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Agriculture
Volume 16
Issue 1
10.3390/agriculture16010032
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Zirui Li
1,
Jinping Gu
1,
Siying Shang
1,
Yang Zhou
1,
Qing Luo
1,*,
Mingxue Zheng
2,*,
Xiaokai Li
3,
Chengjun Lin
4 and
Xuefeng Guan
5
1
School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan 430205, China
2
Institute of Agricultural Economy and Technology, Hubei Academy of Agricultural Sciences, Wuhan 430064, China
3
Wuhan DaSpatial Technology Co. Ltd., Wuhan 430223, China
4
Hubei Provincial Institute of Forest Survey and Planning, Wuhan 430079, China
5
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Agriculture 2026, 16(1), 32; https://doi.org/10.3390/agriculture16010032
Submission received: 29 October 2025 / Revised: 1 December 2025 / Accepted: 16 December 2025 / Published: 22 December 2025
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
Versions Notes

Abstract

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.
Keywords: farmland cultivation zone; precision agriculture; UAV imagery dataset; mixed farmland; dataset generation strategy farmland cultivation zone; precision agriculture; UAV imagery dataset; mixed farmland; dataset generation strategy

Share and Cite

MDPI and ACS Style

Li, Z.; Gu, J.; Shang, S.; Zhou, Y.; Luo, Q.; Zheng, M.; Li, X.; Lin, C.; Guan, X. A Novel Dataset Generation Strategy and a Multi-Period Farmland Cultivation Zones Dataset from Unmanned Aerial Vehicle Imagery. Agriculture 2026, 16, 32. https://doi.org/10.3390/agriculture16010032

AMA Style

Li Z, Gu J, Shang S, Zhou Y, Luo Q, Zheng M, Li X, Lin C, Guan X. A Novel Dataset Generation Strategy and a Multi-Period Farmland Cultivation Zones Dataset from Unmanned Aerial Vehicle Imagery. Agriculture. 2026; 16(1):32. https://doi.org/10.3390/agriculture16010032

Chicago/Turabian Style

Li, Zirui, Jinping Gu, Siying Shang, Yang Zhou, Qing Luo, Mingxue Zheng, Xiaokai Li, Chengjun Lin, and Xuefeng Guan. 2026. "A Novel Dataset Generation Strategy and a Multi-Period Farmland Cultivation Zones Dataset from Unmanned Aerial Vehicle Imagery" Agriculture 16, no. 1: 32. https://doi.org/10.3390/agriculture16010032

APA Style

Li, Z., Gu, J., Shang, S., Zhou, Y., Luo, Q., Zheng, M., Li, X., Lin, C., & Guan, X. (2026). A Novel Dataset Generation Strategy and a Multi-Period Farmland Cultivation Zones Dataset from Unmanned Aerial Vehicle Imagery. Agriculture, 16(1), 32. https://doi.org/10.3390/agriculture16010032

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