Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices

Christine Musanase (), Anthony Vodacek, Damien Hanyurwimfura, Alfred Uwitonze and Innocent Kabandana
Additional contact information
Christine Musanase: African Centre of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda
Anthony Vodacek: Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
Damien Hanyurwimfura: African Centre of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda
Alfred Uwitonze: African Centre of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda
Innocent Kabandana: African Centre of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda

Agriculture, 2023, vol. 13, issue 11, 1-23

Abstract: Agriculture plays a key role in global food security. Agriculture is critical to global food security and economic development. Precision farming using machine learning (ML) and the Internet of Things (IoT) is a promising approach to increasing crop productivity and optimizing resource use. This paper presents an integrated crop and fertilizer recommendation system aimed at optimizing agricultural practices in Rwanda. The system is built on two predictive models: a machine learning model for crop recommendations and a rule-based fertilization recommendation model. The crop recommendation system is based on a neural network model trained on a dataset of major Rwandan crops and their key growth parameters such as nitrogen, phosphorus, potassium levels, and soil pH. The fertilizer recommendation system uses a rule-based approach to provide personalized fertilizer recommendations based on pre-compiled tables. The proposed prediction model achieves 97% accuracy. The study makes a significant contribution to the field of precision agriculture by providing decision support tools that combine artificial intelligence and domain knowledge.

Keywords: precision agriculture; Internet of Things; artificial intelligence; crop recommendation; fertilizer recommendation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/11/2141/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/11/2141/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:11:p:2141-:d:1279234

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().