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Crop Recommendation System for Precision Agriculture

Bharath Kumar R1 , Balakrishna K2 , Bency Celso A3 , Siddesha M4 , Sushmitha R5

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 1277-1282, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.12771282

Online published on May 31, 2019

Copyright © Bharath Kumar R, Balakrishna K, Bency Celso A, Siddesha M, Sushmitha R . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Bharath Kumar R, Balakrishna K, Bency Celso A, Siddesha M, Sushmitha R, “Crop Recommendation System for Precision Agriculture,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1277-1282, 2019.

MLA Style Citation: Bharath Kumar R, Balakrishna K, Bency Celso A, Siddesha M, Sushmitha R "Crop Recommendation System for Precision Agriculture." International Journal of Computer Sciences and Engineering 7.5 (2019): 1277-1282.

APA Style Citation: Bharath Kumar R, Balakrishna K, Bency Celso A, Siddesha M, Sushmitha R, (2019). Crop Recommendation System for Precision Agriculture. International Journal of Computer Sciences and Engineering, 7(5), 1277-1282.

BibTex Style Citation:
@article{R_2019,
author = {Bharath Kumar R, Balakrishna K, Bency Celso A, Siddesha M, Sushmitha R},
title = {Crop Recommendation System for Precision Agriculture},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {1277-1282},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4402},
doi = {https://doi.org/10.26438/ijcse/v7i5.12771282}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.12771282}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4402
TI - Crop Recommendation System for Precision Agriculture
T2 - International Journal of Computer Sciences and Engineering
AU - Bharath Kumar R, Balakrishna K, Bency Celso A, Siddesha M, Sushmitha R
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 1277-1282
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

Crop forecasting or prediction is the art of predicting crop yields and production before the harvest actually takes place, typically a couple of months in advance. Crop forecasting relies on computer programs that describe the plant-environment interactions in quantitative terms. The soil testing program starts with the collection of a soil sample from a field. The first basic principle of soil testing is that a field can be sampled in such a way that chemical analysis of the soil sample will accurately reflect the field’s true nutrient status.

Key-Words / Index Term

Precision Agriculture, Recommendation system, Ensemble model, Majority Voting technique, K-Nearest Neighbour.

References

[1]Satish Babu (2013), ‘A Software Model for Precision Agriculture for Small and Marginal Farmers’,at the International Centre for Free and Open Source Software (ICFOSS) Trivandrum, India.
[2]AnshalSavla, Parul Dhawan, HimtanayaBhadada, Nivedita Israni, Alisha Mandholia ,Sanya Bhardwaj (2015), ‘Survey of classification algorithms for formulating yield prediction accuracy in precision agriculture`, Innovations in Information,Embedded and communication systems (ICIIECS).
[3]Aakunuri Manjula, Dr.G .Narsimha (2015), ‘XCYPF: A Flexible and ExtensibleFramework for Agricultural Crop Yield Prediction’ , Conference on Intelligent Systems and Control (ISCO)
[4]Yash Sanghvi, Harsh Gupta, Harmish Doshi, DivyaKoli, AmoghAnshDivyaKoli, Umang Gupta (2015), ‘Comparison of Self Organizing Maps and Sammon’s Mapping on agricultural datasets for precision agriculture’, International Conference on Innovations in Information,Embedded and Communication systems (ICIIECS).
[5]Rakesh Kumar, M.P. Singh, Prabhat Kumar and J.P. Singh (2015), ’Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique’, International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM).
[6]A.T.M Shakil Ahamed, NavidTanzeem Mahmood, Nazmul Hossain, Mohammad Tanzir Kabir, Kallal Das, Faridur Rahman, Rashedur M Rahman (2015) , ‘Applying Data Mining Techniques to Predict Annual Yield of Major Crops and Recommend Planting Different Crops in Different Districts in Bangladesh’ , (SNPD) IEEE/ACIS International Conference.
[7]Liying Yang (2011), ‘Classifiers selection for ensemble learning based on accuracy and diversity’ Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS].
[8]Tapas Ranjan Baitharua, Subhendu Kumar Panib (2016), ‘Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset’ International Conference on Computational Modeling and Security (CMS).
[9]Aymen E Khedr, Mona Kadry, Ghada Walid (2015), ‘Proposed Framework for Implementing Data Mining Techniques to Enhance Decisions in Agriculture Sector Applied Case on Food Security Information Center Ministry of Agriculture, Egypt’, International Conference on Communications, management, and Information technology (ICCMIT`) .
[10]Monali Paul, Santosh K. Vishwakarma, Ashok Verma (2015), ‘Analysis of Soil Behaviour and Prediction of Crop Yield using Data Mining Approach’, International Conference on Computational Intelligence and Communication Networks.