基于深度集成学习的青梅品级智能反馈认知方法

Intelligent feedback cognition of greengage grade based on deep ensemble learning

针对传统机器判定水果品级的开环认知模式存在特征空间和分类准则一旦建立不再更新的缺陷,模仿人由整体到局部反复推敲比对的思维信息交互认知模式,探索了一种具有认知结果熵测度指标约束的青梅品质智能反馈认知方法。首先,在有限论域不确定条件下从信息论角度建立具有信息完备性评价指标的非结构化多层面动态特征表征的青梅品级认知智能决策信息系统模型。其次,基于架构自适应的卷积神经网络(adaptive structure convolutional neural networks,ASCNNs)和集成随机权向量函数连接网络分类器(random vector functional-link net,RVFL),建立青梅图像由整体到局部有明确品级特征表征映射关系的特征空间数据结构与分类准则。再次,基于广义误差和广义熵理论,建立青梅图像认知结果的熵函数形式测度评价指标。最后,建立基于不确定过程认知结果性能测度指标约束的动态反馈认知智能运行机制。针对1 008幅青梅图像的平均识别率为98.15%,表明该文方法有效地增强了特征空间的泛化能力以及分类器的鲁棒性。该研究可为基于可见光的青梅品级快速准确机器认知提供参考。

Fruit planting area and yield in China have reached the top level in the world.However,the lower processing level of the subsequent commercialization after fruit harvest is becoming one of the main factors to restrict the promotion of the added value and the international market competitiveness for the domestic fruit.Therefore,realizing the automatic classification of fruit grade has become an essential precondition of the modernization for the fruit industry in China.For the automatic classification method of fruit grade based on visible light technology,the working strength is considerably heavy and the cognitive effect is difficult to be satisfied,due to the susceptibility to the subjective factors for the man-made screening mode,such as experience.The corresponding machine screening mode based on the computer vision technology is susceptible to the drawbacks of the objective factors,such as traditional cognitive methods,and the classification result is also hence difficult to achieve satisfied effect.When the feature space and classification criteria are established once,they are un-updated,and are summarized as an open-loop fruit grade cognition mode for traditional machine judgment.Aiming at the defects,a greengage grade intelligent feedback cognitive method with cognitive result entropy measurement index constraint is explored,which imitates the human cognitive process with repeated comparison and inference from global to local.Firstly,under uncertain conditions and finite domain,from the information theory point of view,the greengage grade intelligent decision information system model is established by the representation of unstructured multi-level dynamic features with information completeness evaluation index.Secondly,the feature space data structure and classification criterion of greengage images with clear grade and feature mapping relationship are established based on adaptive structure-based convolutional neural networks and ensemble random vector functional-link net classifiers from global to local.Thirdly,based on the generalized error and generalized entropy theories,the entropy measurement evaluation index is established for the greengage image cognitive results.Finally,the intelligent operation mechanism of dynamic feedback cognition is established based on the measurement index constraint of uncertain process cognitive result.The average recognition accuracy of1008greengage images for our proposed method is98.15%.Such performance is7.9%higher than the algorithm based on Gabor wavelet combined with principal component analysis and support vector machine.The performance of the algorithm based on color completed local binary pattern combined with the nearest neighbor classifier is also lower than that of the proposed method,and the average recognition accuracy of it is just92.77%.Moreover,compared with the algorithm based on the wavelet descriptor combined with kernel principal component analysis and radial basis function neural network,the recognition accuracy of the proposed method is much better,although the running time is0.7s longer.The above mentioned conclusions indicate that the proposed method of adaptive structure convolutional neural networks and ensemble random vector functional-link net classifiers is suitable for the greengage grade machine screening recognition to replace the man-made screening mode,which can effectively enhance the generalization ability of the feature space and the robustness of the classifier.This study provides a reference for the rapid and accurate greengage grade machine cognition based on visible light.