In order to improve the detection accuracy and applicability of wheat moisture content detection device for combined harvester based on dielectric properties, the wheat moisture content prediction model was established based on GA-BP method. Focusing on three varieties of wheat, namely “Jingdong 22”, “Shumai 1958” and “Womai 33”. The measured range of wheat moisture content was 8.41% to 21.6% , with the detection temperature ranging from 5℃ to 40℃ and the bulk density ranging from 714.44 kg / m 3 to 777.58 kg / m 3 for wheat dielectric constant. The experiment results indicated that at constant temperature conditions, higher bulk density corresponded to a larger dielectric constant. Similarly, under consistent bulk density, the dielectric constant was increased with the increase of temperature and moisture content. To establish the relationship between dielectric constant, temperature, bulk density, and wheat moisture content, a genetic algorithm optimized back propagation neural network (GA-BP) with 150 samples in the calibration set and 42 samples in the prediction set was established. The model, with a 3-5-1 structure, a maximum iteration of 1 000 times, and a learning error threshold of 1×10 - 6 , demonstrated high detection accuracy and stability. The verification set R 2 , RMSE, and MAE values were 0.996, 0.241% , and 0.189% , respectively, while the prediction set returned values were 0.993, 0.295% , and 0.189% . These results underscored the model’s efficacy in providing a method for online moisture content detection in wheat of different varieties.