發(fā)布時間：2018-05-26 01:30

  本文選題：蘋果病害檢測 + 高光譜成像��； 參考：《沈陽農(nóng)業(yè)大學(xué)》2017年碩士論文

【摘要】：蘋果是我國水果市場上最常見的水果之一,但在其生長和存儲過程中易受病害影響,從而會造成大量的經(jīng)濟(jì)損失。因此病害果的前期分揀十分必要。目前蘋果病害的檢測主要以人工分揀為主,但其分揀難度較大、準(zhǔn)確性差、很難達(dá)到分級的一致性。因此本研究采用高光譜成像技術(shù)對蘋果的病害進(jìn)行快速、無損檢測,其成果對提高蘋果品質(zhì)檢測和分級水平具有重要意義。本研究主要研究內(nèi)容及成果:(1)本研究以北方大面積種植的寒富蘋果為研究對象,經(jīng)調(diào)研發(fā)現(xiàn)寒富蘋果常見的病害有炭疽病、苦痘病、褐斑病和黑腐病。為了提取少量的特征波長對蘋果病害進(jìn)行檢測,利用改進(jìn)流行距離法(IMD)、馬氏距離法(MD)和連續(xù)投影法(SPA)3種方法提取特征波長。通過對比發(fā)現(xiàn)本研究所提出的二次連續(xù)投影算法SPA2提取3個特征波長(681、867和942nm)對蘋果病害檢測效果最佳。(2)本研究采用正常蘋果和病害蘋果的感興趣區(qū)域的紋理特征或SPA2提取的3個特征波長光譜相對反射率的光譜特征作為特征向量,分別建立線性判別分析(LDA)、支持向量機(jī)(SVM)和BP神經(jīng)網(wǎng)絡(luò)(BP)模型檢測蘋果病害,得出SPA2-BP為蘋果病害的最佳檢測方法,訓(xùn)練集檢測正確率達(dá)到100%,驗證集檢測正確率為98%。試驗結(jié)果表明,利用少量的光譜信息采用BP神經(jīng)網(wǎng)絡(luò)可以有效地對蘋果病害進(jìn)行檢測,能正確檢測蘋果是否侵染了病害。(3)本研究通過提取的感興趣區(qū)域分割出圖像進(jìn)行紋理特征提取,及采用SPA2提取的3個有效波長的光譜相對反射率作為光譜特征形成三個不同的特征向量組合。利用這些特征向量組合構(gòu)建BP神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)模型對4種蘋果病害進(jìn)行識別,得出光譜特征結(jié)合紋理特征作為輸入矢量的SVM檢測模型對蘋果病害識別效果最佳。驗證集中正常果的檢測正確率為95%,炭疽病的檢測效果稍差為90%,苦痘病的檢測正確率為95%,褐斑病的檢測正確率95%,黑腐病的檢測正確率為95%。試驗結(jié)果表明,光譜特征結(jié)合紋理特征采用支持向量機(jī)建立識別模型可以有效地對蘋果病害進(jìn)行分類檢測,為構(gòu)建多光譜在線檢測水果品質(zhì)分級提供了理論依據(jù)。
[Abstract]:Apple is one of the most common fruits in Chinese fruit market, but it is easy to be affected by diseases in its growth and storage process, which will cause a lot of economic losses. Therefore, it is very necessary for the early stage sorting of the diseased fruit. At present, the main detection of apple diseases is manual sorting, but its sorting is difficult and accurate, so it is difficult to achieve the consistency of classification. Therefore, hyperspectral imaging technology is used to detect apple diseases quickly and nondestructive, and the results are of great significance to improve apple quality detection and grading level. The main contents and results of this study were as follows: (1) in this study, the common diseases of cold rich apple were anthracnose, bitter acne, brown spot and black rot, which were planted in a large area in the north of China. The common diseases of cold rich apple were anthracnose, bitter acne, brown spot and black rot. In order to extract a small amount of characteristic wavelengths for detection of apple diseases, the improved popular distance method (IMD), the Markov distance method (MDD) and the continuous projection method (spa) were used to extract the characteristic wavelengths. It is found that the quadratic continuous projection algorithm (SPA2) proposed in this study has the best effect on apple disease detection by extracting three feature wavelengths (681867 and 942nm).) the texture features of normal apple and diseased apple region of interest are used in this study. Or the spectral features of the relative reflectance of the three characteristic wavelengths extracted by SPA2 as feature vectors, The models of linear discriminant analysis (LDA), support vector machine (SVM) and BP neural network (BP) were established to detect apple diseases. The results showed that SPA2-BP was the best detection method for apple diseases. The correct rate of training set was 100 and the correct rate of verification set was 98. The experimental results show that BP neural network can be used to detect apple diseases effectively by using a small amount of spectral information. In this study, the region of interest was extracted and the image was segmented for texture feature extraction. The spectral relative reflectance of three effective wavelengths extracted by SPA2 is used as the spectral feature to form three different eigenvector combinations. BP neural network and support vector machine (SVM) model are used to identify four apple diseases. It is concluded that the SVM detection model with spectral features and texture features as input vectors is the best for apple disease recognition. The accuracy rate of testing normal fruit was 95%, that of anthracnose was 90%, that of bitter acne was 95%, that of brown spot was 95%, and that of black rot was 95%. The experimental results show that the recognition model based on support vector machine (SVM) combined with spectral features and texture features can effectively classify and detect apple diseases and provide a theoretical basis for the construction of multispectral on-line fruit quality classification.
【學(xué)位授予單位】：沈陽農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;S436.611
，

本文編號：1935420