A Brief Review on Plant Disease Detection Using Image Processing Techniques
DOI:
https://doi.org/10.26438/ijcse/v7i9.112114Keywords:
Disease detection, Productivity, Economic part, Image processing, SpotsAbstract
The crop cultivation plays a very important role within the agriculture. Presently, the loss of food is principally because of infected crops, that reflexively reduce the assembly rate, productivity per unit space and reduction in quality of economic part of the crops, as a result of the 70-80 per cent blackout in yield of crops is because of diseases caused by varied micro-organisms like bacterium, virus and fungi. The detection of unwellness on the plant could be a vital to stop loss of yield and also the quality of agricultural turn out. The symptoms will be ascertained on the components of the plants like leaf, stem, lesions, fruits and roots that area unit developed because of bound organic phenomenon and abiotic factors. The leaf shows the symptoms by modification in color, spots and gall like formation thereon. This identification or detection of the unwellness is completed by manual observation and infectious agent detection which may consume longer and should prove pricey. In agriculture analysis of automatic plant disease detection is crucial analysis topic because it could prove advantages in observant massive fields of crops, and therefore mechanically observe symptoms of unwellness as shortly as they seem on plant leaves. The digital image process could be a technique used for improvement of the image.
References
[1]. Gurjar, Ajay A., and Viraj A. Gulhane. "Disease detection on cotton leaves by eigenfeature regularization and extraction technique." International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE) 1, no. 1 (2012): 1.
[2]. Bhong, Vijay S., and B. V. Pawar. "Study and Analysis of Cotton Leaf Disease Detection Using Image Processing." International Journal of Advanced Research in Science, Engineering and Technology 3, no. 2 (2016).
[3]. Dandawate, Yogesh, and Radha Kokare. "An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective." In 2015 International conference on advances in computing, communications and informatics (ICACCI), pp. 794-799. IEEE, 2015.
[4]. Ying, Geng, Li Miao, Yuan Yuan, and Hu Zelin. "A study on the method of image pre-processing for recognition of crop diseases." In 2009 International Conference on Advanced Computer Control, pp. 202-206. IEEE, 2009.
[5]. Ramakrishnan, M. "Groundnut leaf disease detection and classification by using back probagation algorithm." In 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 0964-0968. IEEE, 2015.
[6]. Sannakki, Sanjeev S., Vijay S. Rajpurohit, V. B. Nargund, and Pallavi Kulkarni. "Diagnosis and classification of grape leaf diseases using neural networks." In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1-5. IEEE, 2013.
[7]. Phadikar, Santanu, and Jaya Sil. "Rice disease identification using pattern recognition techniques." In 2008 11th International Conference on Computer and Information Technology, pp. 420-423. IEEE, 2008.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
