Image Classification of Crop Diseases and Pests Based on Deep Learning and Fuzzy System
| Autor: | Jing Xu, Tongke Fan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Contextual image classification
Computer science business.industry Deep learning 010401 analytical chemistry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Image segmentation Fuzzy control system Machine learning computer.software_genre 01 natural sciences Convolutional neural network 0104 chemical sciences Hardware and Architecture 020204 information systems Crop disease 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer Software |
| Zdroj: | International Journal of Data Warehousing and Mining. 16:34-47 |
| ISSN: | 1548-3932 1548-3924 |
| DOI: | 10.4018/ijdwm.2020040103 |
| Popis: | The automatic classification of crop disease images has important value. The classification algorithm based on manual feature extraction has some problems, such as the need for professional knowledge, is time-consuming and laborious, and has difficulty extracting high-quality features. In this article, the theory of the fuzzy system is discussed. The theory of the fuzzy system is applied to the pretreatment of blurred images. A local blurred image deblurring method based on depth learning is proposed. By training convolutional neural network models with different structures, the image of diseases and insect pests is segmented using normalized segmentation algorithms based on spectral graph theory, and the segmentation knot of leaf diseases is obtained. Finally, the optimal network structure is obtained by comparing the segmentation results with the traditional machine learning algorithm. Experiments show that the segmentation results of pests and diseases obtained by this algorithm have better robustness, generalization, and higher accuracy. |
| Databáze: | OpenAIRE |
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