A Novel Dataset Generation Strategy and a Multi-Period Farmland Cultivation Zones Dataset from Unmanned Aerial Vehicle Imagery

Accurate delineation of farmland cultivation zones (FCZs) is crucial for advancing precision agriculture. However, identifying FCZs in landscapes where standardized and non-standard (fragmented) farmlands coexist remains a pressing challenge, primarily due to the lack of high-quality datasets covering such mixed patterns. To address this, we propose a novel tiling-based dataset generation method that integrates boundary probes and minimum-overlap Poisson-disk sampling (BP-MOPS). Using this strategy, we constructed a multi-temporal unmanned aerial vehicle (UAV) imagery dataset of FCZs—the multi-period farmland cultivation zones (MPFCZ) dataset—which encompasses three critical phenological stages: the dormant period (DP), the intermediate growing period (IGP), and the vigorous growing period (VGP). The source imagery was acquired over Zhouhu Village in China. The MPFCZ dataset comprises 6467 image patches (1024 × 1024 pixels), containing both standardized fields and fragmented cultivation zones typically missed by conventional methods. Both Transformer- and CNN-based models trained on MPFCZ surpassed those trained on the dataset generated by conventional segmentation strategy. The best-performing model achieved remarkable temporal change detection accuracy (mIoU > 0.82 across three phenological stages) and demonstrated strong cross-region generalization capability (0.8817 precision under zero-shot transfer). MPFCZ thus provides essential support for precise farmland identification in complex agricultural landscapes with standard and nonstandard fields mixed.