Artificial Neural Networks Based Integrated Crop Recommendation System Using Soil and Climatic Parameters

J Madhuri; M Indiramma

doi:10.17485/IJST/v14i19.64

Article

Artificial Neural Networks Based Integrated Crop Recommendation System Using Soil and Climatic Parameters

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i19.64

Year: 2021, Volume: 14, Issue: 19, Pages: 1587-1597

Original Article

Artificial Neural Networks Based Integrated Crop Recommendation System Using Soil and Climatic Parameters

J Madhuri^1*, M Indiramma²

¹Research Scholar, Department of Computer Science and Engineering, B.M.S. College of Engineering, India
²Professor, Department of Computer Science and Engineering, B.M.S. College of Engineering, India

*Corresponding Author
Email: [email protected]

Received Date:12 January 2021, Accepted Date:15 May 2021, Published Date:06 February 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective : To develop crop recommendation system depending on location specific soil and climatic conditions. Method: The study introduces a novel recommendation system which uses Artificial Neural Networks (ANN) for recommending the suitable crop. The crops are recommended based on (a) Soil properties (b) Crop characteristics (c) Climate parameters. The crops namely maize, Finger millet, Rice and sugarcane is considered for the study. Depending on degree of relationship and limitations of the factors considered, following suitability classes are established: (a) Highly suitable: S1 (b) Moderately suitable: S2 (c) Marginally suitable: S3 (d) not suitable. The system uses the climate data from Meteorological survey of India and the soil data of Hadonahalli and Durgenahalli of Doddaballapur (dist.), Karnataka, India. The user interface developed takes the location specific soil properties as real time input and recommends the suitable crop considering the input and climate parameters. Findings: For the measurement of accuracy the model was tested on with ANN and decision tree. Overall accuracy value of ANN is 96% where the accuracy value of Decision tree is 91.5%. Hence the results obtained from ANN can be considered more efficient. Novelty: The number of models developed for crop recommendation is limited and the proposed model serves as the promising aspect in the planning of crops.

Keywords: Crop recommendation; ANN; Soil characters; Climate; MongoDB

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Copyright

© 2021 Madhuri & Indiramma.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)