Sugarcane Stem Node Detection Based on Wavelet Analysis

The article discusses a wavelet-based approach for recognizing sugarcane stem nodes in order to improve pre-cut sugarcane planting technology, beginning with sugarcane form characteristics that permit automated sugarcane seed production. The location signal is collected by the acceleration and thin-...

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Autores principales:	Minbo Chen, Qing Xu, Qian Cheng, Zhanpeng Xiao, Yunyun Luo, Youzong Huang, Chunming Wen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Automation multi-sensor redundancy pre-cutting sugarcane stem section wavelet analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a4c96f17c3414218928325d164cbbd42
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id	oai:doaj.org-article:a4c96f17c3414218928325d164cbbd42
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spelling	oai:doaj.org-article:a4c96f17c3414218928325d164cbbd422021-11-18T00:05:44ZSugarcane Stem Node Detection Based on Wavelet Analysis2169-353610.1109/ACCESS.2021.3124555https://doaj.org/article/a4c96f17c3414218928325d164cbbd422021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9597499/https://doaj.org/toc/2169-3536The article discusses a wavelet-based approach for recognizing sugarcane stem nodes in order to improve pre-cut sugarcane planting technology, beginning with sugarcane form characteristics that permit automated sugarcane seed production. The location signal is collected by the acceleration and thin-film piezoelectric sensors and then decomposed into the tenth, eleventh, and twelfth layers using the Daubechies tight-branch wavelet. After capturing the signal, it is reconstructed and superimposed to capture the stem node region’s features using the default threshold technique. A multi-sensor fusion approach is developed based on a weighted average and a Kalman filter to confirm the experiment’s validity. The weighted average process produces an average value that is 0.3512 mm off from the experimentally observed data average. The discrepancy between the Kalman filter method’s anticipated average value and the empirically determined average error is 0.5778 mm. To facilitate the investigation, 175 sugarcane samples with intermediate length processing are used. The detecting position system is determined experimentally after extensive experimental research and diligent examination. On average, the standard deviation is 0.494 mm, while the maximum value is 9.99 mm. 99.63 percent of cane seed samples are detected, with an error rate of 0.37 percent and a response time of 0.25 seconds. The proposed technology is conceptually feasible and achievable, and it can provide a reference for the development of automated cane seed pre-cutting machinery to give its contribution to agricultural production.Minbo ChenQing XuQian ChengZhanpeng XiaoYunyun LuoYouzong HuangChunming WenIEEEarticleAutomationmulti-sensor redundancypre-cuttingsugarcane stem sectionwavelet analysisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147933-147946 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Automation multi-sensor redundancy pre-cutting sugarcane stem section wavelet analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Automation multi-sensor redundancy pre-cutting sugarcane stem section wavelet analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Minbo Chen Qing Xu Qian Cheng Zhanpeng Xiao Yunyun Luo Youzong Huang Chunming Wen Sugarcane Stem Node Detection Based on Wavelet Analysis
description	The article discusses a wavelet-based approach for recognizing sugarcane stem nodes in order to improve pre-cut sugarcane planting technology, beginning with sugarcane form characteristics that permit automated sugarcane seed production. The location signal is collected by the acceleration and thin-film piezoelectric sensors and then decomposed into the tenth, eleventh, and twelfth layers using the Daubechies tight-branch wavelet. After capturing the signal, it is reconstructed and superimposed to capture the stem node region’s features using the default threshold technique. A multi-sensor fusion approach is developed based on a weighted average and a Kalman filter to confirm the experiment’s validity. The weighted average process produces an average value that is 0.3512 mm off from the experimentally observed data average. The discrepancy between the Kalman filter method’s anticipated average value and the empirically determined average error is 0.5778 mm. To facilitate the investigation, 175 sugarcane samples with intermediate length processing are used. The detecting position system is determined experimentally after extensive experimental research and diligent examination. On average, the standard deviation is 0.494 mm, while the maximum value is 9.99 mm. 99.63 percent of cane seed samples are detected, with an error rate of 0.37 percent and a response time of 0.25 seconds. The proposed technology is conceptually feasible and achievable, and it can provide a reference for the development of automated cane seed pre-cutting machinery to give its contribution to agricultural production.
format	article
author	Minbo Chen Qing Xu Qian Cheng Zhanpeng Xiao Yunyun Luo Youzong Huang Chunming Wen
author_facet	Minbo Chen Qing Xu Qian Cheng Zhanpeng Xiao Yunyun Luo Youzong Huang Chunming Wen
author_sort	Minbo Chen
title	Sugarcane Stem Node Detection Based on Wavelet Analysis
title_short	Sugarcane Stem Node Detection Based on Wavelet Analysis
title_full	Sugarcane Stem Node Detection Based on Wavelet Analysis
title_fullStr	Sugarcane Stem Node Detection Based on Wavelet Analysis
title_full_unstemmed	Sugarcane Stem Node Detection Based on Wavelet Analysis
title_sort	sugarcane stem node detection based on wavelet analysis
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a4c96f17c3414218928325d164cbbd42
work_keys_str_mv	AT minbochen sugarcanestemnodedetectionbasedonwaveletanalysis AT qingxu sugarcanestemnodedetectionbasedonwaveletanalysis AT qiancheng sugarcanestemnodedetectionbasedonwaveletanalysis AT zhanpengxiao sugarcanestemnodedetectionbasedonwaveletanalysis AT yunyunluo sugarcanestemnodedetectionbasedonwaveletanalysis AT youzonghuang sugarcanestemnodedetectionbasedonwaveletanalysis AT chunmingwen sugarcanestemnodedetectionbasedonwaveletanalysis
_version_	1718425243331067904

Sugarcane Stem Node Detection Based on Wavelet Analysis

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