As a food consumed worldwide,ginger is often sulfur-fumigated.Sulfur-fumigated ginger is harmful to health.However,traditional methods to detect sulfur-fumigated ginger are expensive and unpractical for the general pu...As a food consumed worldwide,ginger is often sulfur-fumigated.Sulfur-fumigated ginger is harmful to health.However,traditional methods to detect sulfur-fumigated ginger are expensive and unpractical for the general public.In this paper,we present an efficient and convenient identification method based on image processing.First,rapid detection kits were employed to mark three levels of sulfur-fumigated gingers,and the RGB images of the gingers of each sulfur-fumigated level are collected.Second,the brightness and texture features were extracted from the images.Three machine learning methods,Support Vector Machine,Back Propagation Neural Network and Random Forest,were applied to establish prediction models.Third,the accuracy of each model was calculated and different weights were assigned for different models.Finally,models with different weights determined whether the ginger was sulfur-fumigated or non-sulfur-fumigated,and then the results were summarized to establish the final identification model.The experimental results show that the proposed method is robust.When the training set occupies 90%,the prediction accuracy is up to 100%.When the training set only occupies 10%,the accuracy remains high at 80%.Meanwhile,the proposed method is more competitive than other methods in terms of accuracy.展开更多
基金the National Key Research and Development Project of China(No.2020YFD1104100)the National Natural Science Foundation of China(Nos.82204770,62101268,82074580)+4 种基金the Youth Science Foundation of Jiangsu Province(No.BK20210696)the Future Network Scientific Research Fund Project(No.FNSRFP-2021-ZD-24)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.20KJB510021)China Agriculture Research System of MOF and MARA(No.CARS-21)Jiangsu Province 333 High-level Talents Training Project,and the‘Qing Lan Project'in colleges and universities in Jiangsu,China.
文摘As a food consumed worldwide,ginger is often sulfur-fumigated.Sulfur-fumigated ginger is harmful to health.However,traditional methods to detect sulfur-fumigated ginger are expensive and unpractical for the general public.In this paper,we present an efficient and convenient identification method based on image processing.First,rapid detection kits were employed to mark three levels of sulfur-fumigated gingers,and the RGB images of the gingers of each sulfur-fumigated level are collected.Second,the brightness and texture features were extracted from the images.Three machine learning methods,Support Vector Machine,Back Propagation Neural Network and Random Forest,were applied to establish prediction models.Third,the accuracy of each model was calculated and different weights were assigned for different models.Finally,models with different weights determined whether the ginger was sulfur-fumigated or non-sulfur-fumigated,and then the results were summarized to establish the final identification model.The experimental results show that the proposed method is robust.When the training set occupies 90%,the prediction accuracy is up to 100%.When the training set only occupies 10%,the accuracy remains high at 80%.Meanwhile,the proposed method is more competitive than other methods in terms of accuracy.
