Detection of chlorophyll content in growth potato based on spectral variable analysis

Abstract

Monitoring the chlorophyll content in tuber formation and tuber bulking stage has great significance for the nutritional status diagnosis in the potato field. In this paper, the 314 canopy spectra of potato crops were collected at four stages, respectively. The leaves were collected simultaneously to measure the chlorophyll content. After spectra pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. Two variable selection algorithms (successive projection algorithm, and random frog algorithm) were employed to select chlorophyll characteristic wavelengths. The partial least square algorithm was used for modeling analysis. The performance of sensitive wavelengths selected by two algorithms was compared, respectively, from the perspective of correlation with chlorophyll, reflecting leaf information, and model result. The sensitive wavelengths selected by random frog was optimal. The determination coefficient of calibration and validation set of the detection model established based on above sensitive wavelengths was respectively 0.827 and 0.798. The results showed the chlorophyll content of potato could be detected accurately based on the spectroscopy method.