马铃薯冠层光谱响应特征参数优化与生长期判别
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摘要
快速判别马铃薯作物的生长进程是指导田间关键生长期科学水肥管理的重要依据。研究在马铃薯发棵期(M1)、块茎形成期(M2)、块茎膨大期(M3)和淀粉积累期(M4)四个关键生长期,利用ASD便携式光谱仪采集80个样本区的314组作物冠层反射率数据,并同步采集叶片测定叶绿素含量。在光谱数据预处理后,分析了马铃薯不同生长期的光谱反射率变化特征,并初步选取了光谱"峰谷"响应参数,提出了一种基于方差分析与变量减少组合的光谱参数筛选算法(variance analysis combined with variable reduction, VACVR)用于明确光谱学响应的优化指标,采用Kennard-Stone(K-S)法划分样本集,最终基于支持向量机(support vector machine, SVM)方法建立马铃薯关键生长期判别模型。针对光谱数据,首先使用变量标准化(standard normalized variable, SNV)进行光谱预处理,在定性分析了随着生长期的推进马铃薯冠层反射特征的变化趋势的基础上,基于作物生长期动态光谱学响应与峰谷特性选取14个参数,包括:8个位置参数、 2个面积参数、 4个植被指数参数。采用K-S算法将样本按照3∶1划分为训练集(240个样本)和测试集(74个样本)。分析马铃薯不同生长期冠层反射光谱发现,随生长期的推进冠层光谱存在差异性:即在400～500和740～880 nm范围内,光谱反射率呈降低趋势;在530～640和910～960 nm范围内,反射率呈升高趋势;在530～640 nm范围内, M2和M3生长期的平均光谱非常接近, M4生长期的平均光谱与其他三个生长期的差别较大。叶绿素平均含量随生长期的进程,从M1(28.12 mg·L~(-1))到M2(31.04 mg·L~(-1))增加,在M2生长期达到最大值,之后M3(22.00 mg·L~(-1))和M4(15.36 mg·L~(-1))依次减少。光谱响应参数随着生长期的进程,绿峰位置L_g和红谷位置L_r逐渐红移,红边位置L_(re)逐渐蓝移;蓝边面积A_(be)逐渐增大,红边面积A_(re)逐渐减小;红边面积与蓝边面积比值依次呈现减小趋势。根据VACVR算法筛选10个敏感光谱响应参数,建立SVM判别模型,训练集判别率为100%,测试集判别率为94.59%,该模型可在判别马铃薯的生长期的基础上为田间管理决策提供支持。
In order to satisfy the field management requirement, the research was conducted to indicate the optimizing parameters and identify the growth stage based on the canopy spectral response of potato plants. Aiming to the four growth stages of potato, tillering stage(M1), tuber formation stage(M2), tuber expansion stage(M3) and starch accumulation stage(M4), 80 sample plots were divided in the potato field. The 314 groups data of canopy spectral reflectance were collected by ASD Handheld2 portable spectrometer. The potato leaves were collected synchronously in per sample plot to determine the chlorophyll content. After spectral pretreatment, the spectral reflectance changes of potato crop at different growth stages were analyzed. The spectral response parameters of potato growth stages were selected according to the "peak-valley" reflectance characteristics. A new algorithm was proposed to select sensitive spectral response parameters based on the variance analysis combined with variable reduction(VACVR) method. The Kennard-Stone(K-S) algorithm was used to divide the all samples into training sets and test sets. The identification model of potato growth stages was established by the support vector machine(SVM) method. For spectral reflectance, the standard normalized variable(SNV) was used for spectral pretreatment. Based on the qualitative analysis of the canopy reflection characteristics change trend as potato growth stage progress, the 14 spectral response parameters, including the 8 position parameters, the 2 area parameters and the 4 vegetation index parameters, were selected combining with spectral "peak-valley" characteristics and the dynamicspectral response of potato growth stages. The K-S algorithm was used to divide the overall sample according to 3∶1 into a training set(240 samples) and a test set(74 samples). In general, the canopy spectral reflectance varied with the growth stages progress. In the range of 400～500 and 740～880 nm, the spectral reflectance decreased. In the range of 530～640 and 910～960 nm, the spectral reflectance increased. In the range of 530～640 nm, the canopy average spectral reflectance of the M2 and M3 growth stage were very close. The canopy average spectral reflectance of the M4 growth stage was significantly different from that of the other three growth stages. The average chlorophyll content increased from M1(28.12 mg·L~(-1)) to M2(31.04 mg·L~(-1)), reaching a maximum in the M2 growth stage. And the average chlorophyll content of M3(22.00 mg·L~(-1)) and M4(15.36 mg·L~(-1)) reduced successively. With the progress of the growth stage, the green peak position and the red valley position gradually red-shifted, the red edge position gradually blue-shifted, the blue edge area gradually increased, the red edge area decreased gradually, and the ratio and normalized ratio of red edge area to blue edge decreased in turn. According to the VACVR algorithm, 10 sensitive spectral response parameters were selected to establish the SVM identification model. The identification rate of the training set was 100%, and the identification rate of the test set was 94.59%(70/74). Therefore, the model can identify the potato growth stage to support the water and fertilizer management in the potato field.
