Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Study on the Theory and Practice of Data Visualization Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Development of a Recognition System for Alfalfa Leaf Diseases Based on Image Processing Technology

Authors : Feng Qin, Haiguang Wang

Published in: Computer and Computing Technologies in Agriculture X

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

To implement rapid identification and diagnosis of leaf diseases on alfalfa, an image-based recognition system was developed using the GUIDE platform under MATLAB software environment. An integrated segmentation method of K_median clustering algorithm and linear discriminant analysis was applied to implement the lesion image segmentation in this developed recognition system. A multinomial logistic regression model for disease recognition was built based on 21 color, shape and texture features selected by using correlation-based feature selection method. Using this system, disease image reading, image segmentation and lesion image recognition can be done. This system can be applied to conduct image recognition of four common kinds of leaf diseases on alfalfa including alfalfa Cercospora leaf spot, alfalfa rust, alfalfa common leaf spot and alfalfa Leptosphaerulina leaf spot. Some basis was provided for further development of image recognition system of various alfalfa diseases and for building a network-based automatic diagnosis system of alfalfa diseases.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Fruit Tree Image Registration Based on Improved FAST Algorithm
next chapter Design Optimization and Performance Evaluation of a Tillage Depth Precision Measurement System
Literature
1.
He, F., Han, D.M., Wan, L.Q., Li, X.L.: The nutrient situations in the major alfalfa producing areas of China. J. Plant Nutri. Fert. 20, 503–509 (2014). (in Chinese)
2.
Liu, A.P., Hou, T.J.: Pests and Their Control of Grassland Plants. China Agricultural Science and Technology Press, Beijing (2005). (in Chinese)
3.
Samac, D.A., Rhodes, L.H., Lamp, W.O.: Compendium of Alfalfa Diseases and Pests, 3rd edn. APS Press, St. Paul (2014)
4.
Li, Y.Z., Nan, Z.B.: The Methods of Diagnose, Investigation and Loss Evaluation for Forage Diseases. Phoenix Science Press, Nanjing (2015). (in Chinese)
5.
Pydipati, R., Burks, T.F., Lee, W.S.: Identification of citrus disease using color texture features and discriminant analysis. Comput. Electron. Agric. 52, 49–59 (2006)CrossRef
6.
Sankaran, S., Mishra, A., Ehsani, R., Davis, C.: A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72, 1–13 (2010)CrossRef
7.
Patil, J.K., Kumar, R.: Advances in image processing for detection of plant diseases. J. Adv. Bioinform. Res. 2, 135–141 (2011)
8.
Li, G.L., Ma, Z.H., Wang, H.G.: Image recognition of wheat stripe rust and wheat leaf rust based on support vector machine. J. China Agric. Univ. 17, 72–79 (2012). (in Chinese)
9.
10.
Xie, C.Y., Wu, D.K., Wang, C.Y., Li, Y.: Insect pest leaf detection system based on information fusion of image and spectrum. Trans. Chin. Soc. Agric. Mach. 44, 269–272 (2013). (in Chinese)
11.
Phadikar, S., Sil, J., Das, A.K.: Rice diseases classification using feature selection and rule generation techniques. Comput. Electron. Agric. 90, 76–85 (2013)CrossRef
12.
Barbedo, J.G.A.: Digital image processing techniques for detecting. Quantifying Classifying Plant Dis. 2, 660 (2013)
13.
Barbedo, J.G.A.: An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Dis. 98, 1709–1716 (2014)CrossRef
14.
Zhou, R., Kaneko, S., Tanaka, F., Kayamori, M., Shimizu, M.: Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition. Comput. Electron. Agric. 116, 65–79 (2015)CrossRef
15.
