Evidence map›Paper›PMID 39119504›Full record

ArticleFrontiers in plant science2024

Enhancing kiwifruit flower pollination detection through frequency domain feature fusion: a novel approach to agricultural monitoring.

Fei Pan, Mengdie Hu, Xuliang Duan, Boda Zhang, Pengjun Xiang, Lan Jia, Xiaoyu Zhao, Dawei He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Fei Pan *College of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Mengdie Hu *College of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Xuliang DuanCollege of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Boda ZhangCollege of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Pengjun XiangCollege of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Lan JiaCollege of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Xiaoyu ZhaoCollege of Information Engineering, Sichuan Agricultural University, Ya'an, China.
Dawei HeCollege of Information Engineering, Sichuan Agricultural University, Ya'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pollination process of kiwifruit flowers plays a crucial role in kiwifruit yield. Achieving accurate and rapid identification of the four stages of kiwifruit flowers is essential for enhancing pollination efficiency. In this study, to improve the efficiency of kiwifruit pollination, we propose a novel full-stage kiwifruit flower pollination detection algorithm named KIWI-YOLO, based on the fusion of frequency-domain features. Our algorithm leverages frequency-domain and spatial-domain information to improve recognition of contour-detailed features and integrates decision-making with contextual information. Additionally, we incorporate the Bi-Level Routing Attention (BRA) mechanism with C3 to enhance the algorithm's focus on critical areas, resulting in accurate, lightweight, and fast detection. The algorithm achieves a

Indexed as

BRAfrequency domain feature fusionkiwifruit flowerprecision agriculture deletedYOLO

Identifiers

PMID39119504
PMCPMC11306074

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.