温室黄瓜光谱成像识别方法研究

new spectrum selected method was developed to analyze and verify sensitive bands of cucumber based on statistical variance analysis and artificial neural network. Then the selected spectrum composition was used as reference basis for spectral image acquisition in greenhouse cucumber recognition, and the results of image processing indicated that the images within sensitive bands were captured to cope with the similar-color segmentation problem under complex environment effectively. By comparing the spectral reflectance difference of cucumber plant (fruit, leaf and flower) from visible to infrared region (350 nm-1200 nm), sensitive bands of fruit information were obtained by statistical variance analysis. After that, principal component analysis compressed the sensitive bands into several new variables that were the linear combination of original spectral data. In order to set up the three layer verifying model of back propagation artificial neural network (BP-ANN), the first four principal components were applied as inputs of BP-ANN, and the values of type of cucumber organs were applied as outputs. In this model, the trained network arrives at a 100% identification rate for 120 training samples as well as a 95% identification rate for 40 test samples. It proved that the selected spectrum composition could better reflect the characteristic difference of cucumber organs, which provided theoretical evidence for research and application of spectral imaging technique.