International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Agriculture remains the backbone of many developing economies, yet farmers continue to face significant challenges in selecting the most suitable crop for cultivation due to unpredictable climatic conditions, soil variability, and limited access to data-driven decision support systems. Traditional crop advisory methods largely depend on manual expertise, historical practices, or generalized recommendations, which often fail to account for real-time environmental parameters and regional diversity. With the increasing availability of agricultural datasets and advancements in Machine Learning (ML), there is a growing opportunity to enhance crop selection accuracy and improve farming outcomes through intelligent systems.

This research presents CropSense_AI, a machine learning–based crop recommendation system designed to assist farmers in identifying the most appropriate crop to cultivate based on key soil and environmental parameters. The system utilizes essential inputs such as nitrogen, phosphorus, potassium levels, soil pH, temperature, humidity, and rainfall to generate reliable crop recommendations. A Random Forest classification algorithm is employed due to its robustness, ability to handle non-linear relationships, and resistance to overfitting when working with real-world agricultural data. The model is trained and evaluated using a well-structured agricultural dataset, achieving high prediction accuracy and stable performance across multiple crop classes.

Unlike many existing solutions that rely heavily on IoT sensors, image processing, or complex infrastructure, CropSense_AI focuses on simplicity, accessibility, and interpretability. The system is designed to operate using readily available data inputs, making it suitable for deployment in resource-constrained rural environments. Additionally, the web-based interface allows users to interact easily with the system, visualize input parameters, and understand prediction outcomes without requiring technical expertise. This practical design ensures that the system can be adopted by farmers, agricultural officers, and extension services with minimal training.

The proposed system bridges a critical gap in current agricultural decision support tools by combining accuracy, usability, and deployment readiness. By providing data-driven crop recommendations before cultivation, CropSense_AI has the potential to reduce crop failure risk, optimize resource utilization, and support sustainable farming practices. The results demonstrate that machine learning–based crop recommendation systems can play a vital role in modern precision agriculture, contributing to improved productivity, informed decision-making, and long-term agricultural sustainability.

Downloads: | DOI: 10.17148/IJARCCE.2026.15165