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RiceStageSeg: A Multimodal Benchmark Dataset for Semantic Segmentation of Rice Growth Stages

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Publicado en:Remote Sensing vol. 17, no. 16 (2025), p. 2858-2880
Autor principal: Zhang, Jianping
Otros Autores: Chen Tailai, Li, Yizhe, Meng Qi, Chen Yanying, Deng Jie, Sun Enhong
Publicado:
MDPI AG
Materias:
Accuracy
Deep learning
Agricultural production
Datasets
Growth stage
Corn
Remote sensing
Phenology
Crops
Data integration
Semantic segmentation
Monitoring
Time series
Benchmarks
Vegetation
Spatial data
Crop growth
Image segmentation
Unmanned aerial vehicles
Precision agriculture
Rice
Image acquisition
Methods
Algorithms
Crop management
Multisensor fusion
Semantics
Acceso en línea:Citation/Abstract
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Resumen: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).
ISSN:2072-4292
DOI:10.3390/rs17162858
Fuente:Advanced Technologies & Aerospace Database