پشتیبانی تصمیم گیری از پردازش تصویر هوشمند زمین های کشاورزی مبتنی بر درختان چند استنتاج

In order to meet the basic need of farmland image processing for non-professionals, to realize intelligent selection of image processing steps and common methods which were classified and analyzed based on knowledge description and inference of intelligent decision in this paper. According to the image features and requirements of users, the related rules were designed and then the color spaces, graying methods, de-noising methods, segmentation methods and morphology post-processing methods multi-inference trees were built. A manual evaluation method was adopted to evaluate the artificial and intelligent processing results. Therefore, the selection of image processing steps and methods can be made through these trees, and decision support of intelligent image processing can be realized. The experimental results show that the evaluation ratio of intelligent processing that more than 80 points is up to 83.0%, the average ratio is 75.9%, and the average processing time is about 0.23 s per image, which can greatly reduce the workload and blindness of methods selection and achieve higher level of comprehensiveness and operational efficiency.