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

2025-02-06

AI Summary

To address low agricultural resource utilization efficiency and insufficient crop recommendation accuracy, this paper proposes a multi-source heterogeneous real-time data-driven agricultural digital twin framework. The framework integrates NPK soil sensing, GPS geolocation, meteorological API data, and lightweight machine learning models to construct a crop growth digital twin capable of closed-loop simulation and dynamic optimization, enabling precise recommendations for irrigation, pest control, and other agronomic decisions. Its key innovation lies in establishing, for the first time in agriculture, a digital twin architecture specifically designed for real-time data streams, supporting online iterative optimization of crop recommendations and intervention strategies. Empirical evaluation demonstrates significant improvements: crop recommendation accuracy increases markedly, pesticide usage decreases by 18%, irrigation water consumption reduces by 12%, and crop yield prediction error is constrained within 4.3%.

Technology Category

• Search and Optimization: Algorithm Configuration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Optimize agricultural yield using digital twins
• Enhance crop recommendations with machine learning
• Improve water and pesticide management strategies

Innovation

• Digital twin for agriculture
• Machine learning crop models
• Real-time soil and weather data