Dissertation > Excellent graduate degree dissertation topics show

Research of Diagnosing Cucumber Diseases Based on Hyperspectral Imaging

Author: ZhangLin
Tutor: TianYouWen
School: Shenyang Agricultural University
Course: Applied Computer Technology
Keywords: Hyperspectral imaging technology Cucumber Diseases Principal component analysis Feature extraction Support vector machine
CLC: S436.421
Type: Master's thesis
Year: 2011
Downloads: 22
Quote: 0
Read: Download Dissertation

Abstract


The cucumber is the most important vegetable in China, which are planted widely and get the economic benefits significantly. With the development of greenhouse vegetables, the cucumber in the "Vegetable Basket Project" plays an important role especially. However, the cucumber are affected by various factors and diseases in the process of growth frequently, thus affecting the yield and quality of cucumber, or even crops. The use of pesticides receive good results sometimes, but leading to the pollution of the cucumbers and the environment. The effective technique to solve such problems is real-time monitoring on cucumber, early warning and spraying pesticides precisely, and the most important premise is to get the information of infected cucumbers rapidly and accurately. With the development of modern information science and technology, the technologies of image processing and recognition, spectral analysis techniques have been applicated in the diagnosis of plant diseases, and offer a powerful means of diagnosis for the realization of plants fast, accurately and nondestructively.Hyperspectral imaging technology as a new agricultural technology for crops disease detecting, discusses the feasibility of cucumber disease detection in this paper. Due to high-dimensional data of the hyperspectral sensing images brings difficulties for further processing, to solve this problem, this paper offers a principal component analysis method for dimensionality reduction to detect cucumber downy mildew. Firstly the collected hyperspectral image data is disposed through principal component analysis. Optimized the three wavelengths 634 nm,679 nm and 700 nm. After the feature images are pretreatmented, the features are extracted by gray statistics,color in the two areas. Three wavelengths will each feature image by pretreatment statistics from the gray scale, color made two feature extraction, twenty-two eigenvalues are extracted initially, and then the nine eigenvalues are optimized and selected by discriminant analysis, the most representative parameters are selected. Finally, support vector machines and neural network technology, the sample from the number of samples of cucumber diseases, feature images, the characteristic parameters and kernel function to classify and compare, identify the best parameters.The paper is carried out by image acquisitions,the principal component analysis,image preprocessing,feature extraction and pattern recognition methods,and made a better results.Paper conclusions will promote the technique of hyperspectral imaging and the application will have some references in other crop diseases of early diagnosis.

Related Dissertations

  1. Research on Automatic Detection Algorithm for Substructure Distress of Highway Pavement Based on SVM,U418.6
  2. ISAR Imaging Simulation of Space Targets and Target Recognition Based on ISAR Images,TN957.52
  3. Research on Autamatic Music Structrue Analysis,TN912.3
  4. Research on Feature Extraction and Classification of Pulse Waveform for Cholecystitis and Nephrotic Syndrome Diagnosis,TP391.41
  5. Application of Q-Learning in the Content-Based Image Retrieval Technology,TP391.41
  6. Research on Transductive Support Vector Machine and Its Application in Image Retrieval,TP391.41
  7. Research on Feature Extraction and Classification of Tongue Shape and Tooth-Marked Tongue in TCM Tongue Diagnosis,TP391.41
  8. Research on Visual Measurement for Spacecraft Rendezvous and Approach,TP391.41
  9. Research on the Image Real-Time Acquisition, Storage and Image Processing System,TP391.41
  10. Feature Extraction, Selection and Combination in Lipreading,TP391.41
  11. Multi-currency Notes Technology Research and Implementation,TP391.41
  12. The Research on Paper Currency Classification Method Based on Harr-Like Feature and Minimal Ball Including Samples,TP391.41
  13. Pavement Distress Recognition Based on Image,TP391.41
  14. Research on Visual Detection and Tracking of Mobile Robots,TP242.62
  15. Research on Fusion Algorithm of Hyper Spectral and High Spatial Resolution Remote Sensing Image,TP751
  16. Fault Diagnosis Method Based on Support Vector Machine,TP18
  17. Process Support Vector Machine and Its Application to Satellite Thermal Equilibrium Temperature Prediction,TP183
  18. Application of Improved Principal Component Analysis Algorithm in Course Construction,G642.4
  19. An Approach for Identifying a Plant Resistance Gene Based on the Random Forest,Q943
  20. Tobacco Diseases Auto-Recognition Research Based on Image Processing Technology,S435.72
  21. Research on Nondestructive Detection Technology for External Qualities of Papayas Based-on Vision,S667.9

CLC: > Agricultural Sciences > Plant Protection > Pest and Disease Control > Horticultural Crops Pest and Disease Control > Vegetable pests > Melons, pests and diseases > Cucumber pests and diseases
© 2012 www.DissertationTopic.Net  Mobile