Data-Driven Crop Recommendation Using Machine Learning: A Comparative Study of Classification Algorithms
Keywords:
Classification algorithms, Crop recommendation, Machine learning, Precision agriculture, Random forestAbstract
Choosing the right crop for a given field is one of the earliest and most consequential decisions a farmer makes, yet across much of the developing world it is still guided by habit and word-of-mouth rather than by measurable soil and weather evidence. This paper develops and benchmarks a machine-learning-based crop recommendation model that maps soil nutrient concentration (nitrogen, phosphorus and potassium), soil pH, ambient temperature, humidity, and rainfall onto the most suitable crop for cultivation. Six supervised classifiers Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbours, Logistic Regression, and Naïve Bayes were trained on a benchmark agronomic dataset and compared using accuracy, precision, recall, F1-score, and learning-curve diagnostics. Random Forest achieved the best overall balance, with 93.6% accuracy and an F1-score of 0.936, closely trailed by Decision Tree and Naïve Bayes, whereas the Support Vector Machine underfit the data and reached only 62.4% accuracy. These findings indicate that tree-based ensemble learners generalise more reliably on structured agronomic data than distance-based or margin-based classifiers, and they support the choice of Random Forest as the recommendation engine of a lightweight, cloud-deployable crop-advisory tool.
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