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Intelligent Recognition Method of Rice Growth Stage Based on Multimodal Fusion
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Yongwei XU1, Shuhua LIU2, Zelin FENG1, 3, Ju LUO2, Zhaoxin NI1, Baojun YANG2, Qing YAO1, Agen LI4, *
Chinese Journal of Rice Science | 2026, 40(5) : 663 - 678
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Chinese Journal of Rice Science | 2026, 40(5): 663-678
• Research Papers •
Intelligent Recognition Method of Rice Growth Stage Based on Multimodal Fusion
Full
Yongwei XU1, Shuhua LIU2, Zelin FENG1, 3, Ju LUO2, Zhaoxin NI1, Baojun YANG2, Qing YAO1, Agen LI4, *
Affiliations
  • 1College of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
  • 2China National Rice Research Institute, Hangzhou 311401, China
  • 3School of Information and Control, Zhejiang Sci-Tech University, Hangzhou 312369, China
  • 4Yuhang Agro-ecological Environment & Crop Protection Service Station, Hangzhou 311100, China
Published: 2026-09-10 doi: 10.16819/j.1001-7216.2026.250703
Outline
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Objective

The growth period of rice is an important reference indicator for irrigation, fertilization, and pest control measures in rice planting management. Researching an intelligent recognition method for rice growth period based on multimodal fusion, improving the accuracy of rice growth period recognition, is of great significance for optimizing rice planting management measures and increasing rice yield.

Method

To address the vulnerability of RGB images to lighting, an H-color channel image is extracted, and a dual-branch rice growth stage image recognition model based on residual network and attention mechanism is established; Due to the influence of meteorological factors on the length of rice growth period, a rice growth period meteorological identification model based on three meteorological factors and a bidirectional long-short period network is established; Using decision trees for decision-level multimodal feature fusion, multiple models were trained and tested on a self-built rice growth period dataset.

Result

The improved rice growth period image recognition model achieved an accuracy of 84.25%, which is superior to traditional image recognition models such as ResNet50 and ConvNeXt. The multi-modality model based on images and meteorological achieved an accuracy of 87.66% in identifying 9 rice growth stages, which is superior to the single modality recognition model based on images and meteorology.

Conclusion

The multimodal recognition model based on image and meteorological fusion compensates for the shortcomings of single modal recognition methods. Compared with single modal recognition models, it has significantly improved the accuracy of identifying 9 rice growth stages.

rice growth period  /  multimodal  /  image modality  /  meteorological modality  /  decision tree
许永炜, 刘淑华, 冯泽霖, 罗举, 倪兆新, 杨保军, 姚青, 李阿根. 基于多模态融合的水稻生育期智能识别方法. 中国水稻科学, 2026 , 40 (5) : 663 -678 . DOI: 10.16819/j.1001-7216.2026.250703
Yongwei XU, Shuhua LIU, Zelin FENG, Ju LUO, Zhaoxin NI, Baojun YANG, Qing YAO, Agen LI. Intelligent Recognition Method of Rice Growth Stage Based on Multimodal Fusion[J]. Chinese Journal of Rice Science, 2026 , 40 (5) : 663 -678 . DOI: 10.16819/j.1001-7216.2026.250703
Year 2026 volume 40 Issue 5
105
21
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Article Info
doi: 10.16819/j.1001-7216.2026.250703
  • Receive Date:2025-07-10
  • Online Date:2026-09-29
  • Published:2026-09-10
Article Data
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History
  • Received:2025-07-10
  • Revised:2025-09-09
Funding
Affiliations
    1College of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
    2China National Rice Research Institute, Hangzhou 311401, China
    3School of Information and Control, Zhejiang Sci-Tech University, Hangzhou 312369, China
    4Yuhang Agro-ecological Environment & Crop Protection Service Station, Hangzhou 311100, China

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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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