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IJSTR >> Volume 8 - Issue 12, December 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Detection And Classification Of Plant Pathology With Image Processing Technique

[Full Text]

 

AUTHOR(S)

Dr.D.Sivabalaselvamani, Mr.D.Selvakarthi, Mr.L.Rahunathan, Mr.P.Leninpugalhanthi

 

KEYWORDS

Plant disease detection, Fuzzy Clustering, image processing, SVM classification.

 

ABSTRACT

About 70% of the India economy relies upon horticulture. Because of plant illnesses and bug bothers, the harvest yield gets influenced harshly. It requires cautious determination and opportune dealing with to shield the harvests from overwhelming loses. Ranchers experience staggering issues in changing beginning with one contamination control course of action then onto the following. The examinations show that depending on unadulterated unaided eye perception of specialists to identify and order ailments can be tedious and costly, particularly in country territories and creating nations. The point of this task is to configuration, execute and assess a picture handling programming based answer for programmed location and arrangement of plant leaf ailments. Picture handling underpins the ranchers in the recognizable proof of infections at an early or starting stage and give helpful data to its control. In picture preparing, the work starts with catching the pictures. At that point the example matches contrast the data from this picture and the data of sound plant's data which was put away in the database. The various highlights are extricated and contrasted and the shading and surface of the leaf. In view of the example acknowledgment a leaf can be distinguished as solid or ailing. This paper shows an investigation on methodologies that use propelled picture taking care of frameworks to recognize, measure and mastermind plant contaminations from electronic pictures in the undeniable range. Despite the way that affliction signs can appear in any bit of the plant, just systems that examine unquestionable symptoms in leaves and stems were considered. The proposed strategy or isolated into three classes as per the targets as recognition, seriousness evaluation and arrangement. Every one of these classifications, thusly, is additionally characterized by the fundamental specialized arrangement.

 

REFERENCES

[1] Lee, I., Shaw, W., & Fan, X. (2009). Wireless multimedia sensor networks. London: Springer.
[2] Khirade, S. D., & Patil, A. B. (2015). Plant disease detection using image processing. In 2015 International conference on computing communication control and automation (ICCUBEA) (pp. 768–771). IEEE.
[3] Donoho, D. L. (2006). Compressed Sensing. IEEE Transactions on Information Theory, 52, 1289–1306.
[4] Candes, E. J. (2006). Compressive sampling. In Proceedings of the international congress of mathematicians, Madrid, Spain. European Mathematical Society.
[5] Sethi, P., & Sarangi, S. R. (2017). Internet of Things: Architectures, protocols, and applications. Journal of Electrical and Computer Engineering.
[6] Mat, I., Kassim, M. R. M., Harun, A. N., & Yusoff, I. M. (2016). IoT in Precision Agriculture applications using Wireless Moisture Sensor Network. In IEEE conference on open systems (ICOS) (pp. 24–29). IEEE.
[7] Rad, C. R., Hancu, O., Takacs, I. A., & Olteanu, G. (2015). Smart monitoring of potato crop: A cyber-physical system architecture model in the field of precision agriculture. Agriculture and Agricultural Science Procedia, 6, 73–79.
[8] Jones, A., Ali, U., & Egerstedt, M. (2016). Optimal pesticide scheduling in precision agriculture. In 2016 ACM/IEEE 7th international conference on cyber-physical systems (ICCPS) (pp. 1–8). IEEE.
[9] Cimino, D., et al. (2016). A low-cost, open-source cyber-physical system for automated, remotely controlled precision agriculture. In International conference on applications in electronics pervading industry, environment, and society. Cham: Springer. S. Aasha Nandhini et al. 123
[10] Dong, X., Vuran, M. C., & Irmak, S. (2013). Autonomous precision agriculture through the integration of wireless underground sensor networks with center pivot irrigation systems. Ad Hoc Networks, 11(7), 1975–1987.
[11] Taheriazad, L., Portillo-Quintero, C., & Sanchez-Azofeifa, G. A. (2014). Application of wireless sensor networks (WSNs) to oil sands environmental monitoring. OSRIN Report No. TR-48
[12] Singh, V., & Misra, A. K. (2016). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4, 41–49.
[13] Dandawate, Y., & Kokare, R. (2015). An automated approach for classification of plant diseases towards the 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.
[14] Dhakate, M., & Ingole, A. B. (2015). Diagnosis of pomegranate plant diseases using the neural network. In 2015 fifth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG) (pp. 1–4). IEEE.
[15] Bhange, M., & Hingoliwala, H. A. (2015). Smart farming: Pomegranate disease detection using image processing. Procedia Computer Science, 58, 280–288.
[16] Mokhtar, U., Ali, M. AS., Hassenian, A. E., & Hefny, H. (2015) Tomato leaves diseases detection approach based on support vector machines. In 2015 11th international computer engineering conference (ICENCO) (pp. 246–250). IEEE.
[17] Indumathi, K., Hemalatha, R., Aasha Nandhini, S., Radha, S. (2017). Intelligent plant disease detection system using wireless multimedia sensor networks. In IEEE international conference on wireless communications, signal processing and networking (WiSPNET), March 2017 (to be published in IEEE Xplore digital library).
[18] Aasha Nandhini, S., Radha, S., & Kishore, R. (2015). Video compressed sensing framework for wireless multimedia sensor networks using a combination of multiple matrices. Elsevier’s Computers & Electrical Engineering, 44, 51–66.
[19] Tropp, J. A., & Gilbert, A. C. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12), 4655–4666.
[20] Sivabalaselvamani, D., A. Tamilarasi, and L. Rahunathan. "Supporting Trust-based Design for Efficient Transportation using Intelligent Transportation System (ITS) in VANET." Asian Journal of Research in Social Sciences and Humanities 6.7 (2016): 634-647.
[21] Rahunathan, L., A. Tamilarasi, and D. Sivabalaselvamani. "Efficient and Secure Interoperable Healthcare Information System Using Keyword Searchable and Role-Based Access Control in Cloud Environment." Journal of Computational and Theoretical Nanoscience 15.4 (2018): 1176-1181.
[22] Sivabalaselvamani,, D and Harishankher, A.S and Rahunathan, L. and Tamilarasi, A, Accident Identification Using Fuzzy Cognitive Maps with Adaptive Non-Linear Hebbian Learning Algorithm (November 15, 2017). Proceedings of the International Conference on Intelligent Computing Systems (ICICS 2017 – Dec 15th - 16th 2017) organized by Sona College of Technology, Salem, Tamilnadu, India. Available at SSRN https://ssrn.com/abstract=3125251 or http://dx.doi.org/10.2139/ssrn.3125251