K-means- SVM To Predict Diseases From Fruits In Agriculture Data

M. Akhila, A. L. Prasanna, T. N. S. P. Nikita, T. Prasanna, P. P. Sowmya, R. John Mathew

Abstract


A fruit disease's early discovery is crucial since it will harm the agricultural sector. This work focuses mostly on the identification and analysis of fruit illnesses that are present in plant regions, as well as the documentation of the agricultural sector. In this study, the author uses the SVM algorithm and KMEANS segmentation to forecast fruit illnesses. When using KMEANS segmentation, similar colour clusters can be created. Clustering features are then utilised to train the SVM classifier, which uses those features to determine if an input image is normal or has a disease.

References


Asha R.PatilVarshaI.Patil, B.S.Panchbhai, “Detection of Plant Diseases Using Image Processing Toolsâ€. Asha R. PatilVarshaI.Patil. Int. Journal of Engineering Research and Application ISSN : 2248-9622, Vol. 7, Issue 4, ( Part -2) April 2017, pp.44-45.

Pratik Agarwal, “AGROCLOUD-Open surveillance of Indian Agriculture via cloudâ€2016.International Conference on Information Technology(InCITe)-The Next Generation IT Summit.

Athmaja S1, Hanumanthappa M2, “Applications of Mobile Cloud Computing and Big Data Analytics in Agriculture Sector- A Surveyâ€. International Journal of Advanced Research in Computer and Communication Engineering ICRITCSA M S Ramaiah Institute of Technology, Bangalore Vol. 5, Special Issue 2, October 2016.

Wang-Su Jeon1 and Sang-Yong Rhee,â€Plant Leaf Recognition Using a Convolution Neural Networkâ€International Journal of Fuzzy Logic and Intelligent Systems Vol. 17, No. 1, March 2017, pp. 26-34

Li Tan1,2†,Hongfei Hou1,Qin Zhang2.“An Extensible Software Platform for Cloud-based Decision Support and Automation in Precision Agricultureâ€.2016 IEEE 17th International Conference on Information Reuse and Integration.

P. Deepan“Detection and Classification of Plant Leaf Diseases by using Deep Learning Algorithmâ€International Journal of Engineering Research & Technology (IJERT) ISSN: 2278- 0181Published by, www.ijert.org,ICONNECT - 2k18

JagadeeshD.Pujari.a, Rajesh Yakkundimuthb,*, Abdulmunaf S.Byadgi.c, “Image Processing Based Detection of Fungal Diseases in Plantsâ€. International Conference on Information and Communication Technologies(ICICT 2014).Procedia Computer Science 46 ( 2015 ) 1802 – 1808.

Shiv Ram Dubey1, Pushkar Dixit2, Nishant Singh3, Jay Prakash Gupta4, “Infected Fruit Part Detection Using K-Means Clustering Segmentation Techniqueâ€. International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 2.

Lianjie Zhou, Nengcheng Chen, Zeqiang Chen, and ChenjieXing.“ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on Cloud Computing for Soil Moisture Mapping in Precision Agricultureâ€. IEEE Journal of selected topics in applied earth observations and remote sensing.


Full Text: PDF [Full Text]

Refbacks

  • There are currently no refbacks.


Copyright © 2013, All rights reserved.| ijseat.com

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.

Â