本文選題：高光譜圖像 + 麥粒硬度��； 參考：《華北水利水電大學》2017年碩士論文

【摘要】：小麥作為我國的主糧,其品質(zhì)的好壞直接影響到我們?nèi)粘ｏ嬍成畹陌踩?且其籽粒硬度是評價谷物品質(zhì)的一個重要參數(shù)。因此,如何能快速、有效、準確地檢測麥粒硬度具有非常重要的研究意義。本文以溫麥6號、中麥895號和西農(nóng)979號3種不同硬度的品種為樣本,研究了基于近紅外高光譜圖像的不同硬度麥�？蓞^(qū)分的理論機理,提取了能夠表征麥粒硬度的有效區(qū)域,并對22類不同品種麥粒的近紅外高光譜圖像數(shù)據(jù)進行了預處理,建立了基于徑向基核函數(shù)極限學習機回歸分析技術的智能測定模型,實現(xiàn)了對小麥籽粒硬度的自動無損檢測。(1)基于PCA的麥粒硬度的高光譜圖像無損檢測機理研究利用高光譜圖像采集系統(tǒng)對22類品種的1540個麥粒進行圖像采集,并依據(jù)國標法分別測定不同品種的麥粒實際硬度值。通過圖像預處理提取出可有效表征麥粒硬度的感興趣區(qū)域ROI,并以麥粒ROI內(nèi)的平均光譜曲線作為該麥粒的近紅外特征光譜。經(jīng)預處理分析后將原始特征波長從871.6-1766.3nm范圍內(nèi)的256個減少到902.1-1699.6nm內(nèi)的232個有效波長。利用PCA法對麥粒的高光譜圖像進行分析,其中PC1、PC2和PC3的貢獻率之和超過99.15%,故選取前3個主成分即可有效表征麥粒硬度的原始圖像信息。依據(jù)不同硬度麥粒的像素點分布區(qū)域的密集程度可解釋小麥籽粒樣本間的化學差異,最終利用PC2和PC3組合的得分圖研究麥粒硬度分類的可行性。建立了基于偏最小二乘判別分析的分類驗證模型,正確識別率為100%,證實了基于PCA的麥粒硬度的高光譜圖像無損檢測機理研究的可行性。(2)基于人工蜂群優(yōu)化算法的小麥籽粒硬度的波長選擇針對麥粒近紅外高光譜三維數(shù)據(jù)的特征波段多、混合度大及信息冗余多等特點,利用人工蜂群優(yōu)化算法對特征波長進行優(yōu)化選擇。對于ABC算法中存在的早收斂、易陷入局部最優(yōu)及接近全局最優(yōu)解時搜索速度緩慢等缺點,提出了基于混沌搜索思想的人工蜂群優(yōu)化算法。結果表明:從902.1-1699.6nm波段范圍內(nèi)的232個波長中優(yōu)選出902.1-935.9nm、968.7-992.6nm、1042.7-1072.4nm等范圍內(nèi)的105個波長子集,波長數(shù)據(jù)量減少了54.7%。與ABC算法相比,CABC優(yōu)化算法的運行時間縮短了45.3%,MSE降低了0.042%,SCC增加了0.55%。(3)基于RBF-ELM的小麥籽粒硬度智能預測模型為解決ELM算法中人工設置參數(shù)帶來的系統(tǒng)不穩(wěn)定性問題,利用GSM自動確定RBF-ELM算法中參數(shù)的輸入,并基于RBF-ELM的回歸分析技術建立小麥籽粒硬度的智能測定模型。結果表明:RBF-ELM預測模型與ELM模型相比,運行速度差別不大,但在精度和相關系數(shù)上分別提高了3.31%和7.36%;且與SVR模型相比,在訓練和預測時間上均縮短了三個數(shù)量級,預測精度降低了0.53%,相關系數(shù)提高了0.96%。因此,利用基于RBF-ELM回歸分析技術的智能測定模型有效實現(xiàn)了麥粒硬度的自動無損檢測。
[Abstract]:As the main grain in China, the quality of wheat directly affects the safety of our daily diet, and its grain hardness is an important parameter to evaluate the grain quality.Therefore, it is very important to study how to detect wheat hardness quickly, effectively and accurately.In this paper, three varieties with different hardness, Wenmai 6, Zhongmai 895 and Xinong 979, were used as samples to study the theoretical mechanism of differentiating wheat grains with different hardness based on near infrared hyperspectral images, and to extract the effective regions which could characterize the hardness of wheat grains.The near infrared hyperspectral image data of 22 different varieties of wheat were preprocessed, and an intelligent measurement model based on radial basis function (RBF) learning machine regression analysis technique was established.The mechanism of hyperspectral image nondestructive testing of wheat grain hardness based on PCA was realized. The hyperspectral image acquisition system was used to collect the image of 1 540 wheat grains of 22 varieties.According to the national standard method, the actual hardness values of wheat grains of different varieties were determined.The region of interest (ROI), which can effectively characterize the hardness of wheat grains, was extracted by image preprocessing, and the average spectral curve in ROI was used as the near infrared characteristic spectrum of wheat grains.After pretreatment, the original characteristic wavelength was reduced from 256 in 871.6-1766.3nm range to 232 effective wavelengths in 902.1-1699.6nm.The hyperspectral images of wheat grains were analyzed by PCA method. The total contribution rate of PC1 + PC2 and PC3 was more than 99.150.Therefore, the original image information of wheat hardness could be effectively characterized by selecting the first three principal components.The chemical difference among wheat grain samples can be explained according to the density of pixel distribution area of wheat grains with different hardness. Finally, the feasibility of wheat hardness classification was studied by using the score chart of PC2 and PC3 combination.A classification verification model based on partial least squares discriminant analysis is established.The correct recognition rate is 100%, which confirms the feasibility of studying the mechanism of nondestructive detection of wheat grain hardness based on PCA.) the wavelength selection of wheat grain hardness based on artificial bee colony optimization algorithm is aimed at the near infrared hyperspectral triplet of wheat grain.There are many characteristic bands of dimensional data.Because of large mixing degree and much information redundancy, artificial bee colony optimization algorithm is used to optimize the selection of characteristic wavelengths.For the shortcomings of early convergence, easy to fall into local optimum and slow search speed when approaching the global optimal solution in ABC algorithm, an artificial bee colony optimization algorithm based on chaotic search idea is proposed.The results show that 105 subsets of the wavelength of 902.1-935.9nmhmhmnmnmnmnmnmhl9442.7-1072.4 nm are selected from the 232 wavelengths in the range of 902.1-1699.6nm band, and the amount of wavelength data is reduced by 54.7%.Compared with ABC algorithm, the running time of CABC-based optimization algorithm is shortened by 45.3MSE and 0.042%. SCC increases 0.55%.) the intelligent prediction model of wheat grain hardness based on RBF-ELM can solve the problem of system instability caused by manual setting parameters in ELM algorithm.The input of parameters in RBF-ELM algorithm was automatically determined by GSM, and an intelligent measurement model of wheat grain hardness was established based on RBF-ELM regression analysis technique.The results show that compared with the ELM model, the operation speed of the weight RBF-ELM model is not different from that of the ELM model, but the accuracy and correlation coefficient are increased by 3.31% and 7.36%, respectively, and the training and prediction time are shortened by three orders of magnitude compared with the SVR model.The prediction accuracy is reduced by 0.53, and the correlation coefficient is increased by 0.96.Therefore, an intelligent model based on RBF-ELM regression analysis was used to realize the automatic nondestructive testing of wheat grain hardness.
【學位授予單位】：華北水利水電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S512.1;TP391.41

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