In order to satisfy the field management requirement, the research was conducted to indicate the optimizing parameters and identify the growth stage based on the canopy spectral response of potato plants. Aiming to the four growth stages of potato, tillering stage(M1), tuber formation stage(M2), tuber expansion stage(M3) and starch accumulation stage(M4), 80 sample plots were divided in the potato field. The 314 groups data of canopy spectral reflectance were collected by ASD Handheld2 portable spectrometer. The potato leaves were collected synchronously in per sample plot to determine the chlorophyll content. After spectral pretreatment, the spectral reflectance changes of potato crop at different growth stages were analyzed. The spectral response parameters of potato growth stages were selected according to the "peak-valley" reflectance characteristics. A new algorithm was proposed to select sensitive spectral response parameters based on the variance analysis combined with variable reduction(VACVR) method. The Kennard-Stone(K-S) algorithm was used to divide the all samples into training sets and test sets. The identification model of potato growth stages was established by the support vector machine(SVM) method. For spectral reflectance, the standard normalized variable(SNV) was used for spectral pretreatment. Based on the qualitative analysis of the canopy reflection characteristics change trend as potato growth stage progress, the 14 spectral response parameters, including the 8 position parameters, the 2 area parameters and the 4 vegetation index parameters, were selected combining with spectral "peak-valley" characteristics and the dynamicspectral response of potato growth stages. The K-S algorithm was used to divide the overall sample according to 3∶1 into a training set(240 samples) and a test set(74 samples). In general, the canopy spectral reflectance varied with the growth stages progress. In the range of 400～500 and 740～880 nm, the spectral reflectance decreased. In the range of 530～640 and 910～960 nm, the spectral reflectance increased. In the range of 530～640 nm, the canopy average spectral reflectance of the M2 and M3 growth stage were very close. The canopy average spectral reflectance of the M4 growth stage was significantly different from that of the other three growth stages. The average chlorophyll content increased from M1(28.12 mg·L~(-1)) to M2(31.04 mg·L~(-1)), reaching a maximum in the M2 growth stage. And the average chlorophyll content of M3(22.00 mg·L~(-1)) and M4(15.36 mg·L~(-1)) reduced successively. With the progress of the growth stage, the green peak position and the red valley position gradually red-shifted, the red edge position gradually blue-shifted, the blue edge area gradually increased, the red edge area decreased gradually, and the ratio and normalized ratio of red edge area to blue edge decreased in turn. According to the VACVR algorithm, 10 sensitive spectral response parameters were selected to establish the SVM identification model. The identification rate of the training set was 100%, and the identification rate of the test set was 94.59%(70/74). Therefore, the model can identify the potato growth stage to support the water and fertilizer management in the potato field.
引文
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[2] Beheral S K,Panda R K.AssamUniversity Journal of Science & Technology,2010,4(2):22.
[3] SUN Lei,WANG Hong,LI Ming-yue,et al(孙磊,王弘,李明月,等).Chinese Potato Journal(中国马铃薯),2013,(5):314.
[4] Zotarelli L,Rens L R,Cantliffe D J,et al.Field Crops Research,2015,183:246.
[5] SUN Hong,ZHENG Tao,LIU Ning,et al(孙红,郑涛,刘宁,等).Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2018,34(1):149.
[6] DIAO Hang,WU Yong-ming,YANG Yu-hong,et al(刁航,吴永明,杨宇虹,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(6):1826.
[7] Roosjen P P J,Brede B,Suomalainen J M,et al.International Journal of Applied Earth Observation & Geoinformation,2018,66:14.
[8] ZHAO Xin-fei,FENG Jia-min,ZHANG Da-wei,et al(赵新飞,冯嘉敏,张大伟,等).Journal of Zhongkai University of Agriculture & Engineering(仲恺农业工程学院学报),2015,28(4):26.
[9] Dutta D,Das P K,Paul S,et al.Journal of the Indion Society of Remote Sensing,2016,44(3):363.
[10] LI Feng,Alchanatis Victor,ZHAO Hong,et al(李峰,Alchanatis Victor,赵红,等).Chinese Journal of Agrometeorology(中国气象学报),2014,35(3):338.
[11] Zhou Z,Jabloun M,Plauborg F,et al.Computers & Electronics in Agriculture,2018,144:154.
[12] QIN Wei-zhi,XIONG Jun,ZHENG Xu,et al(覃维治,熊军,郑虚,等).Journal of Southern Agriculture(南方农业学报),2017,48(6):985.
[13] WANG Xu,LIU Ren-jie,SUN Hong,et al(王旭,刘仁杰,孙红,等).Transactions of the Chinese Society for Agricultural Machinery(农业机械学报),2017,(s1):90.
[14] Fu JuiHsi,Lee SingLing.Expert System with Applications,2012,39(3):3127.
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