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IITM Journal of Information Technology

ISSN (P) 2395-5457 | Single Blind Peer Reviewed Journal

Published By
INSTITUTE OF INNOVATION IN TECHNOLOGY & MANAGEMENT
Affiliated to GGSIPU, NAAC Grade ‘A’, ISO 14001:2015, 17020:2012, 21001:2018 & 50001:2018 Certified,
A Grade by GNCTD, A++ Grade by SFRC

An IoT-Enabled Machine Learning–Based Crop Recommendation System for Smart Agriculture

Milan Sood1 ,Priyanka Bhutani2
Department of Computer Science Engineering
University School of Information, Communication and Technology, GGSIPU 

Abstract:  Many developing economies depend on agriculture but farmers often struggle to choose a crop suitable to dynamic soil and climatic conditions. Existing crop recommendation systems, which rely heavily on past data and static soil information, are unable to adapt to current environmental changes[15],[18]. With the recent IoT and ML developments, new smart adaptive agricultural decision support possibilities have emerged [9],[13]. The IoT-based machine learning model effectively predicts the best crop based on real-time soil and weather conditions of the agriculture land. Moreover, Decision Tree, Random Forest, and Decision Naïve Bayes are suitable for identifying the most suitable crop and can predict the crop with an overall accuracy of 89%, 94%, and 75%, respectively. The Random Forest model with an overall accuracy of 94% is the most suitable machine learning technique for predicting crop recommendation.

Keywords:   Crop Recommendation, Smart Agriculture, Internet of Things, Machine Learning, Random Forest, Decision Tree

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