Evidence map›Paper›PMID 34883932›Full record

ArticleSensors (Basel, Switzerland)2021

Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization.

Jianqiang Lu, Weize Lin, Pingfu Chen, Yubin Lan, Xiaoling Deng, Hongyu Niu, Jiawei Mo, Jiaxing Li, Shengfu Luo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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

9 authors.

Jianqiang LuSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.ORCID 0000-0002-6417-4646
Weize LinSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
Pingfu ChenSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
Yubin LanSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
Xiaoling DengSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.ORCID 0000-0001-5588-3443
Hongyu NiuSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
Jiawei MoSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
Jiaxing LiSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
Shengfu LuoSchool College of Electronic Engineering and School College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

At present, learning-based citrus blossom recognition models based on deep learning are highly complicated and have a large number of parameters. In order to estimate citrus flower quantities in natural orchards, this study proposes a lightweight citrus flower recognition model based on improved YOLOv4. In order to compress the backbone network, we utilize MobileNetv3 as a feature extractor, combined with deep separable convolution for further acceleration. The Cutout data enhancement method is also introduced to simulate citrus in nature for data enhancement. The test results show that the improved model has an mAP of 84.84%, 22% smaller than that of YOLOv4, and approximately two times faster. Compared with the Faster R-CNN, the improved citrus flower rate statistical model proposed in this study has the advantages of less memory usage and fast detection speed under the premise of ensuring a certain accuracy. Therefore, our solution can be used as a reference for the edge detection of citrus flowering.

Indexed as

CitrusNeural Networks, ComputerAlgorithmsCluster AnalysisModels, Statisticalcitrus flowering ratedeep learningedge computinglight weightYOLOv4

Identifiers

PMID34883932
PMCPMC8659452

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.