Enhancing Crop Management: Ensemble Machine Learning for Real-Time Crop Recommendation System from Sensor Data

The agricultural industry is essential to the world’s food production, and it is critical to use cutting-edge technologies to increase crop productivity. We provide a revolutionary Crop Recommendation System (CRS) that utilizes cutting-edge technology to maximize crop output in response to the pressing need for improvement. Our study incorporates real-time monitoring of soil conditions, made possible by a custom hardware configuration that includes sensors for temperature, humidity, phosphorus, potassium, nitrogen, and pH measurements. First, we assembled a large dataset with 22 kinds of agricultural production components. Using many machine learning models, such as ensemble methods and baseline classifiers, we were able to classify crops with an astounding $99 \%$ accuracy rate. With the application of these insights, the CRS provides customized recommendations through an easy-to-use user interface for appropriate crops under particular climatic conditions. Our system’s innovative combination of hardware sensing capabilities and AI-driven decision-making promises to revolutionize crop management practices, offering actionable insights for agricultural stakeholders. Our system’s novel integration of AI-driven decision-making and hardware sensing capabilities promises to transform crop management techniques and provide agricultural stakeholders with useful insights.