{"title":"Noise reduction and analysis of leaf electrical signals of strap-leaved plants based on VMD-EWT","authors":"Jiaming Gu, Fangming Tian, Jingxiu Shi, Feng Tan","doi":"10.1016/j.compag.2024.109441","DOIUrl":null,"url":null,"abstract":"<div><div>Plant electrical signals are rapid responses of plants to external stimuli, and their characteristic changes are closely associated with plant life activities. However, due to their weak and low-frequency nature, the collected signals often suffer from significant noise interference. Therefore, investigating appropriate denoising methods is crucial for subsequent data analysis. In this study, a plant electrical signal synchronous acquisition system based on a 16-channel array electrode was employed to collect and store surface potentials of maize leaves under no stimulation, light stimulation, and electrical stimulation conditions. To address the issue of excessive noise in raw plant electrical signals, we propose a denoising method (VMD-EWT) that combines Variational Mode Decomposition (VMD) with Empirical Wavelet Transform (EWT). Based on the denoised multi-channel data obtained through these methods, we analyze the transmission characteristics and variation patterns of plant leaf electrical signals. The results demonstrate that traditional wavelet hard and soft thresholding-based denoising methods as well as VMD+EWT were utilized to remove noise from the plant surface potential data under electrical stimulation.The comprehensive evaluation indicators included the energy ratio and waveform analysis of the denoised signal in the time, frequency, and time–frequency domains. Based on a comprehensive assessment, it was determined that the VMD+EWT method exhibited superior denoising performance compared to the other two methods investigated in this study. Furthermore, further analysis of the surface potential of maize leaves under electrical stimulation revealed that the signal frequency primarily ranged from 0-30 Hz, with significant energy concentration observed particularly within the 0–1 Hz frequency range. Additionally, when action potentials were generated under electrical stimulation, there was a high concentration of energy. Further investigation into the transmission characteristics of surface potential in maize leaves exposed to electrical stimulation indicated a leaf potential transmission speed ranging from 29 mm/s to 51 mm/s.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924008329","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Plant electrical signals are rapid responses of plants to external stimuli, and their characteristic changes are closely associated with plant life activities. However, due to their weak and low-frequency nature, the collected signals often suffer from significant noise interference. Therefore, investigating appropriate denoising methods is crucial for subsequent data analysis. In this study, a plant electrical signal synchronous acquisition system based on a 16-channel array electrode was employed to collect and store surface potentials of maize leaves under no stimulation, light stimulation, and electrical stimulation conditions. To address the issue of excessive noise in raw plant electrical signals, we propose a denoising method (VMD-EWT) that combines Variational Mode Decomposition (VMD) with Empirical Wavelet Transform (EWT). Based on the denoised multi-channel data obtained through these methods, we analyze the transmission characteristics and variation patterns of plant leaf electrical signals. The results demonstrate that traditional wavelet hard and soft thresholding-based denoising methods as well as VMD+EWT were utilized to remove noise from the plant surface potential data under electrical stimulation.The comprehensive evaluation indicators included the energy ratio and waveform analysis of the denoised signal in the time, frequency, and time–frequency domains. Based on a comprehensive assessment, it was determined that the VMD+EWT method exhibited superior denoising performance compared to the other two methods investigated in this study. Furthermore, further analysis of the surface potential of maize leaves under electrical stimulation revealed that the signal frequency primarily ranged from 0-30 Hz, with significant energy concentration observed particularly within the 0–1 Hz frequency range. Additionally, when action potentials were generated under electrical stimulation, there was a high concentration of energy. Further investigation into the transmission characteristics of surface potential in maize leaves exposed to electrical stimulation indicated a leaf potential transmission speed ranging from 29 mm/s to 51 mm/s.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.