Abstract: With regarding to the status quo ofpoor accuracy of single resistanceand instability of single capacitance detection on moisture regain of seed cotton in trade, this study proposed a detection method of moisture regain of seed cotton based on the resistance and capacitance information fusion. Firstly, the reliability of the resistance and capacitance detection board of the test platform was verified with using standard resistance and capacitance. Then, the detection platform and Eight Basket oven were used to test the cotton samples and acquire thedata. Based on the data, a testing model of moisture regain of seed cotton was built according to the information fusion of resistance and capacitance. In this process, multiple linear regression, partial least squares regression, support vector regression and back propagation neural network were used to construct and verify the models. The BPNNmodel achieved the best performanceamong the other three models, where the determination coefficient R~2 was 0.8966, the RMSE was 0.0504%, and the timecostwas 0.6690s. Finally, the information fusion algorithm was compared with single resistance and single capacitance method, respectively, and the results showed that the method of information fusion of resistance and capacitancewas superior to the single resistance and single capacitance method. Therefore, this method was feasible and could provide insightful reference for detecting moisture regain of seed cotton in the purchase process.