高光谱图像结合卷积神经网络的马铃薯干腐病潜育期识别

Study on Hyperspectral Detection of Potato Dry Rot in Gley Stage Based on Convolutional Neural Network

马铃薯是世界第四大粮食作物,具有丰富的营养价值。但其在贮藏和运输过程中易被镰刀真菌侵染而产生干腐病,最终造成巨大资源浪费和经济损失,因此实现马铃薯干腐病的早期快速无损检测是必要的。在样品被病原菌侵染时,经历了健康—潜育期—轻度病害—重度病害的阶段,其中潜育期的样品难以识别,主要源于病害发生时间较短,表面未形成肉眼可见的病斑,与健康样品相似。为了实现马铃薯干腐病潜育期的识别,结合高光谱成像和深度学习展开马铃薯干腐病早期诊断研究。以健康和不同腐败程度马铃薯为实验对象,获取健康和不同病害等级的马铃薯高光谱图像。然后基于ENVI人工选取健康部位和不同腐败程度样品的病斑部位为感兴趣区域(ROI),并计算ROI的平均光谱值作为该样品的最终光谱信息。以光谱数据作为输入变量,病害等级作为输出变量,建立卷积神经网络(CNN)模型,并对其网络结构进行优化,对比分析不同模型的预测结果,筛选出最优网络层模型为Model_3_3。并基于此结构进行学习率的优化,得到Model_0.0001识别效果最好,其总体准确率、精度、灵敏度和特异性分别为99.68%、99.76%、98.82%、99.54%。为了进一步突显CNN应用于马铃薯干腐病潜育期识别的优势,建立了最小二乘支持向量机(LS-SVM)、随机森林(RF)、K-近邻法(KNN)和线性判别分析(LDA)模型。结果显示,四种常规算法模型的识别准确率分别为90.77%、92.30%、93.10%和92.34%,其中对潜育期样品识别率分别为91.00%、85.58%、94.18%和90.33%。对于总体准确率,CNN模型较几种常规方法提高了6.58%~8.91%;对于潜育期样品的识别,CNN模型较常规方法提高了5.55%~14.15%。研究表明,高光谱成像技术结合CNN可以有效实现马铃薯干腐病潜育期识别,为提高马铃薯病害早期诊断的智能化水平提供了参考方法。

Potato is the fourth largest food crop in the world and has rich nutritional value.However,it is easy to be infected by the sickle fungus during storage and transportation,resulting in a huge waste of resources and economic losses.Therefore,it is necessary to quickly and accurately realize the early nondestructive detection of potato dry rot.When pathogenic bacteria infected the samples,they experienced the stages of healthy-gley stage-mild disease-severe disease.The gley stage was difficult to identify,mainly because the disease occurred quickly and no visible disease spots were formed on the surface,similar to the healthy samples.In order to realize the recognition of the gley stage of potato dry rot,this study combined hyperspectral imaging technology and deep learning to carry out early diagnosis of potato dry rot.The hyperspectral images of healthy potatoes and potatoes with different spoilage grades were obtained.Based on the ENVI,healthy parts and spots of samples with different grades of corruption were selected as regions of interest(ROI),and the average spectral value of ROI was calculated as the final spectral information of the sample.The Convolutional Neural Network(CNN)model was established with the spectral data as the input variable and the disease grade as the output variable,and the network structure was optimized.The results of different models were compared and analyzed,and the optimal model was selected as Model_3_3.Based on the optimal structure,the learning rate was optimized,and the Model_0.0001 has the best recognition effect,and its overall accuracy,accuracy,sensitivity and specificity are 99.68%,99.76%,98.82%and 99.54%,respectively.In order to further highlight the advantages of CNN in gley stage identification of potato dry rot,LSSVM,RF,KNN and LDAmodels were established.The results showed that the accuracy of the four conventional algorithm models were 90.77%,92.30%,93.10%and 92.34%,respectively,and the recognition rate of gley samples was 91.00%,85.58%,94.18%and 90.33%,respectively.For overall accuracy,the CNN model improves by 6.58%~8.91%compared with other conventional methods.Compared with conventional methods,the CNN model improved the recognition of gley samples by 5.55%~14.15%.The results show that hyperspectral imaging combined with CNN can effectively recognize the gley stage of potato dry rot,which provides a reference method for improving the intelligence level of early diagnosis of potato disease.