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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Christine Musanase & Anthony Vodacek & Damien Hanyurwimfura & Alfred Uwitonze & Innocent Kabandana, 2023. "Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices," Agriculture, MDPI, vol. 13(11), pages 1-23, November.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:11:p:2141-:d:1279234
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    References listed on IDEAS

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    1. George W. Norton & Jeffrey Alwang, 2020. "Changes in Agricultural Extension and Implications for Farmer Adoption of New Practices," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 8-20, March.
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