Digital Image Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Identification of Medicinal Plants using Visual Characteristics of Leaves and Flowers


Affiliations
1 Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru, India
2 Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru, India
     

   Subscribe/Renew Journal


The proposed framework helps in ID of plant sickness and gives cures that can be utilized as a safeguard component against the illness. The information base got from the Internet is appropriately isolated and the distinctive plant species are recognized and are renamed to frame a legitimate data set then, at that point get test-data set which comprises of different plant infections that are utilized for checking the exactness and certainty level of the undertaking. Then, at that point utilizing preparing information, the classifier is prepared and afterward yield will be anticipated with ideal exactness. The proposed system comes under Machine learning domain. Machine Learning centers around the improvement of programs that can get to information and use it to find out on their own. Machine Learning has various applications and has been used to tackle real world problems in an efficient manner. It has applications in medicine, communication, entertainment, military and so on. Convolutional Neural Network (CNN) which comprises of different has been used for classification and prediction. The problem with existing systems is that they are limited to a few numbers of plant species or due to use of inefficient algorithms have not been able to achieve the desired levels of accuracy. With the proposed system and training model an accuracy level of 78% was achieved. The proposed framework gives the name of the plant species with its certainty level and the cure that can be taken as fix.

Keywords

Machine Learning, Convolutional Neural Network.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Manojkumar P., Surya C. M., and Varun P. Gopi, "Identification of Ayurvedic Medicinal Plants by Image Processing of Leaf Samples", 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp 978- 1- 5386-1931-5.

  • Mr. K.Nithiyanandhan and Prof.T. Bhaskara Reddy, "Analysis of the Medicinal Leaves by using Image Processing Techniques and ANN", Vol 8, No. 5, ISSN No. 0976-5697, May June 2017.

  • Adams Begue, Venitha Kowlessur, Fawzi Mahomoodally, Upasana Singh and Sameerchand, "Automatic Recognition of Medicinal Plants using Machine Learning Techniques", International Journal of Advanced Computer Science and Applications, Vol. 8, No. 4, 2017.

  • H. X. Kan, L. Jin, and F. L. Zhou," Classification of Medicinal Plant Leaf Image Based on Multi-Feature Extraction", Pattern Recognition and Image Analysis, Vol. 27, No. 3, 2017, pp. 581–587, 1054-6618. © Pleiades Publishing, Ltd

  • Riddhi H. Shaparia, Dr. Narendra M. Patel and Prof. Zankhana H. Shah," Flower Classification using Texture and Color Features", International Conference on Research and Innovations in Science, Engineering &Technology, Volume 2, 2017, Pages 113–118.

  • Marco Seeland, Michael Rzanny, NedalAlaqraa, Jana Wa ¨ldchen, Patrick Ma ¨der, "Plant species classification using flower images—A comparative study of local feature representations", PLOS ONE | DOI:10.1371/journal.pone.0170629 February 24, 2017.

  • D Venkataraman and Mangayarkarasi N, "Computer Vision Based Feature Extraction of Leaves for Identification of Medicinal Values of Plants", IEEE International Conference on Computational Intelligence and Computing Research, 978-1-5090-0612-0/16/$31.00 ©2016 IEEE.

  • B.C.Heymans et al.,"A neural network for Opuntia leaf- form recognition,"IJCNN, volumn 3, pp 2116-2121,1991.

  • D.Warren, "Automated leaf shape description for variety testing in Chrysanthemums" in Proc. 6th International Conference Image Processing and its Application, Duplin, Ireland, 1997.

  • Cholhong Im, Hirobumi Nishida, Tosiyasu L. Kunii," Recognizing Plant Species byNormalized Leaf Shapes", Vision Interface' 99, TroisRuvueres, Canada,19-21 May.

  • Ji-Xiang Du , Xiao-Feng-Wang, Guo-Jun Zhang ,"Leaf Shape based Plant Species Recognition", ELSEVIER,Applied Mathematics and Computation 185 (2007) 883-893.

  • Chomtip Pornpanomchai, Supolgaj Rimdusit, Piyawan Tanasap and Chutpong Chaiyod, "Thai Hern Leaf Image Recognition System(THLIRS)", Kasetsart J.(Nat.Sci.) 45 : 551 –562(2011).

  • Krishna Singh, Indra Gupta and Sangeeta Gupta, " Classification of bamboo plant based on digital image processing by central moment", 2011 international conference on image information processing (ICIIP 2011)

  • Miao Teng and Zhao Chun Jiang , "An interactive method for modelling leaves", Fourth International Conference on Intelligent Computation Technology and Automation, 978-7695-4353-6/11 .IEEE DOIN10.1109/ICICTA.2011.298.

  • Zalikha Zulkfli , Puteh Saad and Itaza Afiani Mohtar, "Plant Leaf Identification using Moment Invariants and General Regression Neural Network". 978-1-4577-2152-/11@2011 IEEE.

  • Parag Bhandarkar, Harshal Doshi, Mamata Kadakia, Rizwan Ahmed, "Leaf Identification using Morphology and Structural Decomposition", 2014 International Conference on Signal and Integrated Networks(SPIN), 978-1-4799-2866-8/14 @ 2014 IEEE.

  • M. M. Amlekar, A.T.Gaikwad, R. R. Manza and P.L.Yannawar, "leaf shape extraction for plant classification", 2015 International conference on pervasive computing(ICPC).


Abstract Views: 336

PDF Views: 0




  • Identification of Medicinal Plants using Visual Characteristics of Leaves and Flowers

Abstract Views: 336  |  PDF Views: 0

Authors

N. Akash
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru, India
V. Kaushik
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru, India
Y. B. Prajwal
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru, India
R. Pranav
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru, India
V. C. Rudramurthy
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru, India

Abstract


The proposed framework helps in ID of plant sickness and gives cures that can be utilized as a safeguard component against the illness. The information base got from the Internet is appropriately isolated and the distinctive plant species are recognized and are renamed to frame a legitimate data set then, at that point get test-data set which comprises of different plant infections that are utilized for checking the exactness and certainty level of the undertaking. Then, at that point utilizing preparing information, the classifier is prepared and afterward yield will be anticipated with ideal exactness. The proposed system comes under Machine learning domain. Machine Learning centers around the improvement of programs that can get to information and use it to find out on their own. Machine Learning has various applications and has been used to tackle real world problems in an efficient manner. It has applications in medicine, communication, entertainment, military and so on. Convolutional Neural Network (CNN) which comprises of different has been used for classification and prediction. The problem with existing systems is that they are limited to a few numbers of plant species or due to use of inefficient algorithms have not been able to achieve the desired levels of accuracy. With the proposed system and training model an accuracy level of 78% was achieved. The proposed framework gives the name of the plant species with its certainty level and the cure that can be taken as fix.

Keywords


Machine Learning, Convolutional Neural Network.

References