Smart Crop Selection: A Machine Learning-Based Decision Support System for Farmers

Abstract

The paper proposes a machine learning-based decision support system for smart crop selection, leveraging predictive analytics to assist farmers in making data-driven decisions. The study evaluates multiple machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, and Decision Tree, to determine their effectiveness in crop recommendation. The models are trained on a dataset incorporating soil nutrients (N, P, K), pH levels, rainfall, temperature, and humidity, enabling accurate classification of suitable crops for given agricultural conditions. The experimental results indicate that ensemble learning models such as Random Forest and XGBoost achieve the highest accuracy, outperforming traditional classification models. The findings highlight the potential of AI-driven crop recommendation systems in enhancing productivity, optimizing resource utilization, and reducing farming risks associated with unpredictable climate conditions. By integrating machine learning into precision agriculture, farmers can receive real-time, tailored crop recommendations, leading to improved decision-making and sustainability. This study contributes to the ongoing efforts in data-driven agriculture, emphasizing the importance of advanced computing techniques in addressing modern agricultural challenges.

Introduction

Agriculture is crucial for food security and economic growth but faces challenges from changing environmental conditions and traditional crop selection methods that often fail to adapt. Machine Learning (ML) and Artificial Intelligence (AI) offer data-driven solutions to improve crop recommendations by analyzing soil properties, climate data, and market demand.

This study reviews ML techniques—such as Decision Trees, Random Forest, SVM, KNN, and Deep Learning models—used to predict optimal crops, highlighting their strengths and limitations in accuracy, efficiency, and applicability. Key challenges include limited data quality, model interpretability, regional variability, and accessibility for small-scale farmers. The study aims to develop an optimized, user-friendly framework integrating real-time data and predictive modeling to enhance adoption.

Research shows ML-driven soil analysis improves crop suitability predictions, while environmental data models help adapt to climate variability. Incorporating market demand data further aligns crop selection with profitability. Case studies demonstrate AI’s impact: increased yields, better resource use, and reduced crop failures in diverse regions.

The methodology involves collecting and preprocessing soil and environmental data, then applying various ML algorithms to identify the best models for accurate crop recommendations, thereby supporting sustainable and resilient agricultural practices.

Conclusion

This study underscores the transformative role of machine learning in modern agriculture, particularly in crop recommendation and precision farming[60]. By analyzing multiple machine learning algorithms, it is evident that ensemble models such as Random Forest (RF) and XGBoost consistently achieve superior accuracy and reliability. These models outperform traditional approaches by effectively handling large, multidimensional agricultural datasets while minimizing errors in crop prediction[53]. Support Vector Machine (SVM) and Naïve Bayes also demonstrate strong predictive capabilities, whereas Logistic Regression and Decision Tree exhibit relatively lower accuracy, highlighting the importance of selecting the right algorithm for different agricultural applications[54]. The implementation of machine learning in agriculture extends beyond crop recommendation, providing valuable insights for yield estimation, soil health analysis, and climate adaptation strategies. By leveraging historical data and predictive analytics, farmers can enhance decision-making, optimize resource allocation, and mitigate risks associated with unpredictable weather conditions and soil variability[59]. Additionally, AI-driven models promote efficient land use, reduced dependency on chemical inputs, and improved agricultural sustainability, ultimately contributing to higher productivity and economic benefits[58]. Furthermore, the integration of these models into user-friendly digital platforms and mobile applications can enable broader accessibility for farmers, ensuring real-time recommendations tailored to specific geographical and climatic conditions. The adoption of such AI-based solutions can lead to a paradigm shift in farming, moving towards data-driven, technology-assisted agricultural practices that enhance food security and sustainability. As machine learning continues to evolve, its role in agriculture will remain vital in addressing global challenges and improving overall farming efficiency[55]. In conclusion, the continuous integration of machine learning and smart technologies into agriculture is vital for improving efficiency and sustainability in the face of global challenges[57]. By focusing on relevant crops based on local conditions and economic significance, our research supports farmers and policymakers in navigating the complexities of modern agriculture[56]. Ultimately, this work advocates for a future where integrated AI frameworks not only contribute to enhanced productivity but also play a crucial role in achieving food security and fostering a resilient agricultural sector equipped to meet the demands of a growing global population.

References

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Copyright

Copyright © 2025 Rahul Satheesan Nair, Shreeram Sanjay Sawant , Dr. Ujwala V. Gaikwad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.