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Hydroponic lettuce defective leaves identification based on improved YOLOv5s

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 acquisitio...

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Autores principales: Jin, Xin, Jiao, Haowei, Zhang, Chao, Li, Mingyong, Zhao, Bo, Liu, Guowei, Ji, Jiangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641003/
https://www.ncbi.nlm.nih.gov/pubmed/37965019
http://dx.doi.org/10.3389/fpls.2023.1242337
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author Jin, Xin
Jiao, Haowei
Zhang, Chao
Li, Mingyong
Zhao, Bo
Liu, Guowei
Ji, Jiangtao
author_facet Jin, Xin
Jiao, Haowei
Zhang, Chao
Li, Mingyong
Zhao, Bo
Liu, Guowei
Ji, Jiangtao
author_sort Jin, Xin
collection PubMed
description 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(0.5) of the EBG_YOLOv5 were 0.1%, 2.0% and 2.6% higher than those of YOLOv5s, respectively, while the model size, GFLOPs and Parameters are reduced by 15.3%, 18.9% and 16.3%. Meanwhile, the accuracy and model size of EBG_YOLOv5 were higher and smaller compared with other detection algorithms. This indicates that the EBG_YOLOv5 being applied to hydroponic lettuce defective leaves detection can achieve better performance. It can provide technical support for the subsequent research of lettuce intelligent nondestructive classification equipment.
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spelling pubmed-106410032023-11-14 Hydroponic lettuce defective leaves identification based on improved YOLOv5s Jin, Xin Jiao, Haowei Zhang, Chao Li, Mingyong Zhao, Bo Liu, Guowei Ji, Jiangtao Front Plant Sci Plant Science 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(0.5) of the EBG_YOLOv5 were 0.1%, 2.0% and 2.6% higher than those of YOLOv5s, respectively, while the model size, GFLOPs and Parameters are reduced by 15.3%, 18.9% and 16.3%. Meanwhile, the accuracy and model size of EBG_YOLOv5 were higher and smaller compared with other detection algorithms. This indicates that the EBG_YOLOv5 being applied to hydroponic lettuce defective leaves detection can achieve better performance. It can provide technical support for the subsequent research of lettuce intelligent nondestructive classification equipment. Frontiers Media S.A. 2023-10-26 /pmc/articles/PMC10641003/ /pubmed/37965019 http://dx.doi.org/10.3389/fpls.2023.1242337 Text en Copyright © 2023 Jin, Jiao, Zhang, Li, Zhao, Liu and Ji https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Jin, Xin
Jiao, Haowei
Zhang, Chao
Li, Mingyong
Zhao, Bo
Liu, Guowei
Ji, Jiangtao
Hydroponic lettuce defective leaves identification based on improved YOLOv5s
title Hydroponic lettuce defective leaves identification based on improved YOLOv5s
title_full Hydroponic lettuce defective leaves identification based on improved YOLOv5s
title_fullStr Hydroponic lettuce defective leaves identification based on improved YOLOv5s
title_full_unstemmed Hydroponic lettuce defective leaves identification based on improved YOLOv5s
title_short Hydroponic lettuce defective leaves identification based on improved YOLOv5s
title_sort hydroponic lettuce defective leaves identification based on improved yolov5s
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641003/
https://www.ncbi.nlm.nih.gov/pubmed/37965019
http://dx.doi.org/10.3389/fpls.2023.1242337
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