Evidence map›Paper›PMID 37570960›Full record

ArticlePlants (Basel, Switzerland)2023

HSSNet: A End-to-End Network for Detecting Tiny Targets of Apple Leaf Diseases in Complex Backgrounds.

Xing Gao, Zhiwen Tang, Yubao Deng, Shipeng Hu, Hongmin Zhao, Guoxiong Zhou

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
6.0field-weighted citation impact, top 5% of its field
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

3 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

6 authors at 1 institution in 1 country.

Xing GaoCollege of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Zhiwen TangCollege of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Yubao DengCollege of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Shipeng HuCollege of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Hongmin ZhaoCollege of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Guoxiong ZhouCollege of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.ORCID 0000-0002-5142-4845
Central South University of Forestry and Technology · CN

Funding

Changsha Municipal Natural Science Foundation Grant No. kq2014160Natural Science Foundation of China Grant No.61902436Scientific Research Project of Education Department of Hunan Province Grant No. 21A0179
6 · The paper itself

Abstract

Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel

Indexed as

apple leaf diseasescomplex backgroundHSSNettiny-object detectionTTALDD-4

Identifiers

PMID37570960
PMCPMC10420854
OpenAlexW4385421384

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.