Abstract: 29 黄文倩，陈立平，李江波，等. 基于高光谱成像的苹果轻微损伤检测有效波长选取J. 农业工程学报，2013，29(1)：272－277.Huang Wenqian, Chen liping, Li Jiangbo, et al. Effective wavelengths determination for detection of slight bruises on apples based on hyperspectral imagingJ. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2013, 29(1): 272－277. (in Chinese with English abstract)30 宋怡焕. 苹果果梗/花萼与缺陷的纹理特征识别方法D. 杭州：浙江大学，2012.Song Yihuan. Apple Stem/Calvx and Defect Identification bv Their Texture FeaturesD. Hang Zhou: Zhe Jiang University, 2012. (in Chinese with English abstract)Feature vectors determination for pest detection on apples based on hyperspectral imagingTian Youwen1, Cheng Yi1, Wang Xiaoqi2, Li Qingji1(1. College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China; 2. College of Plant protection, Shenyang Agricultural University, Shenyang 110866, China)Abstract：Insect pestilence is one of the main defects of the apple industry, which could be caused by pest entrance during apple tree growth stages. Insect pest detection in apples is important for an automatic apple quality inspection and sorting system. In this study, we intended to determine the feature vectors that can be used for nondestructive detection of apple fruit insect pests and utilized hyperspectral imaging technology to carry out an effective method for rapid, non-invasive detection of the intact apples and insect pests. There were 160 samples of 80 intact and 80 insect infected 'red Fuji' apples to be investigated from an apple planting demonstration garden in the Shenbei New Area in Shenyang city. A hyperspectral imaging collection system with the wavelength range of 400-1 000 nm was established to acquire the hyperspectral images of these apple samples. Via the analysis of spectral reflectance of apple pest parts and the normal region, there were obvious differences in spectral reflectance at the 646 nm wavelength. So, the image of the 646nm wavelength was named the feature image. Then, the feature image was manipulated by threshold segmentation, dilation, and erosion operation, to obtain a mask image. The mask image was used for image analysis to mask and carried on principal component analysis. The optimum PC1 image was chosen and handled by the maximum entropy threshold segmentation to extract the pest region. Later, a comparative analysis of the texture feature of the insect infested region and the normal region on apples of the PC1 image, a region of interest (ROI) with 80 pixels×65 pixels of the PC1 image of each sample, was obtained. The texture features of the gray level co-occurrence matrix (of energy, entropy, moment of inertia and correlation) in four directions, which were 0, 45, 90, and 135 deg, respectively, were extracted. In addition, the spectral relative reflectance of the apple surface pests and normal regions, whether it was visible or near infrared region, had obvious difference. So the two spectral features of the spectrum relative reflectivity at 646 and 824 nm wavelength were acquired, which had larger relative reflectance differences between the apple surface pests and normal regions in the visible region and near infrared region, respectively. Feature vector selection was one of the key steps in detecting apple insect pests. For faster and more accurate detection of the apple insect pests, in this study, the optimization and integration of the texture features and the spectral feature vectors was analyzed. Four feature vector groups were posed respectively as the input vector of the BP neural network. The validation set of 30 normal apples and 30 insect infested apples was detected by using the BP neural network. The recognition rate was the highest when there was a fusion of the texture features of energy, entropy, moment of inertia, the correlation of 0 deg direction, and the spectral features of relative spectral reflectance with two feature wavelengths of 646 and 824 nm. A recognition rate of the normal apples and insect infested apples was 100 percent. Besides, in this case, the speed of detection is the fastest, and the MSE error is the smallest. Results show that the obtained feature vectors based on hyperspectral imaging technology can identify insect infestation effectively and provide a reference for apples quality detection and grading system using multispectral imaging.