Hyperspectral Imaging: The Intelligent Eye to Uncover the Password of Plant Science

-д хадгалсан:
Номзүйн дэлгэрэнгүй
-д хэвлэсэн:Modern Agriculture vol. 3, no. 2 (Dec 1, 2025)
Үндсэн зохиолч: Song, Jingyan
Бусад зохиолчид: Liang, Haifeng, Lu, Bingjie, Guo, Jing, Gao, Yuan, Hu, Xiao, Yang, Manlin, Li, Xiaofan, Wang, Zhenyu, Chen, Yongqi, Zhang, Yinyin, Su, Shen, Gao, Zhangyun, Li, Shijie, Chen, Ping, Wang, Jing, Yang, Wanneng, Feng, Hui
Хэвлэсэн:
John Wiley & Sons, Inc.
Нөхцлүүд:
Онлайн хандалт:Citation/Abstract
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022 |a 2751-4102 
024 7 |a 10.1002/moda.70026  |2 doi 
035 |a 3276837663 
045 0 |b d20251201 
100 1 |a Song, Jingyan  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
245 1 |a Hyperspectral Imaging: The Intelligent Eye to Uncover the Password of Plant Science 
260 |b John Wiley & Sons, Inc.  |c Dec 1, 2025 
513 |a Journal Article 
520 3 |a ABSTRACT Plant phenomics has emerged as a critical bridge between genotype and phenotype, addressing a significant bottleneck in crop breeding and functional genomics studies. Hyperspectral imaging, a key technology in this field, has been instrumental in high‐throughput, non‐destructive phenotyping. Compared to other imaging technologies, hyperspectral imaging stands out for its continuous and fine spectral resolution, capturing subtle changes in plant biochemical and physiological states, which is essential for precise identification and analysis of plant characteristics. Recent advances in deep learning have further expedited hyperspectral data analysis, fostered multi‐omics research and enhanced our ability to integrate diverse datasets. Despite challenges in establishing standards of data acquisition and processing, a significant proposal has emerged for the scientific community to collaboratively build a vast hyperspectral database. Integrated with reducing the cost of hyperspectral sensors and promoting more open‐source analysis pipelines for hyperspectral data, these initiatives promise to lay the groundwork for robust big data analytics, potentially revolutionising plant research and breeding. 
653 |a Silicon 
653 |a Data acquisition 
653 |a Plant breeding 
653 |a Big Data 
653 |a Phenotypes 
653 |a Unmanned aerial vehicles 
653 |a Crops 
653 |a Genomics 
653 |a Genotypes 
653 |a Optics 
653 |a Deep learning 
653 |a Agriculture 
653 |a Data processing 
653 |a Data analysis 
653 |a Nanowires 
653 |a Fourier transforms 
653 |a Sensors 
653 |a Spectral resolution 
653 |a Plant sciences 
653 |a Light 
653 |a Hyperspectral imaging 
653 |a Environmental 
700 1 |a Liang, Haifeng  |u Shaanxi Province Key Laboratory of Thin Film Technology and Optical Test, School of Opto‐Electronic Engineering, Institute for Interdisciplinary and Innovation Research, Xi’an Technological University, Xi'an, China 
700 1 |a Lu, Bingjie  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Guo, Jing  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Gao, Yuan  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Hu, Xiao  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Yang, Manlin  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Li, Xiaofan  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Wang, Zhenyu  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Chen, Yongqi  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Zhang, Yinyin  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Su, Shen  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Gao, Zhangyun  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Li, Shijie  |u Shaanxi Province Key Laboratory of Thin Film Technology and Optical Test, School of Opto‐Electronic Engineering, Institute for Interdisciplinary and Innovation Research, Xi’an Technological University, Xi'an, China 
700 1 |a Chen, Ping  |u Shaanxi Province Key Laboratory of Thin Film Technology and Optical Test, School of Opto‐Electronic Engineering, Institute for Interdisciplinary and Innovation Research, Xi’an Technological University, Xi'an, China 
700 1 |a Wang, Jing  |u National Institute of Metrology, Beijing, China 
700 1 |a Yang, Wanneng  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
700 1 |a Feng, Hui  |u National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China 
773 0 |t Modern Agriculture  |g vol. 3, no. 2 (Dec 1, 2025) 
786 0 |d ProQuest  |t Agriculture Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3276837663/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3276837663/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3276837663/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch