Evidence map›Paper›PMID 37965019›Full record

ArticleFrontiers in plant science2023

Hydroponic lettuce defective leaves identification based on improved YOLOv5s.

Xin Jin, Haowei Jiao, Chao Zhang, Mingyong Li, Bo Zhao, Guowei Liu, Jiangtao Ji

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Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xin JinCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China.
Haowei JiaoCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China.
Chao ZhangCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China.
Mingyong LiCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China.
Bo ZhaoState Key Laboratory of Soil - Plant - Machine System Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing, China.
Guowei LiuEponic Agriculture Co., Ltd, Zhuhai, China.
Jiangtao JiCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Achieving intelligent detection of defective leaves of hydroponic lettuce after harvesting is of great significance for ensuring the quality and value of hydroponic lettuce. In order to improve the detection accuracy and efficiency of hydroponic lettuce defective leaves, firstly, an image acquisition system is designed and used to complete image acquisition for defective leaves of hydroponic lettuce. Secondly, this study proposed EBG_YOLOv5 model which optimized the YOLOv5 model by integrating the attention mechanism ECA in the backbone and introducing bidirectional feature pyramid and GSConv modules in the neck. Finally, the performance of the improved model was verified by ablation experiments and comparison experiments. The experimental results proved that, the Precision, Recall rate and mAP

Indexed as

BiFPNdefect detectionEBG_YOLOv5ECAGSConv

Identifiers

PMID37965019
PMCPMC10641003

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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.