王若琳, 王栋, 任小林, 马惠玲. 基于电学特征的苹果水心病无损检测[J]. 农业工程学报, 2018, 34(5): 129-136. DOI: 10.11975/j.issn.1002-6819.2018.05.017
    引用本文: 王若琳, 王栋, 任小林, 马惠玲. 基于电学特征的苹果水心病无损检测[J]. 农业工程学报, 2018, 34(5): 129-136. DOI: 10.11975/j.issn.1002-6819.2018.05.017
    Wang Ruolin, Wang Dong, Ren Xiaolin, Ma Huiling. Nondestructive detection of apple watercore disease based on electric features[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(5): 129-136. DOI: 10.11975/j.issn.1002-6819.2018.05.017
    Citation: Wang Ruolin, Wang Dong, Ren Xiaolin, Ma Huiling. Nondestructive detection of apple watercore disease based on electric features[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(5): 129-136. DOI: 10.11975/j.issn.1002-6819.2018.05.017

    基于电学特征的苹果水心病无损检测

    Nondestructive detection of apple watercore disease based on electric features

    • 摘要: 为了探寻快速而准确的苹果水心病无损检测新方法,该文以'秦冠'水心病疑似病果和好果作为试材,逐果采集11个电学指标在100 Hz~3.98 MHz间13个频率点的特征值,然后切开并统计真实发病情况。利用主成分分析结合不同分类模型进行好果与病果判别分析,结果选取方差累积贡献率大于90%的主成分15个,Fisher判别、多层感知器人工神经网络(multi-layer perceptron,MLP)对好果和病果的判断正确率均随着主成分数的增加而增大,并分别在主成分数量达到前13、10时趋于稳定水平93.3%、95.4%。径向基人工神经网络(radical basis function,RBF)结合15个主成分判别的正确率75.1%。水心病引起介电损耗系数D、复阻抗相角deg、串联等效电容Cs和并联等效电容Cp及相对介电常数(ε')、损耗因子(ε")共6个参数在低频区(100~10 000 Hz)的观测值高于好果,是电学法能够对水心病果和好果进行'识别'的原因。同时发现,利用低频率下(100~25 100 Hz)损耗因子(ε")值结合MLP或RBF人工神经网络模型对水心病果和好果识别正确率均能达到100%,是一种简便而高效的苹果水心病无损检测方法,可为今后进一步研发苹果果实水心病在线无损检测仪器提供理论与技术依据。

       

      Abstract: Abstract: In order to find more cost-saving and efficient technology for non-destructive detection of watercore apple, recognizing the disease by following electric feature changes of the fruit was tested in this study. With the suspected watercore fruit and sound fruit of Malus pumila cv. Qinguan as material, we collected 143 feature data of 11 electric parameters at 13 frequency points from 100 Hz to 3.98 MHz fruit by fruit. Then each fruit was crosscut to tell and record whether watercore occurred in it. All the features data were analyzed by 3 steps. The first 2 steps were to screen differential features between sound and watercore apple, and then determine principal components (PCs) whose cumulative variance contribution rate reached over 90%. In the third step, different classification models were used to discriminate the sound and watercore fruit in combination with PCs obtained. The results showed that the incidence of watercore caused the increase in feature values of dielectric loss coefficient, complex impedance angle, series equivalent capacitance, parallel capacitance, relative dielectric constant, and loss factor at low frequency region (100-10000 Hz), a total of 36 differential feature values. These findings supported theoretically the possibility to discriminate sound and watercore apple based on differences in their electric features. Using principal component analysis, 15 and 7 PCs were extracted for original group of 143 features, and the group of 36 differential features, respectively. Accuracy rates of Fisher discrimination and multilayer perceptron (MLP) artificial neural network for the groups of 143 features and 36 differential features all elevated with the increase of PCs number, and reached a stable high level when PC number reached 13 and 10, respectively. Accuracy rates of Fisher discrimination and MLP for the group of 143 features using the former 13 PCs reached 93.8% and 95.4%, while for the group of 36 features using its former 7 PCs reached 91.7% and 93.8%, respectively. It indicated that the discrimination ability between sound and watercore apples was ascribed to mainly the 36 differential electric features. Discrimination by radial basis function (RBF) modeled by using 15 PCs reached an accuracy rate of 75.1% for the group of 143 features. Quality profiles of 2 kinds of apples differed in density, firmness, and soluble solids, which presented significantly higher level in watercore fruit (P<0.05), but titrate acids content was significantly lower. Physio-chemical characteristics changes resulted in the alternation of electric feature of watercore fruit and showed multiple-to-multiple correlation of cause-effect. Values of loss factor at low frequencies (100-25100 Hz) combined with MLP or RBF classifier all achieved accuracy rates of 100% on the recognization of either watercore or sound apple, which can be selected as the simple and effective method for apple watercore detection. The result can provide theoretical and technical support for the development of on-line equipment which can non-destructively detect the disease of apple watercore in the future.

       

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