ROLE OF IMAGE ENHANCEMENT IN PADDY LEAF DISEASE DETECTION : A STUDY
Keywords:
Paddy leaf, image processingAbstract
The detection and classification of illnesses using plant leaf photographs is required in the sector of agriculture. Finding the illnesses of paddy leaf using an image processing system will lessen the dependency on farmers in order to safeguard agricultural products. Using image processing, the study article identifies and categorises illness in paddy leaf. 2- Dimensional computerised photos are electronic images that have been made using a computer. They are mostly created utilising two-dimensional forms such as 2-Dimensional geometric shape, word, and electronic graphics, as well as ways unique to them. It is possible to use the term to refer to a branch of computer science that involves specific methodologies, or it may refer to the forms themselves. These are the most common forms of digital photographs. Initially, they are based on traditional printing and drawing technologies, such as scientific drawing, advertising, typography, mapping, and so forth
References
B.H. Prajapati, J.P. Shah, V.K. Dabhi (2017). Detection and classification of rice plant diseases. Intell Decis Technol, 11 (3) (2017), pp. 357-373
.G. Barbedo, L.V. Arnal, T.T.S. Koenigkan (2016). Identifying multiple plant diseases using digital image processing. Biosyst Eng, 147 (2016), pp. 104-116
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, D. Stefanovic (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci (2016), pp. 1-11
P. Mohanty, D.P. Sharada, M.S. Hughes (2016). Using deep learning for image-based plant disease detection. Front Plant Sci, 7 (1419) (2016), pp. 1-10
A.-K. Mahlein (2016). Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis, 100 (2) (2016), pp. 241-251
F. Pinki, N. Tazmim, S.M.M Islam Khatun (2017). Content based paddy leaf disease recognition and remedy prediction using support vector machine. In: Proc. In Computer and Information Technology (ICCIT), 20th International Conference (2017), pp. 1-5
Dudgeon, D.E. & R.M. Mersereau, Multidimensional Digital Signal Processing. 1984, Englewood Cliffs, New Jersey: Prentice-Hall.
Castleman, K.R., Digital Image/graphic Processing. Second ed. 1996, Englewood Cliffs, New Jersey:
Oppenheim, A.V., A.S. Willsky, & I.T. Young, Systems & Signals. 1983, Englewood
Papoulis, A., Systems & Transforms with Applications in Optics. 1968, New York:
Russ, J.C., Image/graphic Processing Handbook. Second ed. 1995, Boca Raton, Florida: CRC
Giardina, C.R. & E.R. Dougherty, Morphological Methods in Image/graphic & Signal Processing. 1988, Englewood Cliffs, New Jersey: Prentice-Hall. 321.
Gonzalez, R.C. & R.E. Woods, Digital Image/graphic Processing. 1992, Reading, Masachusetts:
Goodman, J.W., Introduction to Fourier Optics. McGraw-Hill Physical & Quantum
Electronics Series. 1968, New York: McGraw-Hill. 287.
Heijmans, H.J.A.M., Morphological Image/graphic Operators. Advances in Electronics & Electron Physics. 1994, Boston: Academic Press.
Hunt, R.W.G., Reproduction of Colour in Photography, Printing & Television,. Fourth ed. 1987, Tolworth, England: Fountain Press.
Freeman, H., Boundary encoding & processing, in Picture Processing & Psychopictorics, B.S. Lipkin & A. Rosenfeld, Editors. 1970, Academic Press: New York. p. 241-266.
Stockham, T.G., Image/graphic Processing in Context of a Visual Model. Proc. IEEE, 1972. 60:
Murch, G.M., Visual & Auditory Perception. 1973, New York: Bobbs-Merrill Company,
Frisby, J.P., Seeing: Illusion, Brain & Mind. 1980, Oxford, England: Oxford University
