摘要
水果采收期的成熟度决定了其最终食用品质,选择果实最佳的采收期是提高水果品质的关键措施之一。可见-近红外光谱技术以其快速、无损的检测特点,适合用于水果的成熟度、采收期检测。由于采收期果实品质差异大,传统化学计量学方法需进行复杂的光谱预处理,模型难以满足不同的季节、果园等需求。提出了一种基于全卷积神经网络(CNNs)的黄花梨采收期可见-近红外光谱(Vis-NIR)检测方法,利用卷积神经网络进行光谱特征信息提取,采用误差反向传播算法结合随机梯度下降法进行层与层之间的连接权重调节,输出多采收期的Logistic回归结果,实现了黄花梨采收期的端到端检测。设计了包含1个输入层、2个卷积层、1个池化层和1个Softmax输出层等5层的一维全卷积神经网络,采用交叉熵为损失函数,增加L2正则项以防止模型的过拟合,无光谱预处理,对比分析了此方法与PLSDA方法的建模结果。试验采集了两个年度黄花梨样品共450个,其中,第一年度的300个组成训练集,90个样本组成测试集1,第二年度的60个样本组成测试集2。实验结果表明,当测试集样品与训练集来自相同年份时,PLSDA和CNNs模型对测试样品集采收期正确识别率均为100%,当测试集样品来自不同年份时,测试集样品采收期的正确识别率分别降为41.67%和88.33%,相关系数、互信息计算模型的回归系数表明,CNNs模型充分利用了样品全波段信息。可见,CNNs方法通过迭代对卷积核进行优化,实现了更灵活的光谱预处理,可降低模型训练难度,所建模型有较好的可解释性和泛化能力,该方法对建立稳健的水果采收期可见-近红外光谱检测模型有一定的参考价值,有利于实现水果精细化的分期、分批采收。
The maturity of fruit at harvest time determines its final eating quality.Choosing the optimal harvest time of fruit is one of the key issues to improve fruit quality.Visible/near-infrared spectroscopy technology is suitable for fruit maturity and harvest time determination because of its rapid and non-destructive detection characteristics.Due to the large difference in fruit quality on the tree,traditional chemometric methods require complex spectral pretreatment,and the model is not suitable for different seasons,orchards,etc.In this paper,the discrimination model of Huanghua pear’s harvest time by full convolutional neural networks(CNNs)based on visible/near infrared spectroscopy(Vis/NIR)was proposed.The CNNs was used for spectral feature extraction,and the error backpropagation algorithm combined with the random gradient descent method was used to adjust the connection weights between layers,and output the Logistic regression of harvest time determination,which implemented the end-to-end discrimination of Huanghua pear’s harvest time,and the result was compared with the PLSDA method.The one-dimensional convolutional neural networks included one input layer,two convolution layers,a pooling layer and one Softmax output layer,using cross-entropy as the loss function,and the L2 regularization was used as the regular term to avoid overfitting,without preprocessing.A total of 450 samples were collected for two years.Three hundred samples in the first year constituted training set,90 samples constituted test set one,and 60 samples in the second year constituted test set two.The results have shown that when the test set and the training set were collected from the same year,correct discrimination rate of PLSDA and CNNs models was 100%,when the test was from different years,correct discrimination rate reduced to 41.67%and 88.33%,respectively.The correlation coefficient and the mutual information of the modes indicated that the CNN model could take advantage of full-spectrum information.Therefore,the CNNs method optimizes convolution kernels through iteration to achieve more flexible preprocessing,which can reduce the difficulty of model training.The established model has good ability of explanation and generalization.The proposed method could be applied in discrimination of fruit’s harvest time.
作者
刘辉军
魏超宇
韩文
姚燕
LIU Hui-jun;WEI Chao-yu;HAN Wen;YAO Yan(College of Metrological Technology and Engineering,China Jiliang University,Hangzhou 310018,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2020年第9期2932-2936,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(51606181)
浙江省自然科学基金项目(LY15E040001)资助。
关键词
黄花梨
可见-近红外光谱
卷积神经网络
采收期检测
Huanghua pear
Visible-near infrared spectroscopy
Convolutional neural networks
Determination of harvest time
