REVIEWING ROLE OF IMAGE ENHANCEMENT IN PADDY LEAF DISEASE DETECTION
Keywords:
Paddy leaf,, image processing, digital image, rotation,Abstract
In the field of agriculture, there is a requirement to detect and classify diseases from leaf images that are taken from plants. Finding the diseases of paddy leaf by making use of image processing mechanism would reduce the reliance on farmers in order to save the product related to agricultural activity. The research paper is finding and categorizing the disease in paddy leaf with the help of image processing. 2- Dimensional computerized pictures are those electronic pictures that have been generated on the basis of the computer. They are mainly generated out of twodimensional forms like 2-Dimensional geometric form, word, and electronic pictures, and using methods exclusive to them. It becomes possible to refer word to a field of computer science that includes certain methods, or it can refer to the forms it selves. These types of digital pictures are mostly used. These are initially built on conventional printing and drawing technology, such as scientific drawing, advertising, typography, cartography, and so on. In such implementations, a two-dimensional image/graphic is more than just a reflection of a real-world object; it is an individual artifact with added textual meaning. 2D models are considered for the reason that these models have additional strict control of pictures/graphics in comparison to three Dimensional computerized pictures. The approach of three Dimensional computerized pictures is very much analogous to camera work in comparison to style. In this article, we implemented scaling using BILINEAR Interpolation in order to compress images with minimal loss in image quality.
References
Ramesh, S., & Vydeki, D. (2020). Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. Information processing in agriculture, 7(2), 249-260.
Vo-Tong Xuan (2018). Rice production, agricultural research, and the environment. Routledge, In Vietnam's rural transformation (2018), pp. 185-200
X.E. Pantazi, D. Moshou, A.A. Tamouridou (2019). Automated leaf disease detection in different crop species through image features analysis and one class classifiers. Comput Electron Agric, 156 (2019), pp. 96-104
M.K. Elkazzaz, E.A. Salem, K.E. Ghoneim, M.M. El sharkawy, G.A. El-Kot, Z.A. Kalboush (2015). Integrated control of rice kernel smut disease using plant extracts and salicylic acid. Arch Phytopathol Plant Protect, 48 (8) (2015), pp. 664-675
M. Yusof, N.F. Mohd, M. Rosli, R. Othman, M.H.A A. Mohamed (2018). M-DCocoa: Magriculture expert system for diagnosing cocoa plant diseases. Proc. International Conference on Soft Computing and Data Mining. 2018 (2018), pp. 363-371
Kim, Dae Young, A. Kadam, S. Shinde, R.G. Saratale, J. Patra, G. Ghodake (2018). Recent developments in nanotechnology transforming the agricultural sector: a transition replete with opportunities. J Sci Food Agric, 98 (3) (2018), pp. 849-864
Astonkar, R. Shweta, V.K. Shandilya (2018). Detection and Analysis of Plant Diseases Using Image Processing. Int Res J Eng Technol, 5 (4) (2018), pp. 3191-3193
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