Have a personal or library account? Click to login
Recognition and classification of Produce affected by identically looking Powdery Mildew disease Cover

Recognition and classification of Produce affected by identically looking Powdery Mildew disease

Open Access
|Aug 2014

References

  1. AL-HIARY, H. – BANI-AHMAD, S. – REYALAT, M. – BRAIK, M. – ALRAHAMNEH, Z. 2011. Fast and accurate detection and classification of plant diseases. In International Journal of Computer Applications, vol. 17, 2011. no. 1, pp. 31–38.
  2. BANDI, S.R. – VARADHARAJAN, A. – CHINNASAMY, A. 2013. Performance evaluation of various statistical classifiers in detecting the diseased citrus leaves. In International Journal of Engineering Science and Technology, vol. 5, 2013. no. 2, pp. 298–307.
  3. BARBEDO, J.C.A. 2013. Digital image processing techniques for detecting, quantifying and classifying plant diseases. In Springer Plus, 2013, no. 2, p. 660.
  4. BAUER, S.D. – FILIP, K. – WOLFGANG, F. 2011. The potential of automatic methods of classification to identify leaf diseases from multispectral images. In Precision Agriculture, vol. 12, 2011. no. 3, pp. 361–377.
  5. BOISSARD, P. – MARTIN, V. – MOISAN, S. 2008. A cognitive vision approach to early pest detection in greenhouse crops. In Computers and Electronics in Agriculture, vol. 62, 2008. no. 2, pp. 81–93.
  6. CUI, D. – ZHANG, Q. – LI, M. – HARTMAN, G.L. – ZHAO, Y. 2010. Image processing methods for quantitatively detecting soybean rust from multispectral images. In Biosystems Engineering, vol. 107, 2010. no. 3, pp. 186–193.
  7. DUBEY, S.R. – JALAL, A.S. 2012. Adapted approach for fruit disease identification using images. In International Journal of Computer Vision and Image Processing, vol. 2, 2012. no. 3, pp. 51–65.
  8. DUDA, R.O. – HART, P.E. – STORK, D.G. 2000. Pattern classification. Third edition. John Wiley & Sons.
  9. GONZALEZ, R.C. – WOODS, R.E – EDDINS, S.L. 2009. Digital image processing using MATLAB. Second edition. Addison-Wesley Publishing Company.
  10. GURU, D.S. – MALLIKARJUNA, A.B. – MANJUNATH, S. 2011. Segmentation and classification of tobacco seedling diseases. In COMPUTE, 11 Proceedings of the Fourth Annual ACM. Bangalore.10.1145/1980422.1980454
  11. HUANG, K.Y. 2007. Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. In Computers and Electronics in Agriculture, vol. 57, 2007. no. 1, pp. 3–11.
  12. KIM, D.G. – BURKS, T.F. – QIN, Z. – BULANON, D.M. 2009. Classification of grapefruit peel diseases using color texture feature analysis. In International Journal of Agricultural & Biological Engineering, vol. 2, 2009. no. 3, pp. 41–50.
  13. PATIL, J.K. – KUMAR, R. 2011. Advances in image processing for detection of plant diseases. In Journal of Advanced Bioinformatics Applications and Research, vol. 2, 2011. no. 2, pp. 135–141.
  14. PUJARI, J.D. – YAKKUNDIMATH, R. – BYADGI, A.S. 2013a. Reduced color and texture features based identification and classification of affected and normal fruits’ images. In International Journal of Agricultural and Food Science, vol. 3, 2013. no. 3, pp. 119–127.
  15. PUJARI, J.D. – YAKKUNDIMATH, R. – BYADGI, A.S. 2013b. Statistical methods for quantitatively detecting fungal disease from fruits’ images. In International Journal of Intelligent Systems and Applications in Engineering, vol. 1, 2013. no. 4, pp. 60–67.
  16. PYDIPATI, R. – BURKS, T.F. – LEE, W.S. 2006. Identification of citrus disease using color texture features and discriminate analysis. In Computers and Electronics in Agriculture, vol. 52, 2006. pp. 49–59.
  17. RUMPF, T. – MAHLEIN, A.K. – STEINER, U. – OERKE, E.C. – DEHNE,
  18. H.W. – PLUMER, H. 2010. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. In Computers and Electronics in Agriculture, vol. 74, 2010. no. 1, pp. 91–99.
  19. SANKARAN, S. – MISHRA, A. – EHSANI, R. – DAVIS, C. 2010. A review of advanced techniques for detecting plant diseases. In Computers and Electronics in Agriculture, vol. 72, 2010. no. 1, pp. 1–13.
  20. SONKA, M. – HLAVAC, V. – BOYLE, R. 2008. Digital image processing and computer vision. Third edition. Cengage Learning.
  21. YAO, Q. – GUAN, Z. – ZHOU, Y. – TANG, J. – HU, Y. – YANG, B. 2009. Application of support vector machine for detecting rice diseases using shape and color texture features. In International Conference on Engineering Computation.10.1109/ICEC.2009.73
Language: English
Page range: 29 - 34
Published on: Aug 15, 2014
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2014 Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi, published by Slovak University of Agriculture in Nitra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.