Research on Ecological Monitoring of Digital Agriculture Industry Driven by Remote Sensing-Smart Devices-AI Collaboration

Continuous ecological monitoring is required for digital agriculture, but traditional approaches usually rely on isolated data sources such as satellites, UAVs, or field sensors, which limit spatial coverage, temporal frequency, and real-time decision-making. A single protocol that combines multi-scale data and describes how to create an effective and scalable monitoring workflow is desperately needed. To provide a dependable and automated ecological monitoring system for digital agriculture, this research aims to provide a clear, sequential process for combining remote sensing, smart field-based equipment, and Artificial Intelligence (AI) techniques. Developing an integrated monitoring method that provides reliable, high-resolution ecological data remains possible by adhering to the protocol. Harmonized datasets, robust data streams, and automated analytical outputs appropriate for operational agricultural monitoring are produced by the integrated Long Short-Term Memory with Transformer and Graph Neural Network (LSTM/Transformer/GCN) technique. Experiments were carried out in the Huang-Huai-Hai Plain (China) over a full crop rotation cycle (June 2023-May 2024). Results showed that fused data improved overall integrity to 92.3 ± 2.1% (23.5% higher than single RS data), reducing RMSE of soil volumetric water content (to 1.78 ± 0.25%) and crop NDVI (to 0.04 ± 0.01) by over 50%. Investigators and practitioners are able to utilize the structured methodology to implement real-time ecological monitoring in a useful and flexible way. It facilitates effective, automated, and scalable digital farming applications by integrating remote sensing, advanced technology, and AI applications.