor-Based Spray Boom Height Detection Method and the Corresponding Experimental Validation.

机构信息

Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.

National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.

出版信息

Sensors (Basel). 2021 Mar 17;21(6):2107. doi: 10.3390/s21062107.

DOI:10.3390/s21062107
PMID:33802785
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8002541/
Abstract

Sprayer boom height () variations affect the deposition and distribution of droplets. An control system is used to adjust to maintain an optimum distance between the boom and the crop canopy, and an detection sensor is a key component of the control system. This study presents a new, low-cost light detection and ranging (LiDAR) sensor for detection developed based on the principle of single-point ranging. To examine the detection performance of the LiDAR sensor, a step height detection experiment, a field ground detection experiment, and a wheat stubble (WS) height detection experiment as well as a comparison with an ultrasonic sensor were performed. The results showed that the LiDAR sensor could be used to detect . When used to detect the WS height (), the LiDAR sensor primarily detected the WS roots and the inside of the WS canopy. and movement speed of the LiDAR sensor () has a greater impact on the detection performance of the LiDAR sensor for the WS canopy than that for the WS roots. The detection error of the LiDAR sensor for the WS roots is less than 5.00%, and the detection error of the LiDAR sensor for the WS canopy is greater than 8.00%. The detection value from the LiDAR sensor to the WS root multiplied by 1.05 can be used as a reference basis for adjusting , and after the WS canopy height is added to the basis, the value can be used as an index for adjusting in WS field spraying. The results of this study will promote research on the boom height detection method and autonomous control system.

摘要

喷头高度(boom height)变化会影响雾滴的沉积和分布。采用(control system)控制系统来调整(to adjust),以保持喷头和作物冠层之间的最佳距离,(detection sensor)检测传感器是控制系统的关键组成部分。本研究提出了一种新的、低成本的基于单点测距原理的(light detection and ranging,LiDAR)检测传感器。为了检验 LiDAR 传感器的检测性能,进行了阶跃高度检测实验、田间地面检测实验、小麦茬(wheat stubble,WS)高度检测实验以及与超声波传感器的对比实验。结果表明,LiDAR 传感器可用于检测。当用于检测 WS 高度(WS height)时,LiDAR 传感器主要检测 WS 根部和 WS 冠层内部。LiDAR 传感器的(LiDAR sensor)位置和运动速度(movement speed)对 LiDAR 传感器检测 WS 冠层的性能比对 WS 根部的影响更大。LiDAR 传感器检测 WS 根部的误差小于 5.00%,检测 WS 冠层的误差大于 8.00%。LiDAR 传感器检测到 WS 根部的数值乘以 1.05 可以作为调整(to adjust)的参考依据,在此基础上再加上 WS 冠层的高度,该数值可以作为 WS 田间喷雾调整(adjust)的指标。本研究的结果将促进对喷头高度检测方法和自主(autonomous)控制系统的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/1106b8182950/sensors-21-02107-g013a.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/3b30cce79675/sensors-21-02107-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/07a808df513a/sensors-21-02107-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/55034fb52611/sensors-21-02107-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/dd923b7869b2/sensors-21-02107-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/428422584334/sensors-21-02107-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/1106b8182950/sensors-21-02107-g013a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/8af99402edfd/sensors-21-02107-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/dcfe15197ea9/sensors-21-02107-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/049374cc998f/sensors-21-02107-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/30572ef10e12/sensors-21-02107-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/01fbe377893d/sensors-21-02107-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/e85be24db0d4/sensors-21-02107-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/3b30cce79675/sensors-21-02107-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/07a808df513a/sensors-21-02107-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/55034fb52611/sensors-21-02107-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/dd923b7869b2/sensors-21-02107-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/428422584334/sensors-21-02107-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/f89075c972a1/sensors-21-02107-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b5a/8002541/1106b8182950/sensors-21-02107-g013a.jpg

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本文引用的文献

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Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine.基于多特征融合和支持向量机的田间杂草和玉米幼苗检测。
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Spray drift as influenced by meteorological and technical factors.喷雾漂移受气象和技术因素的影响。
Pest Manag Sci. 2011 May;67(5):586-98. doi: 10.1002/ps.2114. Epub 2011 Feb 28.