Abstract:Non-destructive detection of chlorophyll content and drawing the chlorophyll distribution map of potato crop leaves could indicate crop growth and guide field management. In this paper, the hyperspectral imaging technique was used to diagnose the chlorophyll content index and help to describe chlorophyll distribution of potato leaf. The hyperspectral images of 65 potato leaves were collected and divided into 400 regions of interesting (ROI). Meanwhile, the SPAD values of these 400 ROI samples were measured. After extracting and calculating the average leaf spectrum of the chlorophyll measurement area, the 12 chlorophyll content sensitive wavelengths were chosen by the Monte Carlo uninformative variables elimination (MC-UVE) algorithm and the 23 chlorophyll content sensitive wavelengths were selected by the competitive adaptive reweighted sampling (CARS) algorithm. They were used to establish the partial least squares regression (PLSR) model of chlorophyll content index of potato leaves respectively. The results were as follows: 12 sensitive wavelengths selected by MC-UVE algorithm were 532.54nm, 534.27nm, 566.78nm, 737.60nm, 741.61nm, 742.51nm, 759.49nm, 772.92nm, 816.54nm, 880.88nm, 928.84nm, 943.88nm. The modeling determination coefficient was 0.79, and predictive determination coefficient was 0.73. Meanwhile, 23 sensitive wavelengths selected by the CARS algorithm were 394.01nm, 399.94nm, 492.03nm, 493.32nm, 494.18nm, 534.27nm, 536.86nm, 537.30nm, 537.73nm, 543.79nm, 544.22nm, 545.52nm, 547.25nm, 547.69nm, 548.12nm, 550.29nm, 550.72nm, 553.76nm, 555.49nm, 938.93nm, 986.36nm, 987.74nm, 1018.30nm.The modeling determination coefficient of the PLSR diagnostic model built with these wavelengths was 0.82, and predictive determination coefficient was 0.80. Thus, the chlorophyll content of potato leaves can be calculated by CARS-PLS model, and the visual distribution map of chlorophyll content in potato leaves was plotted by using pseudo-color drawing. It provides a method for the diagnosis of chlorophyll distribution in the future.