In order to realize accurate, rapid and nondestructive detection for moisture content (MC) of maize kernel and avoid effects of placement state (embryo up or down) on detection results, a novel detection method based on hyperspectral imaging and image processing techniques is proposed. The variable selection method is used to establish MC prediction model according to the placement state of maize kernel. The hyperspectral images including both front and reverse side of maize kernel are acquired, spectral data in centroid region is extracted, and competitive adaptive reweighted sampling algorithm is used for characteristic wavelength selection. And the prediction models including front and reverse side prediction model are built for MC prediction. The spectral curves in different parts of hyperspectral images are contrasted mutually to judge if the maize kernel appeared in the image is front side upward (embryo up) or not, and four wavebands (1104, 1304, 1454, 1751 nm) are selected for front and reverse side detection with band math. The MC of 45 validation set samples are detected with the proposed algorithm. Results show that the accuracy of front and reverse side detection is about 97.8%, 100%, respectively, the validation set correlation coefficient of front and reverse side are 0.969, 0.946, respectively, the root mean square error are 0.464%, 0.616%, respectively. This research establishes foundation for the MC detection of maize kernel with multi-spectral technique.