Crop chlorophyll detection based on multiexcitation fluorescence imaging analysis

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
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The fluorescence images were processed after segmentation and channel extraction to calculate the parameters of each leaf based on fluorescence images (<span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>440</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>520</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>690</mn></mrow></math></span>, and <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>740</mn></mrow></math></span>) obtained by UV excitation, and fluorescence images (<span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>440</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>520</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>690</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>740</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>b</mi></msub><mn>690</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>b</mi></msub><mn>740</mn></mrow></math></span>, and <span><math><mrow><msub><mi>F</mi><mi>r</mi></msub><mn>740</mn></mrow></math></span>) obtained by three excitations of 365 nm, 460 nm, and 610 nm light. 12 fluorescence ratio parameters under UV excitation and 26 fluorescence ratio parameters under three excitations were calculated. The correlation analysis revealed that the fluorescence parameters (<span><math><mrow><msub><mi>F</mi><mi>r</mi></msub><mn>740</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>440</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>520</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>690</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>740</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>b</mi></msub><mn>690</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>b</mi></msub><mn>740</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>440</mn><mo>/</mo><msub><mi>F</mi><mi>u</mi></msub><mn>520</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>520</mn><mo>/</mo><msub><mi>F</mi><mi>u</mi></msub><mn>690</mn></mrow></math></span>, and <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>740</mn><mo>/</mo><msub><mi>F</mi><mi>r</mi></msub><mn>740</mn></mrow></math></span>) showed a strong correlation with the chlorophyll content. These parameters have the potential to measure the chlorophyll content. Subsequently, stepwise regression analysis (SRA) was employed to screen 16 fluorescence parameters under UV excitation and 33 fluorescence parameters under three excitations, with the objective of identifying and eliminating redundant variables. Finally, four variables (<span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>520</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>690</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>740</mn></mrow></math></span>, and <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>690</mn><mo>/</mo><msub><mi>F</mi><mi>u</mi></msub><mn>520</mn></mrow></math></span>) under UV excitation and five variables (<span><math><mrow><msub><mi>F</mi><mi>r</mi></msub><mn>740</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>520</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>b</mi></msub><mn>740</mn></mrow></math></span>, <span><math><mrow><msub><mi>F</mi><mi>u</mi></msub><mn>740</mn><mo>/</mo><msub><mi>F</mi><mi>u</mi></msub><mn>690</mn></mrow></math></span>, and <span><math><mrow><msub><mi>F</mi><mi>b</mi></msub><mn>740</mn><mo>/</mo><msub><mi>F</mi><mi>b</mi></msub><mn>690</mn></mrow></math></span>) under three excitations were selected. The partial least squares regression (PLSR) model, constructed using three excitations, demonstrated enhanced performance with an <span><math><mrow><msubsup><mi>R</mi><mi>c</mi><mn>2</mn></msubsup></mrow></math></span> of 0.901, <span><math><mrow><msubsup><mi>R</mi><mi>v</mi><mn>2</mn></msubsup></mrow></math></span> of 0.904, root mean square error (RMSE) of calibration of 4.398, and RMSE of validation of 4.267. Multiexcitation fluorescence based on three excitations techniques has better performance for evaluating chlorophyll content.</p></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S153751102400165X","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

The chlorophyll content of wheat was assessed using multispectral fluorescence imaging (MSFI). Ultraviolet (UV) light (365 nm)-induced fluorescence images at 440, 520, 690, and 740 nm, and visible light (460, and 610 nm)-induced fluorescence images at 690 and 740 nm were acquired while leaf chlorophyll content was measured using SPAD 520. The fluorescence images were processed after segmentation and channel extraction to calculate the parameters of each leaf based on fluorescence images (Fu440, Fu520, Fu690, and Fu740) obtained by UV excitation, and fluorescence images (Fu440, Fu520, Fu690, Fu740, Fb690, Fb740, and Fr740) obtained by three excitations of 365 nm, 460 nm, and 610 nm light. 12 fluorescence ratio parameters under UV excitation and 26 fluorescence ratio parameters under three excitations were calculated. The correlation analysis revealed that the fluorescence parameters (Fr740, Fu440, Fu520, Fu690, Fu740, Fb690, Fb740, Fu440/Fu520, Fu520/Fu690, and Fu740/Fr740) showed a strong correlation with the chlorophyll content. These parameters have the potential to measure the chlorophyll content. Subsequently, stepwise regression analysis (SRA) was employed to screen 16 fluorescence parameters under UV excitation and 33 fluorescence parameters under three excitations, with the objective of identifying and eliminating redundant variables. Finally, four variables (Fu520, Fu690, Fu740, and Fu690/Fu520) under UV excitation and five variables (Fr740, Fu520, Fb740, Fu740/Fu690, and Fb740/Fb690) under three excitations were selected. The partial least squares regression (PLSR) model, constructed using three excitations, demonstrated enhanced performance with an Rc2 of 0.901, Rv2 of 0.904, root mean square error (RMSE) of calibration of 4.398, and RMSE of validation of 4.267. Multiexcitation fluorescence based on three excitations techniques has better performance for evaluating chlorophyll content.

基于多激发荧光成像分析的作物叶绿素检测
利用多光谱荧光成像技术(MSFI)评估了小麦的叶绿素含量。采集紫外线(365 nm)诱导的 440、520、690 和 740 nm 波长的荧光图像,以及可见光(460 和 610 nm)诱导的 690 和 740 nm 波长的荧光图像,同时使用 SPAD 520 测量叶片叶绿素含量。荧光图像经过分割和通道提取处理后,根据紫外线激发的荧光图像(Fu440、Fu520、Fu690 和 Fu740)和 365 nm、460 nm 和 610 nm 三种激发的荧光图像(Fu440、Fu520、Fu690、Fu740、Fb690、Fb740 和 Fr740)计算出每片叶片的参数。计算出紫外激发下的 12 个荧光比参数和三次激发下的 26 个荧光比参数。相关分析表明,荧光参数(Fr740、Fu440、Fu520、Fu690、Fu740、Fb690、Fb740、Fu440/Fu520、Fu520/Fu690 和 Fu740/Fr740)与叶绿素含量有很强的相关性。这些参数具有测量叶绿素含量的潜力。随后,采用逐步回归分析法(SRA)筛选了紫外激发下的 16 个荧光参数和三次激发下的 33 个荧光参数,目的是识别和剔除多余的变量。最后,筛选出紫外激发下的 4 个变量(Fu520、Fu690、Fu740 和 Fu690/Fu520)和三种激发下的 5 个变量(Fr740、Fu520、Fb740、Fu740/Fu690 和 Fb740/Fb690)。使用三种激发构建的偏最小二乘回归(PLSR)模型显示出更高的性能,Rc2 为 0.901,Rv2 为 0.904,校准均方根误差(RMSE)为 4.398,验证均方根误差为 4.267。基于三次激发的多激发荧光技术在评估叶绿素含量方面具有更好的性能。
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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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