[Recognition of producing areas of Angelicae Sinensis Radix based on structure-texture image decomposition]

The pharmacological effects of Angelicae Sinensis Radix from different producing areas are uneven. Accurate identification of its producing areas by computer vision and machine learning(CVML) is conducive to evaluating the quality of Angelicae Sinensis Radix. This paper collected the high-definition images of Angelicae Sinensis Radix from different producing areas using a digital camera to construct an image database, followed by the extraction of texture features based on the grayscale relationship of adjacent pixels in the image. Then a support vector machine(SVM)-based prediction model for predicting the producing areas of Angelicae Sinensis Radix was built. The experimental results showed that the prediction accuracy reached up to 98.49% under the conditions of the model training set occupying 80%, the test set occupying 20%, and the sampling radius(r) of adjacent pixels being 2. When the training set was set to 10%, the prediction accuracy was still over 93%. Among the three producing areas of Angelicae Sinensis Radix, Huzhu county, Qinghai province exhibited the highest error rate, while Heqing county, Yunnan province the lowest error rate. Angelicae Sinensis Radix from Minxian county, Gansu province and Huzhu county, Qinghai province were both wrongly attributed to Heqing county, Yunnan province, while most of those from Huzhu county, Qinghai province were misjudged as the samples produced in Minxian county, Gansu province. The method designed in this paper enabled the rapid and non-destructive prediction of the producing areas of Angelicae Sinensis Radix, boasting high accuracy and strong stability. There were definite morphological differences between Angelicae Sinensis Radix samples from Minxian county, Gansu province and those from Huzhu county, Qinghai province. The wrongly predicted samples from Minxian county, Gansu province and Huzhu city, Qinghai province shared similar morphological characteristics with those from Heqing county, Yunnan province. Most wrongly predicted samples from Heqing county, Yunnan province were similar to the ones from Minxian county, Gansu province in morphological characteristics.