Precision Agriculture Revolution: Integrating Digital Twins and Advanced Crop Recommendation for Optimal Yield

TL;DR

This paper proposes an integrated digital twin framework utilizing real-time data and machine learning to enhance crop recommendations and optimize agricultural yields in the context of Agriculture 4.0.

Contribution

It introduces a novel digital twin-based system combining weather APIs, GPS, soil sensors, and machine learning for precise crop management and yield optimization.

Findings

Enhanced crop growth forecasting accuracy

Improved water and pesticide management strategies

Effective integration of real-time data with predictive models

Abstract

With the help of a digital twin structure, Agriculture 4.0 technologies like weather APIs (Application programming interface), GPS (Global Positioning System) modules, and NPK (Nitrogen, Phosphorus and Potassium) soil sensors and machine learning recommendation models, we seek to revolutionize agricultural production through this concept. In addition to providing precise crop growth forecasts, the combination of real-time data on soil composition, meteorological dynamics, and geographic coordinates aims to support crop recommendation models and simulate predictive scenarios for improved water and pesticide management.