Crop Recommendation System Using Machine Learning: A Comparative Study

Authors

  • Nirajan Acharya Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Prajwal Khatiwada Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Rakesh Pandey Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Sagar Niroula Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Pralhad Chapagain Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal

DOI:

https://doi.org/10.3126/injet.v1i2.66708

Keywords:

Ensemble, Recommendation, Cross Validation, Robustness, Recall

Abstract

Agriculture, as a fundamental aspect of human existence, faces challenges in crop selection, impacting resource allocation and productivity. This project addresses these challenges by proposing a stable system employing a soft voting classifier ensemble method. The ensemble comprises Naive Bayes, Support Vector Machine (SVM), Decision Tree, and Random Forest classifiers, offering personalized crop recommendations. Feasibility analysis encompasses technical, operational, economic, and scheduling aspects, ensuring practicality and efficacy. Development follows an incremental model, emphasizing continuous enhancement through feedback. Results indicate accuracies for individual classifiers (’Decision Tree’: 98.38%, ’Random Forest’: 98.90%, ’Naive Bayes’: 98.14%, ’SVM’: 98.50%), with an ensemble accuracy of 98.99%. Cross-validation confirms robustness. Evaluation metrics such as recall, precision, and F1 score demonstrate that the soft voting ensemble outperforms individual classifiers, highlighting its effectiveness in optimizing crop selection processes in agriculture and facilitating improved resource management and productivity.

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Published

2024-06-24

How to Cite

Acharya, N., Khatiwada, P., Pandey, R., Niroula, S., & Chapagain, P. (2024). Crop Recommendation System Using Machine Learning: A Comparative Study. International Journal on Engineering Technology, 1(2), 302–311. https://doi.org/10.3126/injet.v1i2.66708

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Section

Articles