Embedded Compression Algorithm for Agricultural Optical Remote Sensing Images Based on Adaptive Sparse Coding

High-resolution remote sensing is essential for information acquisition in smart agriculture, yet real-time processing remains a critical challenge. Although high-resolution imagery provides comprehensive data, its massive volume complicates efficient handling. Existing techniques are predominantly restricted to offline scenarios, conflicting with the practical requirements for online acquisition and transmission. To address these challenges, we propose an adaptive sparse coding method for agricultural remote sensing images that dynamically selects compression strategies based on image content. Using this approach, we developed an embedded terminal system for real-time agricultural data transmission over 5G networks. Experimental results show that at a 95% compression ratio, transmission time is reduced by over 90% compared with uncompressed images. The method also achieves high-fidelity reconstruction; the deviation rates for the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) remain below 5% even at a 97% compression ratio. This approach offers fast transmission, high compression efficiency, and strong reconstruction quality, making it suitable for field equipment such as unmanned aerial vehicles in real-time monitoring networks.