RiceStageSeg: A Multimodal Benchmark Dataset for Semantic Segmentation of Rice Growth Stages
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| Pubblicato in: | Remote Sensing vol. 17, no. 16 (2025), p. 2858-2880 |
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| Autore principale: | |
| Altri autori: | , , , , , |
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MDPI AG
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| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17162858 |2 doi | |
| 035 | |a 3244059286 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Zhang, Jianping |u Chongqing Institute of Meteorological Sciences, Chongqing 401147, China; zhangjp@cqsqxj.com (J.Z.); chenyy@cqsqxj.com (Y.C.) | |
| 245 | 1 | |a RiceStageSeg: A Multimodal Benchmark Dataset for Semantic Segmentation of Rice Growth Stages | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The accurate identification of rice growth stages is critical for precision agriculture, crop management, and yield estimation. Remote sensing technologies, particularly multimodal approaches that integrate high spatial and hyperspectral resolution imagery, have demonstrated great potential in large-scale crop monitoring. Multimodal data fusion offers complementary and enriched spectral–spatial information, providing novel pathways for crop growth stage recognition in complex agricultural scenarios. However, the lack of publicly available multimodal datasets specifically designed for rice growth stage identification remains a significant bottleneck that limits the development and evaluation of relevant methods. To address this gap, we present RiceStageSeg, a multimodal benchmark dataset captured by unmanned aerial vehicles (UAVs), designed to support the development and assessment of segmentation models for rice growth monitoring. RiceStageSeg contains paired centimeter-level RGB and 10-band multispectral (MS) images acquired during several critical rice growth stages, including jointing and heading. Each image is accompanied by fine-grained, pixel-level annotations that distinguish between the different growth stages. We establish baseline experiments using several state-of-the-art semantic segmentation models under both unimodal (RGB-only, MS-only) and multimodal (RGB + MS fusion) settings. The experimental results demonstrate that multimodal feature-level fusion outperforms unimodal approaches in segmentation accuracy. RiceStageSeg offers a standardized benchmark to advance future research in multimodal semantic segmentation for agricultural remote sensing. The dataset will be made publicly available on GitHub v0.11.0 (accessed on 1 August 2025). | |
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Agricultural production | ||
| 653 | |a Datasets | ||
| 653 | |a Growth stage | ||
| 653 | |a Corn | ||
| 653 | |a Remote sensing | ||
| 653 | |a Phenology | ||
| 653 | |a Crops | ||
| 653 | |a Data integration | ||
| 653 | |a Semantic segmentation | ||
| 653 | |a Monitoring | ||
| 653 | |a Time series | ||
| 653 | |a Benchmarks | ||
| 653 | |a Vegetation | ||
| 653 | |a Spatial data | ||
| 653 | |a Crop growth | ||
| 653 | |a Image segmentation | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Precision agriculture | ||
| 653 | |a Rice | ||
| 653 | |a Image acquisition | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Crop management | ||
| 653 | |a Multisensor fusion | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Chen Tailai |u College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2022308130511@cau.edu.cn (T.C.); yizhe.li@cau.edu.cn (Y.L.); mengqi@cau.edu.cn (Q.M.) | |
| 700 | 1 | |a Li, Yizhe |u College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2022308130511@cau.edu.cn (T.C.); yizhe.li@cau.edu.cn (Y.L.); mengqi@cau.edu.cn (Q.M.) | |
| 700 | 1 | |a Meng Qi |u College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2022308130511@cau.edu.cn (T.C.); yizhe.li@cau.edu.cn (Y.L.); mengqi@cau.edu.cn (Q.M.) | |
| 700 | 1 | |a Chen Yanying |u Chongqing Institute of Meteorological Sciences, Chongqing 401147, China; zhangjp@cqsqxj.com (J.Z.); chenyy@cqsqxj.com (Y.C.) | |
| 700 | 1 | |a Deng Jie |u College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; djcc@cau.edu.cn | |
| 700 | 1 | |a Sun Enhong |u Jiangjin Meteorologica Administration, Jiangjin Modern Agrometeorological Experimental Station of Chongqing, Chonqging 402260, China | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 16 (2025), p. 2858-2880 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244059286/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244059286/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244059286/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |