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.
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