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基于超声测距和迭代自组织数据分析技术算法的作物冠层定位方法。

A Crop Canopy Localization Method Based on Ultrasonic Ranging and Iterative Self-Organizing Data Analysis Technique Algorithm.

机构信息

College of Engineering, Shenyang Agricultural University, Shenyang 110866, China.

School of Mining Engineering, Liaoning Shihua University, Fushun 113001, China.

出版信息

Sensors (Basel). 2020 Feb 3;20(3):818. doi: 10.3390/s20030818.

DOI:10.3390/s20030818
PMID:32028735
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7038768/
Abstract

To protect crops from diseases and increase yields, chemical agents are applied by boom sprayers. To achieve the optimal effect, the boom and the crop canopy should be kept at an appropriate distance. So, it is crucial to be able to distinguish the crop canopy from other plant leaves. Based on ultrasonic ranging, this paper adopts the fuzzy iterative self-organizing data analysis technique algorithm to identify the canopy location. According to the structural characteristics of the crop canopy, based on fuzzy clustering, the algorithm can dynamically adjust the number and center of clusters so as to get the optimal results. Therefore, the distances from the sensor to the canopy or the ground can be accurately acquired, and the influence of lower leaves on the measurement results can be alleviated. Potted corn plants from the 3-leaf stage to the 6-leaf stage were tested on an experiment bench. The results showed that the calculated distances from the sensor to the canopy using this method had good correlation with the manually measured distances. The maximum error of calculated values appeared at the 3-leaf stage. With the growth of plants, the error of calculated values decreased. The increased sensor moving speeds led to increased error due to the reduced data points. From the 3-leaf stage to the 5-leaf stage, the distances from the sensor to the ground can also be obtained at the same time. The method proposed in this paper provides a practical resolution to localize the canopy for adjusting the height of sprayer boom.

摘要

为了保护作物免受病虫害的侵害并提高产量,通常使用喷杆喷雾器施药。为了达到最佳效果,喷杆和作物冠层应保持适当的距离。因此,能够将作物冠层与其他植物叶片区分开来至关重要。基于超声测距,本文采用模糊迭代自组织数据分析技术算法来识别冠层位置。根据作物冠层的结构特点,该算法基于模糊聚类,可以动态调整聚类的数量和中心,从而获得最佳结果。因此,可以准确获取传感器到冠层或地面的距离,并减轻下部叶片对测量结果的影响。在实验台上对三叶期至六叶期的盆栽玉米植株进行了测试。结果表明,该方法计算出的传感器到冠层的距离与手动测量的距离具有很好的相关性。最大误差出现在三叶期。随着植物的生长,计算值的误差减小。由于数据点减少,增加传感器的移动速度会导致误差增大。从三叶期到五叶期,同时也可以获得传感器到地面的距离。本文提出的方法为调整喷雾器喷杆高度的冠层定位提供了一种实用的解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/5245f3aa37e6/sensors-20-00818-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/25e50800893d/sensors-20-00818-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/64a6088af644/sensors-20-00818-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/30fd0b9ea28a/sensors-20-00818-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/5d4a70cd7730/sensors-20-00818-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/5245f3aa37e6/sensors-20-00818-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/25e50800893d/sensors-20-00818-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/64a6088af644/sensors-20-00818-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/30fd0b9ea28a/sensors-20-00818-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/5d4a70cd7730/sensors-20-00818-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9adc/7038768/5245f3aa37e6/sensors-20-00818-g005.jpg

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引用本文的文献

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2
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本文引用的文献

1
An ultrasonic system for weed detection in cereal crops.一种用于检测谷物作物中杂草的超声波系统。
Sensors (Basel). 2012 Dec 13;12(12):17343-57. doi: 10.3390/s121217343.