Abstract: Abstract: Peanut is an important oil and economic crop, and also a major export product in China. Peanut industries manufacture many types of peanut-based products for human's life. The moisture content of peanuts is in the range of 45%-50% when peanuts are freshly harvested, and the moisture content has to be below 10.5% in storage. High moisture content in peanut kernels can make them moldy, which mainly produces the toxic aflatoxins and causes huge waste and jeopardizes human's health. Therefore it is necessary to keep proper moisture content in every treating process of peanuts. The detection of moisture content plays an important role in harvesting, drying, storage and trade of peanuts. Although the traditional oven-drying method has high precision, it is cumbersome and takes a lot of time. In addition to the oven-drying method and the microwave method, the near infrared method and the capacitance method are also common methods of moisture content detection. Comparing to other methods, the capacitance method has advantages of simple structure and low cost. In order to develop a rapid and accurate moisture content detection method for peanut kernels, the capacitance method was adopted to measure moisture content by using dielectric properties of grain. A peanut kernel moisture content detector was designed; MSP430 single chip microcomputer was taken as its control chip, and cylindrical capacitance sensor, temperature sensor and weighing sensor were used to detect capacitance, temperature and bulk density of peanut kernels respectively. Capacitance was converted to frequency through the signal detection and conditioning circuit, and frequency was detected when the capacitance sensor was empty or full of peanut kernels samples. The difference frequency values were processed and calculated by the single chip microcomputer. Subsequently an equation of measured difference frequency, temperature and moisture content values was established by the oven-drying method, and the detection results were displayed on the liquid crystal display (LCD) screen and all detected data were saved in a memory card. To investigate the influence of moisture content, temperature and bulk density on difference frequency, the tests were conducted at 7 moisture content levels from 6.4% to 18.2% and 4 temperature levels from 10 to 40℃. The results indicated that there was a good linear relationship between moisture content and difference frequency, as the difference frequency values increased with the elevation of moisture content, temperature and bulk density. In the test, because peanut kernels fell into the capacitance sensor from fixed height, the bulk density was not changed, which caused little effect on measurement. Therefore a mathematical model of 3 parameters of moisture content, temperature and difference frequency was established based on the MATLAB 7.10.0 software by using a multi-variation binomial regression method. Linear model, pure quadratic model, interaction model and full quadratic model were compared and the results suggested that the full quadratic model described the relationship of moisture content, temperature and difference frequency more accurately. In the validation test, the results showed the absolute value of relative error measured by the moisture content detector was below 0.5%. Therefore the feasibility of detecting the moisture content of peanut kernels based on the capacitance method was verified, as well as the reliability of the multi-linear regression model. This investigation provides a useful tool for the rapid and nondestructive measurement of moisture content for peanut kernels.