The economic impact of crop diseases caused by environmental pollution can be complex and go beyond their immediate effect on directly affected agricultural producers.Some of the possible effects are food insecurity,health risk,environmental degradation,increased financial costs,reduced yield of crop food production,and increased production costs.Automatic detection and classification of crop diseases at an early age could help the producers to take corresponding prevention measures in time and ... 

【文章來源】：浙江理工大學(xué)浙江省

【文章頁數(shù)】：92 頁

【學(xué)位級別】：碩士

【文章目錄】：
Abstract
List of Abbreviations
Chapter 1 Introduction general
    1.1. Background
    1.2. Literature Reviews
    1.3. Research content and technical process
        1.3.1 Research content
        1.3.2 Technical processes
    1.4. Hardware and Software
        1.4.1 Hardware
        1.4.2 Software
    1.5. Chapter Arrangement
Chapter 2: Theoretical Background of Image classification
    2.1. Introduction
    2.2. Preprocessing and Segmentation based techniques
        2.2.1 Preprocessing
            2.2.1.1 Color space transformation
        2.2.2 Segmentation
            2.2.2.1 K-means clustering
            2.2.2.2 Unsupervised Fuzzy Clustering
    2.3. Introduction of Machine Learning
        2.3.1 SVM Algorithm for image classification
    2.4. Introduction of Deep Learning Methods
        2.4.1 Convolutional Neural Network
            2.4.1.1 Convolutional Neural Networks Architecture
        2.4.2 Parameter setting: CNN
            2.4.2.1 Number of filters
            2.4.2.2 Kerne/Filter size
            2.4.2.3 Max-Pooling Parameter
        2.4.3 Regularization methods
            2.4.3.1 Dropout regularization
            2.4.3.2 DropConnect
            2.4.3.3 Network size
        2.4.4 VGG16
    2.5. Chapter summary
Chapter 3: Segmented leaf spot diseases recognition using improved Convolutional NeuralNetworks
    3.1. Materials and methods
        3.1.1 Datasets
        3.1.2 Proposed Convolutional Neural Network model and Experimental process
            3.1.2.1 Proposed Convolutional Neural Network model
            3.1.2.2 Experimental process
    3.2. Results and Discussion
        3.2.1 Performance of the proposed model
        3.2.2 Recognition performance
        3.2.3 Performance evaluation
    3.3. Chapter summary
Chapter 4: leaf crop diseases recognition based on LAB color space and ConvolutionalNeural Networks
    4.1. Materials and methods
        4.1.1 Datasets and Preprocessing
        4.1.2 Proposed Convolutional Neural Networks model and Experimental process
    4.2. Results and Discussion
        4.2.1 Per class recognition results
        4.2.2 Performance evaluation
    4.3. Chapter summary
Chapter 5: leaf diseases recognition based on Encoder-Classification Network
    5.1. Materials and methods
        5.1.1 Datasets
        5.1.2 Proposed Convolutional Neural Network model "E-CN"
    5.2. Results and Discussion
        5.2.1 Performance of the proposed CNN model
        5.2.2 Performance evaluation
    5.3. Chapter summary
Chapter 6 Conclusion and future work
    6.1. Conclusion
    6.2. Future work
References
Acknowledgements
Research and Publication



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