Vertical distribution of chlorophyll in potato plants based on hyperspectral imaging

Abstract: To monitor the potato growth status, the chlorophyll content was involved and its vertical distribution was detected at different leaf positions in plants. In this paper, the experiment was conducted during flowering stage of potato in the greenhouse of School of Water Conservancy and Engineering, China Agricultural University in June 2017. The potato leaf samples were randomly collected at 3 leaf positions (upper, middle, lower) of different potato plants. Then the hyper-spectral data of 66 potato leaf samples were divided into 400 regions of interesting (ROIs) and the SPAD (soil plant analysis development) values of the corresponding leaf positions were measured, of which 140 were taken from the upper part of the leaves and both 130 from the middle and lower leaves. The chlorophyll distribution was analyzed based on the vertical leaf position. The results indicated that its spectral reflectance in the range of 382-700 nm increased with the rise of the position. In the range of 700-1019 nm, the reflectance at the lower leaf position was higher than that at the upper and middle leaves, and the mean values of SPAD were 36.41, 43.11, and 47.04, respectively, at lower, upper and middle position. The black and white corrections of the potato leaf sample images collected using the hyperspectral imaging system were performed. After extracting and calculating the average leaf spectrum of the chlorophyll measurement area, the spectral data were pretreated by the standard normal variable (SNV) correction method, and then 2 sensitive wavelength selection methods were applied to built the chlorophyll content estimation models. The 12 wavelengths were chosen by using the correlation coefficient (CC) analysis and the 11 wavelengths were selected by the random frog (RF) algorithm. The results from the partial least squares regression (PLSR) model showed that 12 sensitive wavelengths selected by the CC analysis method were mainly located in the range of 530-550 and 706-708 nm. And in the PLSR, the modeling determination coefficient was 0.7588, and the predictive determination coefficient was 0.5773. Meanwhile, based on the RF algorithm, 11 sensitive wavelengths were 554.62, 560.26, 575.04, 576.35, 595.09, 604.7, 649.44, 731.8, 752.78, 786.38, and 789.97 nm. The modeling determination coefficient of the PLSR prediction model built with these wavelengths was 0.842 3, and the predictive determination coefficient was 0.767 6. Thus, the chlorophyll content of each leaf of potato was calculated by RF-PLS model, and the visual distribution of chlorophyll content in potato leaves was plotted. The results showed that the hyperspectral imaging could reflect the dynamic response of potato chlorophyll in flowering stage and achieve the non-destructive detection of potato leaf chlorophyll content and the visual expression of chlorophyll distribution.