Abstract: Abstract: Faced with the worldwide shortage of forest resources, industry is showing increasing interest in using straw. Moisture content is listed as a main criterion in wheat straw consuming market. Not only since the unit price is based on weight, but also because moisture content is an important factor which affect straw products' quality. Wheat straw is a main kind of straws all over the world. In order to detect moisture content of wheat straw conveniently, rapidly and precisely, a moisture content meter was designed with AT89S52 single-chip microcomputer as controller, parallel plate, DS18B20 and FSR402 as capacitance sensor, digital temperature sensor and pressure sensor to detect capacitance, temperature and bulk density of wheat straw, respectively. Liquid crystal display was adopted to show the obtained data. The meter's accuracy on measuring capacitance, temperature and bulk density was tested. "Xinong 979" winter wheat straw was used as sample to study the influence of moisture content on output capacitance. The tests were set at five levels (10.6%, 13.6%, 15.6%, 17.3% and 19.6% in wet basis), temperature range from 5℃ to 35℃ with 5℃ interval, and three bulk density levels (generally from 77.2 kg/m3 to 103.6 kg/m3). The model describing capacitance and main factors was regressed. Newton iteration method was applied to program for predicting moisture content from obtained data. The model's feasibility in predicting moisture content from 10%-20% at 5-35℃ was verified. The results indicated that the output voltages of designed circuits for sensing capacitance and pressure had good linear relationship with real capacitance and pressure values, with coefficients of determination higher than 0.996. The absolute temperature error was ±0.2℃. Over the investigated ranges of moisture content, temperature and bulk density, the obtained capacitance value increased with increasing moisture content, temperature and bulk density. The relationship between capacitance and moisture content, temperature and bulk density could be described by trinary polynomial model. However, significance test showed that the bulk density had not significant effect on the model at 0.05 significance level. So a bivariate polynomial model of capacitance with moisture content and temperature was regressed, and the significance test showed that each item had significant effect on regressed model at 0.0005 level. Compared with measured capacitance, the relative error of calculated capacitance by known moisture content and temperature was within -6%-8%. It indicates that the bivariate regression model can well describe the relationship between capacitance and moisture content and temperature. The absolute error, sensitivity and response time for predicting moisture content of wheat straw from obtained capacitance and temperature was -0.9%-2.2%, 0.3% and less than 2 s, respectively. The study can provide a reference for development of straw moisture content meters.