Threshold-based Multi-Crop Recommendation: A Flexible Decision Layer for AI in Precision Agriculture

The majority of machine learning-based crop recommendation systems are designed to output a single most suitable crop, often based on top-1 classification accuracy. However, in real-world agricultural scenarios, farmers benefit more from multiple viable crop options, especially in cases where agro-environmental conditions support multiple alternatives. This paper introduces a threshold-based decision layer built on top of supervised machine learning (ML) and deep learning (DL) models, designed to recommend multiple suitable crops based on confidence scores. By setting a threshold for prediction probability, the system extracts all crops that meet or exceed the defined suitability level, providing ranked outputs that improve flexibility, interpretability, and real-world usability. The proposed approach is evaluated using soil, weather, and Sentinel-2 satellite data specific to Kurukshetra, India. Experimental results demonstrate that the threshold-based method reduces the risk of misclassification and enhances the adaptability of the system across seasonal and spatial conditions. This contribution bridges the gap between intelligent prediction and actionable decision-making in precision agriculture.