杜鹃花各生长期识别与监测研究

doi:10.16180/j.cnki.issn1007-7820.2021.01.004

Abstract

Abstract:

In view of the problem that the traditional BoF algorithm lacks spatial information, an improved BoF algorithm is proposed and applied to the identification of various growth stages and the monitoring of pest and diseases of Rhododendron in this paper. The ordered spatial information is integrated into the LAB-based color features by this algorithm to form a new spatial color aggregation feature to replace the traditional color histogram, which effectively solved the problem of the small scale of color feature change. Besides, SURF features are extracted to replace the original SIFT features. The image classification is realized by a multi-class features, and the leaf features are further analyzed to quickly identify the growth period and disease of Rhododendron plants. Simulation results show that the recognition rate of the improved BoF model of the LAB-based color aggregation vector is 90.6%. Compared with the image classification method of the traditional histogram, the image retrieval speed is increased by 3 times, making it easier to implement the feature fusion.

Key words: improved BoF algorithm, spatial color aggregation feature, SURF, LAB, multi-class feature learning, leaf features, feature fusion

CLC Number:

TP391.4

PEI Xiaofang, HU Min. Study on the Identification of Various Growth and Monitoring of Pest and Disease of Rhododendron[J].Electronic Science and Technology, 2021, 34(1): 17-22.

Figures/Tables 9

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References 18

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检测方法	特征点数/个	运行时间/s	识别率/%
BoF+SIFT	4 096	-	80.60%
BoF+SURF^[17]	8 192	5.853	91.00%
CNN^[18]	-	-	90.45%
本文方法	4 352	4.445	90.60%

Study on the Identification of Various Growth and Monitoring of Pest and Disease of Rhododendron

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