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基于熵權(quán)-模糊綜合評(píng)價(jià)法的無人機(jī)多光譜春玉米長勢監(jiān)測模型研究
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國家自然科學(xué)基金項(xiàng)目(52169013)、新疆維吾爾自治區(qū)十四五重大專項(xiàng)(2020A01003-4)和自治區(qū)研究生科研創(chuàng)新項(xiàng)目(XJ2024G126)


Growth Monitoring of Spring Maize Using UAV Multispectral Imaging Based on Entropy Weight-Fuzzy Comprehensive Evaluation Method
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    摘要:

    為實(shí)現(xiàn)春玉米長勢的快速監(jiān)測,實(shí)時(shí)掌握田間作物的生長狀況,本文以新疆維吾爾自治區(qū)克拉瑪依地區(qū)種植的春玉米作為研究對象,利用無人機(jī)多光譜影像對春玉米進(jìn)行長勢監(jiān)測?;诘孛娌杉拇河衩兹~片葉綠素含量、葉面積指數(shù)、地上部生物量和株高等數(shù)據(jù),結(jié)合熵權(quán)法(EWM)和模糊綜合評(píng)價(jià)法(FCE)建立綜合長勢指標(biāo)CGMIEWM和CGMIFCE。通過無人機(jī)遙感影像數(shù)據(jù)構(gòu)建光譜指數(shù),并利用皮爾遜相關(guān)性分析法和方差膨脹因子確定模型最佳輸入變量。采用偏最小二乘法(PLS)、隨機(jī)森林回歸(RF)及粒子群算法(PSO)優(yōu)化RF模型建立春玉米長勢反演模型,結(jié)合模型精度評(píng)價(jià)指標(biāo),最終確定春玉米空間影像長勢分布圖。結(jié)果表明,以CGMIEWM和CGMIFCE構(gòu)建綜合長勢指標(biāo)的相關(guān)性均高于單一長勢指標(biāo)的相關(guān)性;利用CGMIFCE長勢指標(biāo)結(jié)合PSO-RF模型反演春玉米長勢的效果最優(yōu),其決定系數(shù)(R2)為0.823,均方根誤差(RMSE)為0.084%,相對分析誤差(RPD)為2.345;研究區(qū)春玉米長勢集中在生長正常(ZZ)等級(jí),說明全區(qū)春玉米長勢較為穩(wěn)定。研究結(jié)果可為春玉米的田間管理提供科學(xué)依據(jù)。

    Abstract:

    To achieve rapid monitoring of spring maize growth and gain realtime understanding of field crop conditions, focusing on spring maize planted in the Karamay region of Xinjiang, utilizing UAV multispectral imagery for growth monitoring of the spring maize, based on groundcollected data on spring maize leaf chlorophyll content, leaf area index, aboveground biomass, and plant height, comprehensive growth indicators CGMIEWM and CGMIFCE were established by combining the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE). Spectral indices were constructed by using UAV remote sensing imagery data, and the optimal input variables for the model were determined by using Pearson correlation analysis and the variance inflation factor. Partial least squares (PLS), random forest regression (RF), and particle swarm optimization (PSO) were used to optimize the RF model and establish a spring maize growth inversion model. By combining model accuracy evaluation metrics, the spatial distribution map of spring maize growth was ultimately determined. The results showed that the comprehensive growth indicators constructed using CGMIEWM and CGMIFCE had higher correlations than single growth indicators. The growth indicators derived from CGMIFCE, combined with the PSO-RF model, resulted in the best performance for inversion of spring maize growth. The coefficient of determination (R2) was 0.823, the root mean square error (RMSE) was 0.084%, and the relative percent deviation (RPD) was 2.345. The growth of spring maize in the study area was mostly concentrated in the normal growth (ZZ) category, indicating relatively stable growth across the region. The research results can provide a scientific basis for the field management of spring maize and offer a data foundation for the development of precision agriculture.

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趙經(jīng)華,馬世驕,房城泰.基于熵權(quán)-模糊綜合評(píng)價(jià)法的無人機(jī)多光譜春玉米長勢監(jiān)測模型研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2024,55(8):214-224. ZHAO Jinghua, MA Shijiao, FANG Chengtai. Growth Monitoring of Spring Maize Using UAV Multispectral Imaging Based on Entropy Weight-Fuzzy Comprehensive Evaluation Method[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(8):214-224.

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  • 收稿日期:2024-04-18
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  • 在線發(fā)布日期: 2024-08-10
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