Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (1): 17-22.doi: 10.16180/j.cnki.issn1007-7820.2021.01.004

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

PEI Xiaofang1,2,HU Min3   

  1. 1. Binjiang College,Nanjing University of Information Science and Technology,Wuxi 214105,China
    2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China
    3. School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China
  • Received:2019-10-23 Online:2021-01-15 Published:2021-01-22
  • Supported by:
    National Natural Science Foundation of China(61601229);Binjiang College,Nanjing University of Information Science & Technology Research Team(2019BJYNG006)


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