【摘要】：我國是一個(gè)人口眾多的農(nóng)業(yè)大國。棉花是我國重要的農(nóng)業(yè)作物,不僅與民生息息相關(guān),而且也是重要的戰(zhàn)略物資,影響著國家的經(jīng)濟(jì)建設(shè)與進(jìn)步。棉花在整個(gè)播種到收獲的過程里會遭受40多種病害侵襲,如果不能快速準(zhǔn)確地識別出棉花病害,就會影響對棉花的防治工作。因此能否快速診斷出棉花病害就顯得非常重要。本文首先闡述了研究的背景和意義,論述了國內(nèi)外研究現(xiàn)狀,指出本文研究對象為棉花黃萎病、角斑病、枯萎病三種病害,并說明了棉花病害識別研究的重要性。其次,采用數(shù)字圖像處理技術(shù)對棉花病害圖像預(yù)處理。概述了數(shù)字圖像處理的相關(guān)技術(shù)。采用中值濾波法對圖像噪聲信息進(jìn)行消除,降低這些噪聲的影響;利用加權(quán)平均的方法對圖像進(jìn)行灰度化處理;使用最大類間方差法分割圖像,并提出改進(jìn)措施,提升分割效果;經(jīng)分割處理后,利用形態(tài)學(xué)方法處理圖像以便后續(xù)操作。然后,基于RGB顏色模型和HIS顏色模型提取病害圖像的顏色特征,提取R、G、B和H、S、I共六個(gè)分量的平均灰度值作為顏色特征參數(shù);采用二維Gabor變換提取紋理特征,將圖像與5尺度8方向共40個(gè)濾波器進(jìn)行空間卷積運(yùn)算。對40個(gè)濾波圖像計(jì)算每幅圖的平均幅值,再以每個(gè)尺度上8個(gè)方向的平均值作為紋理特征,采取統(tǒng)計(jì)分析的方法選擇最終的輸入特征。最后,介紹了決策樹方法中的ID3算法和C4.5算法。對兩種算法進(jìn)行比較分析,最終采用C4.5決策樹分類算法對棉花三種病害進(jìn)行識別。借助weka數(shù)據(jù)挖掘平臺進(jìn)行實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果顯著,準(zhǔn)確率達(dá)到94.67%。本文采用基于C4.5的決策樹方法對棉花病害分類識別是一種新的嘗試。C4.5算法簡單、運(yùn)算快,可以處理離散型數(shù)據(jù),易于提取規(guī)則,生成的決策樹直觀、易懂。
[Abstract]:China is a large agricultural country with a large population. Cotton is an important agricultural crop in China, which is not only closely related to people's livelihood, but also an important strategic material, which affects the national economic construction and progress. Cotton will be affected by more than 40 kinds of diseases in the whole process of sowing and harvesting. If cotton diseases can not be identified quickly and accurately, the prevention and control of cotton will be affected. Therefore, it is very important to diagnose cotton diseases quickly. In this paper, the background and significance of the research are first expounded, and the current research situation at home and abroad is discussed. It is pointed out that the research objects in this paper are Verticillium wilt, Corner spot and Fusarium Wilt, and the importance of cotton disease identification is also explained. Secondly, the digital image processing technology is used to preprocess the cotton disease image. The related techniques of digital image processing are summarized. The median filtering method is used to eliminate the noise information of the image to reduce the influence of the noise, the weighted average method is used to grayscale the image, the maximum inter-class variance method is used to segment the image, and the improvement measures are put forward. The segmentation effect is improved. After segmentation, the image is processed by morphological method for subsequent operation. Then, based on the RGB color model and his color model, the color features of the disease image are extracted, and the average gray values of six components are extracted as the color feature parameters, and the two-dimensional Gabor transform is used to extract the texture features, and the two dimensional Gabor transform is used to extract the texture features. A total of 40 filters are used to perform spatial convolution operation. The average amplitude of each image is calculated for 40 filtered images, and the average value of 8 directions in each scale is taken as texture feature, and the final input feature is selected by statistical analysis. Finally, ID3 algorithm and C4.5 algorithm are introduced. Finally, C4.5 decision tree classification algorithm is used to identify three cotton diseases. With the help of weka data mining platform, the experimental results are remarkable and the accuracy is 94.67. In this paper, the decision tree method based on C4.5 is a new attempt to classify and identify cotton diseases. C4.5 algorithm is simple, fast, it can deal with discrete data, it is easy to extract rules, and the decision tree generated is intuitive and easy to understand.
【學(xué)位授予單位】：華北水利水電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S435.62;TP391.41

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