Tan, W.X., Zhao, C.J., Wu, H.R., Gao, R.H.: A deep learning network for recognizing fruit pathologic images based on flexible momentum. Trans. Chin. Soc. Agric. Mach. 46, 20–25 (2015). (in Chinese)
16.
Mutka, A.M., Bart, R.S.: Image-based phenotyping of plant disease symptoms. Front. Plant Sci. 5, 734 (2015)CrossRef
17.
Barbedo, J.G.A.: A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst. Eng. 144, 52–60 (2016)CrossRef
18.
Ye, H.J., Lang, R., Liu, C.Q., Li, M.Z.: Recognition of cucumber downy mildew disease based on visual saliency map. Trans. Chin. Soc. Agric. Mach. 47, 270–274 (2016). (in Chinese)
19.
Mengistu, A.D., Alemayehu, D.M., Mengistu, S.G.: Ethiopian coffee plant diseases recognition based on imaging and machine learning techniques. Int. J. Database Theor. Appl. 9, 79–88 (2016)CrossRef
20.
Zheng, J., Liu, L.B.: Design and application of rice disease image recognition system based on Android. Comput. Eng. Sci. 37, 1366–1371 (2015). (in Chinese)
21.
Li, G.L., Ma, Z.H., Wang, H.G.: Development of a single-leaf disease severity automatic grading system based on image processing. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds.) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. LNEE, vol. 212, pp. 665–675. Springer, Heidelberg (2013). https://​doi.​org/​10.​1007/​978-3-642-34531-9_​70
22.
Atoum, Y., Afridi, M.J., Liu, X.M., McGrath, J.M., Hanson, L.E.: On developing and enhancing plant-level disease rating systems in real fields. Pattern Recogn. 53, 287–299 (2016)CrossRef
23.
Le Cessie, S., Van Houwelingen, J.C.: Ridge estimators in logistic regression. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 41, 191–201 (1992)MATH
24.
Otsu, N.A.: Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRef
25.
Qin, F., Liu, D.X., Sun, B.D., Ruan, L., Ma, Z.H., Wang, H.G.: Recognition of four different alfalfa leaf diseases based on image processing technology. J. China Agric. Univ. 21, 65–75 (2016). (in Chinese)
26.
Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Tech. 2, 37–63 (2011)
27.
Stricker, M.A., Orengo, M.: Similarity of color images. In: Proceedings of SPIE International Society Optics and Engineering, vol. 2420, pp. 381–392 (1995)
28.
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Publishing House of Electronics Industry, Beijing (2005). (in Chinese)
29.
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Publishing House of Electronics Industry, Beijing (2011). (in Chinese)
30.
Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato, Hamilton, New Zealand (1999)
31.
32.
Qin, F., Liu, D.X., Sun, B.D., Ruan, L., Ma, Z.H., Wang, H.G.: Image recognition of four different alfalfa leaf diseases based on deep learning and support vector machine. J. China Agric. Univ. 22, 123–133 (2017). (in Chinese)
33.
Liu, L.Q., Yuan, Z.B., Deng, J.Z., Li, M., Jin, J.: Construction of Tilletia diseases diagnosis system. Plant Quarantine 28, 10–15 (2014). (in Chinese)
34.
Aji, A.F., Munajat, Q., Pratama, A.P., Kalamullah, H., Setiyawan, J., Arymurthy, A.M.: Detection of palm oil leaf disease with image processing and neural network classification on mobile device. Int. J. Comput. Theor. Eng. 5, 528–532 (2013)CrossRef
35.
Xia, Y.Q., Wang, H.M., Zeng, S.: Plant leaf image disease detection based on Android. J. Zhengzhou Univ. Light Ind. (Nat. Sci. Ed.) 29, 71–74 (2014). (in Chinese)
36.
Qu, Y., Tao, B., Wang, Z.J., Wang, S.T.: Design of apple leaf disease recognition system based on Android. J. Agric. Univ. Hebei 38, 102–106 (2015). (in Chinese)
Metadata
Title
Development of a Recognition System for Alfalfa Leaf Diseases Based on Image Processing Technology
Authors
Feng Qin
Haiguang Wang
Copyright Year
2019
Book
Computer and Computing Technologies in Agriculture X

Print ISBN: 978-3-030-06154-8

Electronic ISBN: 978-3-030-06155-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-06155-5_22

Premium Partner

    Image Credits