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

Abstract

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.

Keywords: Crop Recommendation, Agriculture, Machine Learning, Ensemble Model, Internet of Things (IoT)

JEL Classification: Q16, Q12, Q55