Journal of Research and Review in Quantum Computing

Abstract

Agriculture remains the backbone of the global economy, with more than 58% of rural households in developing regions directly depending on farming as their primary source of livelihood. However, despite decades of agricultural moderniza-tion, farmers still struggle with low productivity due to the lack of scientific decision-making support regarding crop selection. Incorrect crop choices—driven by insufficient knowledge about soil health, rainfall patterns, nutrient composition, and local climatic suitability—lead to reduced yields, financial losses, and inefficient land utilization. A data-driven crop recommendation system can assist farmers by identifying the most suitable crop based on soil nutrients (NPK values), temperature, humidity, rainfall, pH level, and historical yield patterns. This research paper presents a comprehensive machine-learning-powered Crop Recommendation System designed to transform traditional agricultural decision-making. The system uses supervised learning algorithms such as Random Forest, Decision Trees, Support Vector Machines, KNN, Na¨ıve Bayes, and advanced deep learning models to predict the optimal crop for a given farmland. A detailed analysis of soil chemistry, climatic parameters, data preprocessing techniques, feature engineering, hyperparameter tuning, and model optimization is provided. The study also includes a comparative evaluation of model accuracy, precision, recall, F1-scores, ROC curves, and error metrics such as RMSE and MAE. Real-world datasets from Indian agricultural repositories, district-wise soil datasets, and the well-known Fertilizer Recommendation Dataset are used for performance benchmarking. The proposed system achieves a maximum accuracy of 98.7% using the Random Forest classifier and demonstrates practical deployability through a mobile-friendly decision support inter-face for farmers. Ethical considerations, scalability challenges, and future research directions involving IoT sensors, satellite monitoring, and AI-driven soil treatment guidance are thor-oughly explored. This 8000-word research work serves as a foundational blueprint for data-driven smart agriculture and contributes significantly to precision farming innovations.