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

TL;DR

This paper addresses fragmented deployment of Agriculture 4.0 technologies by proposing a digital twin framework that fuses NPK soil sensors, GPS geolocation, and weather APIs to enable predictive crop recommendations and resource management. The proposed approach enables scenario-based simulations of crop growth, yield forecasting, and optimization of irrigation and pesticide use within a cohesive platform. Key contributions include a data-fusion-driven digital twin, an adaptive crop-recommendation module, and integration with simulation for decision support. The framework offers practical impact for farmers, agribusiness, and agronomists by improving productivity, reducing resource waste, and supporting environmentally sustainable farming practices.

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.

Figures (4)

• Figure 1: Block Diagram of the proposed Framework
• Figure 2: Correlation between different features
• Figure 3: Correlation between different features
• Figure 4: Multi-class Receiver Operating Characteristic graph