Scientia Agricultura Sinica ›› 2026, Vol. 59 ›› Issue (1): 78-89.doi: 10.3864/j.issn.0578-1752.2026.01.006

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Developing a Lightweight Multimodal Model for Cropland Remote Sensing Monitoring

TANG HuaJun1(), WU WenBin1, YU QiangYi1, SHI Yun1, DUAN YuLin1, LI WenJuan1, QIAN JianPing1, SONG Qian1, XIA Lang1, LI HuiBin1, SU BaoFeng2, FAN BeiLei3, HU Qiong4, YE JianQiu5, ZHANG Shuai6   

  1. 1 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081
    2 College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi
    3 Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081
    4 College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430070
    5 Institute of Scientific and Technical Information Chinese Academy of Tropical Agricultural Sciences, Haikou 571101
    6 Department of Farmland Construction Management, Ministry of Agriculture and Rural Affairs, Beijing 100125
  • Received:2025-08-10 Accepted:2025-11-25 Online:2026-01-07 Published:2026-01-07

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Abstract:

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.

Key words: artificial intelligence, cropland remote sensing monitoring, application scenarios, morphological characteristics, attention mechanism, lightweight multimodal model

Fig. 1

The Data-Information-Knowledge-Wisdom (DIKW) pyramid framework (A) and the new paradigm for cropland remote sensing monitoring in the era of artificial intelligence (B)"

Table 1

Main application scenarios target entities, and requirements of cropland remote sensing monitoring"

应用场景
Application scenario		 主要用户
Main user		 精细信息（研究）
Information (Research)		 宏观知识（应用）
Knowledge (Application)
耕地数量和利用监测
Cropland area and use monitoring		 政府
Governments		 耕地及作物类型的空间分布图；耕地撂荒复种发生的地块；不同地类和作物类型之间的转移矩阵
Maps of cropland area, crop types, abandonment, intensification. Cropland use change transitional matrix		 主要的种植作物类型、种植方式；撂荒与“非粮化”总体趋势与预警
Dominant cropland use types; early warning on cropland use change
高标准农田（农田基础设施）监测监管
Farmland infrastructure monitoring		 政府、农民、新型经营主体
Governments, farmers,
organizations		 田块、道路、灌溉、林网等农田基础设施识别提取，农田形态参数估算
Identification of farmland infrastructure (including field plots, roads, irrigation systems, and shelterbelt), and estimation of morphological parameters		 区域高标准农田建设实施情况、建设质量情况
The overall implementation and quality of well-facilitated farmland construction programs
耕地质量退化监测
Cropland degradation monitoring		 政府、农民、新型经营主体
Governments, farmers,
organizations		 耕地土壤有机质、含盐量、pH等指标定量反演或预测
Quantitative inversion or prediction of soil properties including organic matter content, salinity, pH etc.		 耕地土壤侵蚀、次生盐渍化、酸化等质量退化情况
The overall status of erosion, salinization, and acidification
耕地农情动态监测
Crop growth monitoring		 政府、农民、新型经营主体
Governments, farmers,
organizations		 叶面积、光合有效辐射等作物生长关键参数定量反演
Quantitative inversion of key crop growth parameters including leaf area index and photosynthetically active radiation		 作物长势动态状况、与往年同期的差异情况
Crop growth status compared to the normal situation

Fig. 2

Knowledge of cropland monitoring directly obtained by satellite images"

Fig. 3

Development of cropland remote sensing monitoring multimodal model based on the fingerprint"

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