Moisture regain sensing method for seed cotton under multi-factor fusion

To enhance the accuracy of moisture regain (MR) sensing in seed cotton during mechanical harvesting, a capacitance, resistance, density and temperature (CRDT) fusion method for seed cotton MR sensing was proposed in this study. Initially, seed cotton samples exhibiting various MR levels were prepared using a saturated salt solution method to control the environmental humidity. Subsequently, an experimental platform was designed to simulate the changing temperature and density in the packaging room of a cotton picker. The capacitance and resistance of the “Zhongmian 113” seed cotton samples were measured under eight temperature gradients (5, 10, 15, 20, 25, 30, 35, and 40 °C) and eleven density gradients (149.28, 155.50, 162.26, 169.64, 177.71, 186.60, 196.42, 207.33, 219.53, 233.25 and 248.80 kg/m³). The Pearson correlation analysis showed that MR had a high correlation with capacitance (0.82) and resistance (−0.88). Capacitance displayed a linear relationship with both density (R² = 0.99) and temperature (R² = 0.93), whereas resistance demonstrated a linear relationship with density (R² = 0.94) and an exponential relationship with temperature (R² = 0.97). Furthermore, multiple linear regression (MLR), back propagation neural network (BPNN), support vector machine (SVM) and random forest (RF) were used to build models for seed cotton MR sensing under varying conditions. The results showed that the CRDT fusion model outperformed the models constructed based solely on capacitance or resistance. Among the modeling strategies, the BPNN exhibited the best performance, with an R² of 0.99 and an RMSE of 0.21 %. This study will provide a valuable reference for the development of seed cotton MR sensor in cotton mechanical harvesting.