Detection of Defective Green Coffee Beans Based on Machine Learning
To achieve nondestructive,rapid,and accurate detection of defective green coffee beans,improve the quality of green coffee beans and the economic benefits of coffee farmers,a multi model Stacking integrated defect detection method based on local binary mode(LBP)is proposed.The method adopts machine vision technology to extract the LBP feature vectors of raw coffee beans at two scales(8,1)and(16,2)and three operators(uniform operator,rotation invariant operator,rotation invariant uniform operator),and combines the LBP features at different scales under the same operator,and selects LightGBM,XGBoost,CatBoos and SVM as the base classifiers,and Logistics as the secondary learner for Stacking model integration.The results show that the accuracy and F1 value of using the(8,1)scale unified operator Stacking integrated model for detection are 91.9%and 92.3%,respectively,which are higher than detection models with other scales,operators,and different types of features.Compared with LightGBM,XGBoost,CatBoost,and SVM,The accuracy of the Stacking integrated detection model has been improved by 0.6%,1.7%,2.0%,and 1.2%respectively,resulting in better overall detection performance.
machine visionLBP featuredefective green coffee beansStacking integrationdetection model