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Abstract. This study is performed to investigate the potential of near infrared (NIR) spectroscopy for the detection of botanical impurity content of seed cotton harvested by cotton-picker (SCHCP). In China, the impurity content of seed cotton (SC) has to be detected when farmers sell the SC to ginneries because the weight of the impurity needs be deducted from the whole weight. Ginning and impurity analysis which is complex and time consuming is the normally used method to detect the impurity content of SC. In this study, the models between NIR spectra (4000-12000 cm^-1) and the impurity content of SC samples have been developed with the method of partial least square regression (PLSR), multiplicative signal correction (MSC) was used to eliminate the negative effects caused by sample shapes. The models of the original FT spectra, 1^st derivate spectra and 2^nd derivative spectra were compared, the results indicate that the 2^nd derivate spectra are most suitable for botanical impurity detection in SCHCP.