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Rice Counting and Localization in Unmanned Aerial Vehicle Imagery Using Enhanced Feature Fusion

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成果类型:
期刊论文
作者:
Yue Shen*;Rui Zhang;Wei Li;Li Chen;Haojie Zou;...
通讯作者:
Yue Shen
作者机构:
[Li Chen] Hunan Sureserve Technology Co., Ltd., Changsha 410000, China
[Rui Zhang; Wei Li; Haojie Zou; Sha Yang; Mingwei Yao; Zijie Qiu] College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
Author to whom correspondence should be addressed.
[Yue Shen] College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China<&wdkj&>Author to whom correspondence should be addressed.
通讯机构:
[Yue Shen] C
College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China<&wdkj&>Author to whom correspondence should be addressed.
语种:
英文
关键词:
rice counting;rice localization;feature fusion;attention mechanism;UAV;neural network
期刊:
Agronomy
ISSN:
2073-4395
年:
2024
卷:
14
期:
4
页码:
868-
基金类别:
Conceptualization, M.Y. and Y.S.; methodology, M.Y.; software, M.Y.; validation, M.Y., Y.S. and W.L.; formal analysis, M.Y., Y.S. and W.L.; investigation, M.Y.; resources, Y.S. and L.C.; data curation, M.Y.; writing—original draft preparation, M.Y.; writing—review and editing, M.Y., Y.S., W.L., H.Z., R.Z., L.C., Z.Q. and S.Y.; visualization, M.Y.; supervision, L.C., Y.S. and W.L.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript. This research was supported by the following programs: Hunan Province Key RD Plan Project (2023NK2011), Changsha Science and Technology Major Project (kh2103001), and Scientific research project of Hunan Provincial Department of Education (22B0204).
机构署名:
本校为通讯机构
摘要:
In rice cultivation and breeding, obtaining accurate information on the quantity and spatial distribution of rice plants is crucial. However, traditional field sampling methods can only provide rough estimates of the plant count and fail to capture precise plant locations. To address these problems, this paper proposes P2PNet-EFF for the counting and localization of rice plants. Firstly, through the introduction of the enhanced feature fusion (EFF), the model improves its ability to integrate deep semantic information while preserving shallow spatial details. This allows the model to holistica...

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