Paddy Crop Production Analysis Based on SVM and KNN Classifier
Pankaj Bhambri1, Inderjit Singh Dhanoa2, Vijay Kumar Sinha3, Jasmine Kaur4
1Dr. Pankaj Bhambri, Dept. of Info. Tech., Guru Nanak Dev Engineering College, Ludhiana, Punjab, India.
2Dr. Inderjit Singh Dhanoa, Dept. of CSE, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India.
3Dr. Vijay Kumar Sinha, Dept. of CSE, Chandigarh Engineering College, Mohali, Punjab, India.
4Er. Jasmine Kaur, Dept. of CSE, IKGPTU, Jalandhar, Punjab, India.

Manuscript received on January 09, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on January 30, 2020. | PP: 2790-2793 | Volume-8 Issue-5, January 2020. | Retrieval Number: D8650118419/2020©BEIESP | DOI: 10.35940/ijrte.D8650.018520

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Abstract: In earlier times, the people used to fulfill their own requirements by cultivating the crops in their own land regions. In the economy of a nation, an important role is played by the farming sector. A variety of fungal and bacterial infections infect various crops. Reducing the use of insect killers is a prominent demand of sustainable development. The minimum use of pesticides saves environment and increases the quality of crops. To improve the accuracy of paddy production prediction the KNN is implemented for the paddy production prediction in data mining. The SVM classifieris also implemented which is compared with the KNNclassifier. The presented and earlier classifier will be applied in python and it is expected that accuracy will be improved and execution time will be reduced. It is analyzed that KNN performs well as compared to SVM classifier for the paddy production prediction as per the obtained analytic results.
Keywords: Paddy Crop Production, SVM Classifier, KNN classifier.
Scope of the Article: Production.