基于流形學習的葡萄葉片品種識別方法研究
發(fā)布時間：2018-08-13 10:42
【摘要】：隨著葡萄市場經(jīng)濟的發(fā)展,葡萄品種識別對于葡萄這種經(jīng)濟作物的科普和市場推廣都具有重要意義。在葡萄品種識別研究中,一般以葉片為識別研究對象,主要考慮到葉片相對于果實來說易于保存且可采摘時間長,研究過程中不需要其他學科的輔助實驗。但是葡萄葉片識別存在一個明顯的難點,這種同科屬類葉片顏色和形態(tài)結構差異小,使得識別研究中準確率不高。為解決這個問題,本論文展開了針對同屬類葡萄葉片識別的研究,提出一種基于流形學習的葡萄葉片品種識別方案。本研究采用了15個品種的450幅葡萄葉片作為實驗樣本,進行葡萄品種的分類識別實驗。重點研究葡萄葉片的特征提取和特征降維。在特征提取部分,分別采用人工設計特征的灰度共生矩陣、方向梯度直方圖、可變性部件模型和由卷積神經(jīng)網(wǎng)絡提取的深度學習特征作為葉片特征,分析特征數(shù)據(jù)性質(zhì)和表現(xiàn)能力。發(fā)現(xiàn)高維度的特征表示葡萄葉片的能力優(yōu)于低維度,但是高維特征數(shù)據(jù)量大,冗余性高,雖然能夠得到較好的識別結果,但效率較低。為了降低高維葡萄葉片特征數(shù)據(jù)在識別過程中的復雜度,提高其實用性與實驗效率,本文采用流形學習算法對提取的高維葡萄葉片特征進行降維,在保持識別精度的基礎上,提高算法的效率,使其具有實用性。在葡萄葉片特征降維研究中,分別應用了局部線性嵌入(LLE)、拉普拉斯特征映射(LE)、局部保持投影(LPP)、臨近保持嵌入(NPE)四種不同的算法進行特征降維,得到葡萄葉片在低維空間的特征表示,并對影響降維性能的重點參數(shù)進行了分析;在葡萄葉片識別過程中,對比分析不同分類器的分類效果,最后通過訓練支持向量機(SVM)分類模型,進行葉片分類識別。本論文通過實驗分析驗證了流形降維在葡萄葉片識別中的可行性和必要性,流形降維能夠有效保持數(shù)據(jù)在高維空間的內(nèi)部結構特征。降維后的特征,在提高識別速度的同時,相對于降維前,仍具有良好的葉片識別準確性。其中利用卷積神經(jīng)網(wǎng)絡進行特征提取結合流形學習算法對其進行降維,識別率最高可以達到90.33%,識別性能優(yōu)于降維前的性能,并且識別速度大幅提高,相較于不做特征降維的識別時間而言,時間縮短為原來的1/3。對于人工設計特征的降維,降維后的識別時間有明顯的改善,其中DPM特征維度降為原來的1/30時,識別用時縮短為原來的1/6。本文的研究為葡萄葉片的快速識別,提供了一種有效的方法。
[Abstract]:With the development of grape market economy, grape variety identification is of great significance to the popularization of science and marketing of this kind of cash crop. In the research of grape variety recognition, the leaf is generally regarded as the object of study, considering that the leaf is easy to be preserved and can be picked for a long time compared with the fruit, and there is no need for the auxiliary experiment of other disciplines in the course of the research. However, there is an obvious difficulty in the leaf recognition of grape. The difference of leaf color and morphological structure of the same family, genus and species is small, which makes the accuracy of the recognition research not high. In order to solve this problem, this paper studies the leaf recognition of the same genus grape, and proposes a new method of grape leaf variety recognition based on manifold learning. In this study, 450 leaves of 15 grape varieties were used as experimental samples to classify and identify grape varieties. The feature extraction and dimensionality reduction of grape leaves were studied. In the part of feature extraction, the grayscale co-occurrence matrix, directional gradient histogram, variable component model and depth learning feature extracted from convolutional neural network are used as leaf features, respectively. Analyze the nature and performance of the feature data. It was found that the high dimension feature indicates that the ability of grape leaves is superior to that of the low dimension, but the high dimensional feature has large data volume and high redundancy, which can obtain better recognition results, but its efficiency is low. In order to reduce the complexity of high dimensional grape leaf feature data in recognition process and improve its practicability and experimental efficiency, this paper adopts manifold learning algorithm to reduce the dimension of the extracted high dimensional grape leaf feature, on the basis of maintaining the recognition accuracy. Improve the efficiency of the algorithm and make it practical. In the study of grape leaf feature dimensionality reduction, four different algorithms of locally linear embedded (LLE), Laplacian feature map, (LE), local preserving projection, (LPP), near preserving embedding (NPE), were used to reduce the dimension of grape leaves. The characteristic representation of grape leaves in low-dimensional space was obtained, and the key parameters affecting the performance of reducing dimension were analyzed. In the process of grape leaf recognition, the classification effect of different classifiers was compared and analyzed. Finally, by training support vector machine (SVM) classification model, the blade classification recognition. In this paper, the feasibility and necessity of manifold dimensionality reduction in grape blade recognition are verified by experimental analysis. Manifold dimensionality reduction can effectively maintain the internal structural characteristics of data in high-dimensional space. The features after dimensionality reduction can improve the recognition speed and still have good accuracy of blade recognition compared with those before dimensionality reduction. Among them, convolution neural network is used for feature extraction and manifold learning algorithm to reduce the dimension, the recognition rate can reach 90.33, the recognition performance is better than that before dimensionality reduction, and the recognition speed is greatly improved. Compared with the recognition time without feature reduction, the time is shortened to one third of the original time. For the dimensionality reduction of artificial design features, the recognition time after dimensionality reduction is obviously improved. When the DPM feature dimension is reduced to 1 / 30 of the original, the recognition time is shortened to 1 / 6 of the original. The research in this paper provides an effective method for rapid recognition of grape leaves.
