TY  - JOUR
AU  - Behera, Sobhana
AU  - Reddy, N. Toyaad Kumar
AU  - Pradhan, Shubhasri
PY  - 2025
DA  - 2025/10/23
TI  - Development of an Intelligent Agricultural Decision Support System for Crop Recommendation Using Machine Learning Techniques
JO  - Next-Generation Computing Systems and Technologies
T2  - Next-Generation Computing Systems and Technologies
JF  - Next-Generation Computing Systems and Technologies
VL  - 1
IS  - 1
SP  - 43
EP  - 53
DO  - 10.62762/NGCST.2025.340075
UR  - https://www.icck.org/article/abs/NGCST.2025.340075
KW  - smart crop prediction
KW  - machine learning in agriculture
KW  - precision farming
KW  - random forest classifier
KW  - agricultural decision support system
KW  - real-time data integration
AB  - Agriculture plays a fundamental role in sustaining the global economy and ensuring food security, yet farmers often rely on intuition and traditional practices for crop selection, leading to inefficiencies in yield and resource utilization. This research proposes a machine learning-based system for smart crop prediction and recommendation, aimed at enhancing precision agriculture through data-driven decision-making. The study integrates historical datasets containing soil parameters (pH, nitrogen, phosphorus, potassium) and climatic factors (temperature, humidity, rainfall) with real-time environmental data fetched via APIs. Multiple machines learning models, including Decision Trees, Support Vector Machines, XGBoost, and Random Forest Classifiers, were evaluated, with the Random Forest model achieving the highest prediction accuracy of 87.93%. A user-friendly Flask web application was developed to allow farmers to input their location and receive real-time crop recommendations. Data preprocessing techniques such as normalization, feature selection, and outlier handling were implemented to improve model performance. Challenges like data imbalance, environmental variability, and the absence of socio-economic factors were acknowledged and addressed where possible. The system's adaptability and scalability make it suitable for diverse agricultural contexts, offering a significant step towards smart farming solutions. Future enhancements will involve the integration of IoT sensors, satellite imagery, and advanced deep learning techniques to further increase prediction reliability and applicability across different regions.
SN  - 3070-3328
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Behera2025Developmen,
  author = {Sobhana Behera and N. Toyaad Kumar Reddy and Shubhasri Pradhan},
  title = {Development of an Intelligent Agricultural Decision Support System for Crop Recommendation Using Machine Learning Techniques},
  journal = {Next-Generation Computing Systems and Technologies},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {43-53},
  doi = {10.62762/NGCST.2025.340075},
  url = {https://www.icck.org/article/abs/NGCST.2025.340075},
  abstract = {Agriculture plays a fundamental role in sustaining the global economy and ensuring food security, yet farmers often rely on intuition and traditional practices for crop selection, leading to inefficiencies in yield and resource utilization. This research proposes a machine learning-based system for smart crop prediction and recommendation, aimed at enhancing precision agriculture through data-driven decision-making. The study integrates historical datasets containing soil parameters (pH, nitrogen, phosphorus, potassium) and climatic factors (temperature, humidity, rainfall) with real-time environmental data fetched via APIs. Multiple machines learning models, including Decision Trees, Support Vector Machines, XGBoost, and Random Forest Classifiers, were evaluated, with the Random Forest model achieving the highest prediction accuracy of 87.93\%. A user-friendly Flask web application was developed to allow farmers to input their location and receive real-time crop recommendations. Data preprocessing techniques such as normalization, feature selection, and outlier handling were implemented to improve model performance. Challenges like data imbalance, environmental variability, and the absence of socio-economic factors were acknowledged and addressed where possible. The system's adaptability and scalability make it suitable for diverse agricultural contexts, offering a significant step towards smart farming solutions. Future enhancements will involve the integration of IoT sensors, satellite imagery, and advanced deep learning techniques to further increase prediction reliability and applicability across different regions.},
  keywords = {smart crop prediction, machine learning in agriculture, precision farming, random forest classifier, agricultural decision support system, real-time data integration},
  issn = {3070-3328},
  publisher = {Institute of Central Computation and Knowledge}
}