Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging
Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging
复制标题
通过激光后向散射成像分析基于纹理的特征以预测园艺产品的机械性能
DOI：
10.1016/j.compag.2013.07.011
复制
发表时间：
2013
影响因子：
8.3
通讯作者：
M. Zude
中科院分区：
文献类型：
--
作者：
K. Mollazade;M. Omid;F. Tab;Y. R. Kalaj;S. Mohtasebi;M. Zude
光后向散射成像技术是一种无损检测园艺产品质量的先进技术。由于这种技术的新奇，用于处理这种类型的图像的开发算法处于初步阶段。本研究探讨了基于纹理的分析和空间域分析系数的可行性，以开发更好的模型来预测园艺产品的机械性能（果肉硬度或弹性模量）。使用捕获660 nm的后向散射成像装置获取苹果、李子、番茄和蘑菇的图像。利用可变阈值技术对图像的后向散射区域进行分割，然后对分割后的区域进行纹理分析和空间域分析，以提取多个特征。自适应神经模糊推理系统模型开发的坚固性或弹性预测，使用个别类型的特征集和它们的组合作为输入的预测模型适用于实时应用。结果表明，将图像纹理分析和空域技术相结合，可有效提高后向散射成像系统在园艺产品力学性能预测中的性能。预测阶段的相关系数最大值分别为0.887、0.790、0.919和0.896。
Light backscattering imaging is an advanced technology applicable as a non-destructive technique for monitoring quality of horticultural products. Because of novelty of this technique, developed algorithms for processing this type of images are in preliminary stage. The present study investigates the feasibility of texture-based analysis and coefficients from space-domain analysis to develop better models for predicting mechanical properties (fruit flesh firmness or elastic modulus) of horticultural products. Images of apple, plum, tomato, and mushroom were acquired using a backscattering imaging setup capturing 660 nm. After segmenting the backscattering regions of images by variable thresholding technique, they were subjected to texture analyses and space domain techniques in order to extract a number of features. Adaptive neuro-fuzzy inference system models were developed for firmness or elasticity prediction using individual types of feature sets and their combinations as input for prediction model applicable in real-time applications. Results showed that fusion of the selected feature sets of image texture analysis and space domain techniques provide an effective means for improving the performance of backscattering imaging systems in predicting mechanical properties of horticultural products. The maximum value of correlation coefficient in the prediction stage was achieved as 0.887, 0.790, 0.919, and 0.896 for apple, plum, tomato, and mushroom products, respectively.