The moisture regain rate significantly affects the test results of cotton quality indicators. Accurately measuring the moisture regain rate is of great significance for cotton grading. Aiming at the compensation and correction of the moisture regain rate during the detection of the breaking tenacity of cotton bundle fibers, a method for detecting the moisture regain rate based on the resistance method was proposed. By building a resistance-image synchronous acquisition platform and using image features to represent the fiber thickness, the influence laws of the electrode distance, temperature, and fiber thickness on the resistance measurement during the moisture regain rate measurement were explored, and a multiple prediction model with resistance and temperature as input variables was established. Experiments showed that the gray-scale features of the image were highly correlated with the resistance value and showed a non-linear relationship, and the influence law of the fiber thickness on the resistance measurement was ascertained. The resistance value had a significant positive correlation with the electrode distance within the range of 2~12mm. The intrinsic mechanism of the increase in electrode spacing leading to the expansion of resistance measurement error was explained. Based on this, an electrode distance of 2mm was determined as the optimal detection parameter, and it was verified that there was no significant difference in the resistance of different quality fibers under this parameter (P>0.05). Through experiments on 32 groups of cotton samples with a moisture regain rate of 4.44%~12.2%, the results showed that the random forest (RF) model had the best prediction accuracy, with R2 of 0.99 and RMSE of 0.24%. This study broke through the limitations of traditional moisture regain detection methods for loose cotton fibers and enabled rapid measurement of bundled fibers. It can provide reliable technical support for the precise compensation and correction of physical property indicators such as the breaking tenacity of cotton, and promote the development of intelligent cotton quality detection.