Real-time and accurate information on fine-grained changes in crop cultivation is of great significance for crop growth monitoring, yield prediction and agricultural structure adjustment. Aiming at the problems of serious spectral confusion in visible high-resolution unmanned aerial vehicle (UAV) images of different phases, interference of large complex background and salt-and-pepper noise by existing semantic change detection (SCD) algorithms, in order to effectively extract deep image features of crops and meet the demand of agricultural practical engineering applications, this paper designs and proposes an agricultural geographic scene and parcel-scale constrained SCD framework for crops (AGSPNet). AGSPNet framework contains three parts: agricultural geographic scene (AGS) division module, parcel edge extraction module and crop SCD module. Meanwhile, we produce and introduce an UAV image SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple semantic variation types of crops in complex geographical scene. We conduct comparative experiments and accuracy evaluations in two test areas of this dataset, and the results show that the crop SCD results of AGSPNet consistently outperform other deep learning SCD models in terms of quantity and quality, with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over the sub-optimal method. The method proposed in this paper can clearly detect the fine-grained change information of crop types in complex scenes, which can provide scientific and technical support for smart agriculture monitoring and management, food policy formulation and food security assurance.

翻译：农作物种植精细变化的实时准确信息对作物生长监测、产量预测及农业结构调整具有重要意义。针对现有语义变化检测（SCD）算法在可见光高分辨率无人机（UAV）多时相影像中存在的严重光谱混淆、大范围复杂背景干扰及椒盐噪声问题，为有效提取作物深层图像特征并满足农业工程应用需求，本文设计并提出一种农业地理场景与地块约束的作物SCD框架（AGSPNet）。该框架包含三部分：农业地理场景（AGS）划分模块、地块边缘提取模块及作物SCD模块。同时，我们构建并引入专用于农业监测的UAV影像SCD数据集（CSCD），涵盖复杂地理场景下多种作物语义变化类型。在该数据集的两个测试区进行对比实验与精度评估，结果表明：AGSPNet的作物SCD结果在数量与质量上均持续优于其他深度学习SCD模型，其评估指标F1分数、Kappa系数、总体精度（OA）及平均交并比（mIoU）相比次优方法平均分别提升0.038、0.021、0.011及0.062。本文方法能清晰检测复杂场景中作物类型的精细变化信息，可为智慧农业监测管理、粮食政策制定及粮食安全提供科技支撑。