关键词: 马铃薯, 薯皮粗糙度, 图像特征, 机器视觉, 支持向量机

Abstract:

【Objective】The classification analysis of potato tuber skin roughness could provide the non-destructive testing methods for tuber appearance quality traits, which would establish the theoretical and practical base for the objective evaluation of tuber quality and high-throughput screening varieties.【Method】Seventy-nine potato varieties (lines) were selected as materials, and the images of tuber skin with and without bud-eyes were taken by camera. The tuber skin images were preprocessed using MATLAB R2016a software. Eight materials were randomly selected to compare the effect of image graying, enhancement and denoising using the correlation function indicators. The image characteristic parameters, angular second moment (ASM), entropy (ENT), contrast (CON) and correlation (COR) were extracted using the gray level co-occurrence matrix (GLCM), and the suitable distance (d) of GLCM were determined. The differences in two types of tuber skin image feature parameters were compared, and the set of tuber skin image features with less difference was selected for statistical analysis and classification recognition. The support vector machine (SVM) and backpropagation neural network (BPNN) models were constructed for tuber skin roughness classification, and the evaluation indexes of model grading accuracy were accuracy, precision, recall and harmonic mean, respectively. 【Result】The texture structure of tuber skin image after grayscale processing using the weighted average method was clear, and the evaluation value of image clarity was 2.5698±0.5959, which was significantly higher than that of the mean method (1.8035±0.4856) and the maximum method (1.0535±0.4088). The gray scale range of tuber skin image after histogram equalization enhancement was expanded from 100-200 to 0-200, which made the gray distribution wider. The salt noise denoising effect of tuber skin images using the median filter under 3×3 sliding windows was obvious, and the peak signal-to-noise ratio (PSNR) was maximum (28.6250±3.9784 Bp), which was significantly higher than that under 3×3 and 5×5 windows. Two types of tuber skin image feature parameters extracted by GLCM (d=4) were significantly different, and the set of tuber skin image features (without bud-eyes) with less difference was selected for statistical analysis and classification recognition. The results indicated that the variation coefficient of these parameters was varied significantly. The variation coefficient of contrast was the largest (0.40), followed by the angular second moment (0.24) and correlation (0.23), and the variation coefficient of entropy was the smallest (0.18). Using the feature set as the input variable of tuber skin classification model, the overall classification performance of SVM was higher than BP neural network, and the accuracy reached 87.5%. Especially, the prediction accuracy and recognizability of SVM for smooth and heavy hemp skins was the highest. The accuracy reached 100%, the recall reached 85.7% and 100%, and the harmonic mean reached 100% and 92.3%, respectively. 【Conclusion】The combination of the image processing techniques presented in this study and the GLCM extracted texture feature parameters could effectively characterize potato tuber skin roughness variations. The tuber skin roughness grading based on machine vision could be achieved by constructing SVM classification model, and the accuracy reached 87.5%.

Key words: Solanum tuberosum, tuber skin roughness, image characteristic, machine vision, support vector machine