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Convolutional Neural Network-Based Estimation of Nitrogen Content in Regenerating Rice Leaves

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成果类型:
期刊论文
作者:
Hu, Tian;Liu, Zhihua;Hu, Rong;Tian, Mi;Wang, Zhiwei;...
通讯作者:
Li, M;Chen, GH
作者机构:
[Liu, Zhihua; Li, Ming; Li, M; Hu, Tian; Wang, Zhiwei] Hunan Acad Agr Sci, Hunan Agr Equipment Res Inst, Changsha 410011, Peoples R China.
[Hu, Rong] Hunan Agr Univ, Sch Food Sci & Technol, Changsha 410128, Peoples R China.
[Tian, Mi] Hunan Univ Humanities Sci & Technol, Sch Educ, Loudi 417000, Peoples R China.
[Chen, Guanghui] Hunan Agr Univ, Coll Agron, Changsha 410128, Peoples R China.
通讯机构:
[Chen, GH ; Li, M ] H
Hunan Acad Agr Sci, Hunan Agr Equipment Res Inst, Changsha 410011, Peoples R China.
Hunan Agr Univ, Coll Agron, Changsha 410128, Peoples R China.
语种:
英文
关键词:
nitrogen content prediction;regenerating rice;hyperspectral imaging;convolutional neural network
期刊:
Agronomy
ISSN:
2073-4395
年:
2024
卷:
14
期:
7
页码:
1422-
基金类别:
Hunan Agricultural Science and Technology Innovation Project [2023-CX-118]
机构署名:
本校为通讯机构
院系归属:
农学院
食品科学技术学院
摘要:
Regenerated rice, characterized by single planting and double harvesting, saves labor and costs, significantly contributing to global food security. Hyperspectral imaging technology, which integrates image and spectral data, provides comprehensive, non-destructive, and pollution-free vegetation canopy analysis, making it highly effective for crop nutrient diagnosis. In this study, we selected two varieties of regenerated rice for field trials. Hyperspectral images were captured during key growth stages (flush, grouting, and ripening) of both the first and regenerated seasons. Utilizing a two-d...

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