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JACIII Vol.23 No.6 pp. 1004-1011
doi: 10.20965/jaciii.2019.p1004
(2019)

Paper:

A Novel Growth Evaluation System for Tobacco Planting Based on Image Classification

Yonghua Xiong*,**,† and Shuangqing Yu*,**

*School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

Corresponding author

Received:
January 24, 2019
Accepted:
June 25, 2019
Published:
November 20, 2019
Keywords:
tobacco, evaluation system, image processing, planting guidance
Abstract
A Novel Growth Evaluation System for Tobacco Planting Based on Image Classification

The system structure of a novel growth evaluation

A novel growth evaluation system for tobacco planting (GESTP) based on a B/S architecture is introduced in this paper. It mainly consists of three parts: a mobile application (mobile app), a browser terminal and a server terminal. The GESTP system is used to evaluate the growth of tobacco and give farmers planting guidance instead them having to rely on personal judgment. Once the photos of the tobacco leaf and plant are uploaded to the web server via the mobile app or the browser terminal, the application program of the server terminal is called to process the tobacco images with image processing algorithms. The results including the grade of the tobacco growth and planting guidance will be provided to the client within a 2-second timeframe, which greatly help farmers understand the growth of tobacco and take planting measures. The running result indicates that the GESTP system provides an effective and straightforward way to evaluate the growth of tobacco and provides cultivation guidance to tobacco farmers.

Cite this article as:
Y. Xiong and S. Yu, “A Novel Growth Evaluation System for Tobacco Planting Based on Image Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.6, pp. 1004-1011, 2019.
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References
  1. [1] D. Makoka, J. Drope, A. Appau et al., “Costs, revenues and profits: an economic analysis of smallholder tobacco farmer livelihoods in Malawi,” Tobacco Control, Vol.26, No.6. pp. 634-640, 2017.
  2. [2] J. de J. Rubio, “A method with neural networks for the classification of fruits and vegetables,” Soft Computing, Vol.21, No.23, pp. 7207-7220, 2017.
  3. [3] N. M. H. Hassan and A. A. Nashat, “New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques,” Multidimensional Systems and Signal Processing, Vol.30, No.2, pp. 571-589, 2019.
  4. [4] J. Bin, F. F. Ai, W. Fan et al., “A modified random forest approach to improve multi-class classification performance of tobacco leaf grades coupled with NIR spectroscopy,” RSC Advances, Vol.6, No.36, pp. 30353-30361, 2016.
  5. [5] A. Shibata, F. Dong, and K. Hirota, “Neural Network Structure Analysis Based on Hierarchical Force-Directed Graph Drawing for Multi-Task Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.2, pp. 225-231, 2015.
  6. [6] W. Li, “Image Classification Combined with Fusion Gaussian–Hermite Moments Feature and Improved Nonlinear SVM Classifier,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 875-882, 2018.
  7. [7] J. Wu, S. X. Yang, and F. Tian, “An adaptive neuro-fuzzy approach to bulk tobacco flue-curing control process,” Drying Technology, Vol.35, pp. 465-477, 2017.
  8. [8] J. Zhang, W. Liu, Y. Hou et al., “Sparse Representation Classification of Tobacco Leaves Using Near-Infrared Spectroscopy and a Deep Learning Algorithm,” Analytical Letters, Vol.51, No.7, pp. 1029-1038, 2017.
  9. [9] Y. Qiao and S. Zhang, “Near-Infrared Spectroscopy Technology for Soil Nutrients Detection Based on LS-SVM,” Proc. of the 5th Int. Conf. on Computer and Computing Technologies in Agriculture (CCTA2011), pp. 325-335, 2011.
  10. [10] S. Paulus, H. Schumann, H. Kuhlmann et al., “High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants,” Biosystems Engineering, Vol.121, pp. 1-11, 2016.
  11. [11] J. L. Lovell, D. L. B. Jupp, G. J. Newnham et al., “Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.66, No.1, pp. 46-55, 2011.
  12. [12] M. Tang, I. B. Ayed, D. Marin et al., “Secrets of GrabCut and Kernel K-Means,” IEEE Int. Conf. on Computer Vision, pp. 1555-1563, 2015.
  13. [13] H. Tang, R. Ni, Y. Zhao et al., “Median filtering detection of small-size image based on CNN,” J. of Visual Communication and Image Representation, Vol.51, pp. 162-168, 2018.
  14. [14] C. Guo, M. Xiao, M. Minkov et al., “Photonic crystal slab Laplace operator for image differentiation,” Optica, Vol.5, No.3, pp. 251-256, 2018.
  15. [15] G. O. Petersen, C. E. Leite, J. M. Chatkin et al., “Cotinine as a biomarker of tobacco exposure: development of a HPLC method and comparison of matrices,” J. of Separation Science, Vol.33, No.4-5, pp. 516-521, 2010.
  16. [16] D. Aleksovski, D. Dovžan, S. Džeroski et al., “A comparison of fuzzy identification methods on benchmark datasets,” IFAC-PapersOnLine, Vol.49, No.5, pp. 31-36, 2016.

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Last updated on Nov. 26, 2020