【學位授予單位】：西北農(nóng)林科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S663.1;TP391.41
本文編號：2180731
[Abstract]:With the development of grape market economy, grape variety identification is of great significance to the popularization of science and marketing of this kind of cash crop. In the research of grape variety recognition, the leaf is generally regarded as the object of study, considering that the leaf is easy to be preserved and can be picked for a long time compared with the fruit, and there is no need for the auxiliary experiment of other disciplines in the course of the research. However, there is an obvious difficulty in the leaf recognition of grape. The difference of leaf color and morphological structure of the same family, genus and species is small, which makes the accuracy of the recognition research not high. In order to solve this problem, this paper studies the leaf recognition of the same genus grape, and proposes a new method of grape leaf variety recognition based on manifold learning. In this study, 450 leaves of 15 grape varieties were used as experimental samples to classify and identify grape varieties. The feature extraction and dimensionality reduction of grape leaves were studied. In the part of feature extraction, the grayscale co-occurrence matrix, directional gradient histogram, variable component model and depth learning feature extracted from convolutional neural network are used as leaf features, respectively. Analyze the nature and performance of the feature data. It was found that the high dimension feature indicates that the ability of grape leaves is superior to that of the low dimension, but the high dimensional feature has large data volume and high redundancy, which can obtain better recognition results, but its efficiency is low. In order to reduce the complexity of high dimensional grape leaf feature data in recognition process and improve its practicability and experimental efficiency, this paper adopts manifold learning algorithm to reduce the dimension of the extracted high dimensional grape leaf feature, on the basis of maintaining the recognition accuracy. Improve the efficiency of the algorithm and make it practical. In the study of grape leaf feature dimensionality reduction, four different algorithms of locally linear embedded (LLE), Laplacian feature map, (LE), local preserving projection, (LPP), near preserving embedding (NPE), were used to reduce the dimension of grape leaves. The characteristic representation of grape leaves in low-dimensional space was obtained, and the key parameters affecting the performance of reducing dimension were analyzed. In the process of grape leaf recognition, the classification effect of different classifiers was compared and analyzed. Finally, by training support vector machine (SVM) classification model, the blade classification recognition. In this paper, the feasibility and necessity of manifold dimensionality reduction in grape blade recognition are verified by experimental analysis. Manifold dimensionality reduction can effectively maintain the internal structural characteristics of data in high-dimensional space. The features after dimensionality reduction can improve the recognition speed and still have good accuracy of blade recognition compared with those before dimensionality reduction. Among them, convolution neural network is used for feature extraction and manifold learning algorithm to reduce the dimension, the recognition rate can reach 90.33, the recognition performance is better than that before dimensionality reduction, and the recognition speed is greatly improved. Compared with the recognition time without feature reduction, the time is shortened to one third of the original time. For the dimensionality reduction of artificial design features, the recognition time after dimensionality reduction is obviously improved. When the DPM feature dimension is reduced to 1 / 30 of the original, the recognition time is shortened to 1 / 6 of the original. The research in this paper provides an effective method for rapid recognition of grape leaves.
【學位授予單位】：西北農(nóng)林科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S663.1;TP391.41
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