Abstract: Cotton is one of the most crucial global textile raw materials to determine the quality of final products. The moisture content of seed cotton can profoundly impact the storage, transportation, and textile processing of cotton. This study aims to rapidly and non-destructively measure the moisture content of seed cotton. A quantitative detection model was established using Fourier-transformed near-infrared spectroscopy. A series of experiments were conducted to explore the influence of seed cotton sample density on spectral curves. Sample density was significantly dominated in the spectral curves, where the lower densities resulted in stronger spectral signals. However, the fluctuations were stabilized in the spectral curve, when the sample density reached a specific threshold 0.088 60 g/cm³. Data accuracy and comparability were the pivotal reference points. Subsequently, Fourier-transformed near-infrared spectroscopy was employed to collect the absorbance spectral data of seed cotton samples within the range of 3 900-11 000 cm⁻¹ wavelength. The samples were dried in an air blast drying oven (DWG-9240A) under constant temperature. The airflow was used to remove the moisture from the seed cotton. Pre-experimental verification showed that the weight of samples remained relatively unchanged, when the temperature was raised to (105 ±3) ℃ and maintained for three hours. Afterward, the dried samples were removed and weighed to measure their moisture content using a balance with a precision of 0.001 g. Furthermore, nine preprocessing methods were applied to the original spectral data, in order to enhance the data quality. Comparative analysis determined that the best performance was achieved in the first-order derivative combined with detrending (FD-DT) preprocessing using the Partial Least Squares Regression (PLSR) model. The calibration and prediction set determination coefficients were 0.974, and 0.845, respectively, with the root mean square errors of 0.316 and 0.721, respectively, as well as the residual prediction deviation (R_PD) of 3.00 after FD-DT preprocessing. The wavenumber range of 4000-10000 cm⁻¹ was selected to extract feature spectral data. The reason was that the lower sensitivity and response of the spectrometer at the beginning and end of the spectra, led to weaker or unstable signals in these regions. The optimal feature wavelengths were then obtained using Competitive Adaptive Reweighted Sampling (CARS), Information Gain (IG), Successive Projections Algorithm (SPA), and Pearson's Correlation Coefficient (CC). The feature wavelength counts of 47, 27, 30, and 27, accounting for 6.03%, 3.46%, 3.85%, and 3.46% of the spectral range, respectively. These features effectively reduced the number of variables for the high efficiency and performance of the model. After the extraction of feature wavelength, the quantitative models were established to predict the moisture content of seed cotton using Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM). Comprehensive analysis of various analytical algorithms showed that the combination of FD-DT-CARS-PLSR and FD-DT-CARS-SVM was the most effective predictive model, where the determination coefficients of 0.933 and 0.931, root mean square errors of 0.480 and 0.500, and residual prediction deviations of 3.88 and 3.85 in the prediction dataset. FD-DT was used to effectively remove the trends and noise from the data for data quality and usability. CARS was used to efficiently select the most relevant feature wavelengths for the performance and prediction accuracy of the model. PLSR demonstrated excellent performance on multicollinear data for better interpretability, but with a relatively weaker performance in fitting nonlinear data. SVM displayed strong capabilities in nonlinear modeling and adaptability to high-dimensional data, but it was relatively difficult to interpret with the slower training times for large datasets. Two models can be combined to effectively predict in this case. In summary, near-infrared spectroscopy can be expected to rapidly, non-destructively, and accurately detect the moisture content of seed cotton .