|
|
|
|
|
|
Parameter Optimization of Potato Spectral Response Characteristics and Growth Stage Identification |
SUN Hong1, LIU Ning1, XING Zi-zheng1, ZHANG Zhi-yong1, LI Min-zan1*, WU Jing-zhu2 |
1. Key Laboratory of Modern Precision Agriculture System Integration Research, China Agricultural University, Beijing 100083, China
2. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China |
|
|
Abstract 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.
|
Received: 2018-09-27
Accepted: 2019-02-10
|
|
Corresponding Authors:
LI Min-zan
E-mail: limz@cau.edu.cn
|
|
[1] Horvat T, Poljak M, Lazarevic B, et al. Növénytermelés, 2010, 59: 215.
[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. |
[1] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[2] |
CHENG Gang1, CAO Ya-nan1, TIAN Xing1, CAO Yuan2, LIU Kun2. Simulation of Airflow Performance and Parameter Optimization of
Photoacoustic Cell Based on Orthogonal Test[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3899-3905. |
[3] |
CUI Xiang-yu1, 3, CHENG Lu1, 2, 3*, YANG Yue-ru1, WU Yan-feng1, XIA Xin1, 3, LI Yong-gui2. Color Mechanism Analysis During Blended Spinning of Viscose Fibers Based on Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3916-3923. |
[4] |
CUI Song1, 2, BU Xin-yu1, 2, ZHANG Fu-xiang1, 2. Spectroscopic Characterization of Dissolved Organic Matter in Fresh Snow From Harbin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3937-3945. |
[5] |
FENG Hai-kuan1, 2, FAN Yi-guang1, TAO Hui-lin1, YANG Fu-qin3, YANG Gui-jun1, ZHAO Chun-jiang1, 2*. Monitoring of Nitrogen Content in Winter Wheat Based on UAV
Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3239-3246. |
[6] |
LI Bin, HAN Zhao-yang, WANG Qiu, SUN Zhao-xiang, LIU Yan-de*. Research on Bruise Level Detection of Loquat Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1792-1799. |
[7] |
NIU An-qiu, WU Jing-gui*, ZHAO Xin-yu. Infrared Spectrum Analysis of Degradation Characteristics of PPC Plastic Film Under Different Covering Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 533-540. |
[8] |
JIANG Xiao-gang1, ZHU Ming-wang1, YAO Jin-liang1, LI Bin1, LIAO Jun1, LIU Yan-de1*, ZHANG Jian-yi2, JING Han-song2. Research on Parameter Optimization of Apple Sugar Model Based on Near-Infrared On-Line Device[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 116-121. |
[9] |
ZHANG Jian1, LIU Ya-jian2, CAO Ji-hu3. Raman Spectral Characteristics of Pyrite in Luyuangou Gold Deposit, Western Henan Province and Its Indicative Significance for Multiphase Metallogenesis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3770-3774. |
[10] |
ZHANG Heng-ming1, SHI Yu1*, LI Chun-kai1, 2, 3, GU Yu-fen1, ZHU Ming1. The Effect of Electrode Polarity on Arc Plasma Spectral Characteristics of Self-Shielded Flux Cored Arc Welding[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3917-3926. |
[11] |
DAI Qian-cheng1, XIE Yong1*, TAO Zui2, SHAO Wen1, PENG Fei-yu1, SU Yi1, YANG Bang-hui2. Research on Fluorescence Retrieval Algorithm of Chlorophyll a Concentration in Nanyi Lake[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3941-3947. |
[12] |
WANG Dong-sheng1, WANG Hai-long1, 2, ZHANG Fang1, 3*, HAN Lin-fang1, 3, LI Yun1. Near-Infrared Spectral Characteristics of Sandstone and Inversion of Water Content[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3368-3372. |
[13] |
HUANG Yue-hao1, 2, JIN Yong-ze2. Analysis and Research on Spectral Characteristics of the Traditional Architectural Color Painting Pigments in Regong, Qinghai Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3519-3525. |
[14] |
YAN Kang-ting1, 2, HAN Yi-fang1, 2, WANG Lin-lin1, 2, DING Fan3, LAN Yu-bin1, 2*, ZHANG Ya-li2, 3*. Research on the Fluorescence Spectra Characteristics of Abamectin Technical and Preparation Solution[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3476-3481. |
[15] |
CAO Yu-qi2, KANG Xu-sheng1, 2*, CHEN Piao-yun2, XIE Chen2, YU Jie2*, HUANG Ping-jie2, HOU Di-bo2, ZHANG Guang-xin2. Research on Discrimination Method of Absorption Peak in Terahertz
Regime[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3058-3062. |
|
|
|
|