Study of Pesticide Contaminated Navel Orange Recognition Using near Infrared Spectroscopy

Abstract

Based on Support Vector Machine (SVM) and genetic algorithm (GA), this paper intends to search for the characteristic spectral ranges and wavelengths of near infrared spectroscopy of navel oranges contaminated by different pesticides, and set up recognition models. The pesticides in the experiment were Lannate®L insecticide, fenvalerate and omethoate, and three different concentrations were given to each pesticide. Preparing ten groups of navel oranges, each group was sprayed with a different pesticide and the 10th group without pesticide spraying was used for comparison. Searching the whole spectral range through GA, 5 best spectral ranges (165 wavelengths) were obtained and the recognition rate reached 98.86%. Then based on the chosen spectral ranges, 85 feature wavelengths were extracted with continual GA-SVM optimization, and the recognition rate was 99.14%. Experiment results showed that the application of SVM combining with GA can not only improve recognition accuracy, but also simplify the model effectively