Developing a Lightweight Multimodal Model for Cropland Remote Sensing Monitoring

The spatio-temporal dynamics of cropland and their utilization are crucial to national food security, resource security and ecological security. Currently, the approach to cropland remote sensing monitoring generally follows the “data - (model) -information” paradigm. However, this paradigm has a significant “innovation-application” gap, with numerous information products but weak knowledge service capabilities, which fail to meet practical application needs of cropland protection and utilization. Artificial intelligence (AI) technology is accelerating the transformation from active data retrieval and analysis to intelligent knowledge services and empowerment. In the new era, the technical system for cropland remote sensing monitoring needs to be restructured. This paper thus proposed an innovative idea for constructing a lightweight multimodal model for cropland remote sensing monitoring. Firstly, it analyzed the demands of different subjects and categorized the application scenarios of cropland remote sensing monitoring into four aspects (cropland area and use, infrastructure, degradation, and crop growth), clarifying the specific requirements for monitoring information and knowledge services in different scenarios. Secondly, from the perspective of human cognition, it analyzed the “macro-level knowledge” and “fine-grained information” characteristics contained in the morphological features of cropland, providing a new entry point for the construction of a multimodal model for cropland remote sensing monitoring. Finally, it combines multi-modal remote sensing data with general large language models to construct an AI agent for cropland remote sensing monitoring, featuring capabilities in perception, reasoning, learning, and execution. It strengthens the attention mechanism to focus on and capture the important features of cropland morphology, and builds a lightweight multimodal model based on these features.