Cargando…

Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm

Presently, the quality of cherries in the market is uneven, because human senses are used to distinguish cherry quality, which consumes a lot of time and energy and does not achieve good results in terms of accuracy. If the internal quality indices, such as the PH value, sugar–acid ratio, and vitami...

Descripción completa

Detalles Bibliográficos
Autores principales: Han, Wei, Jiang, Fei, Zhu, Zhiyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025714/
https://www.ncbi.nlm.nih.gov/pubmed/35454714
http://dx.doi.org/10.3390/foods11081127
_version_ 1784690941489053696
author Han, Wei
Jiang, Fei
Zhu, Zhiyuan
author_facet Han, Wei
Jiang, Fei
Zhu, Zhiyuan
author_sort Han, Wei
collection PubMed
description Presently, the quality of cherries in the market is uneven, because human senses are used to distinguish cherry quality, which consumes a lot of time and energy and does not achieve good results in terms of accuracy. If the internal quality indices, such as the PH value, sugar–acid ratio, and vitamin C content, of cherries are extracted using chemical methods, the detection speed will decrease. With the development of artificial intelligence (AI), image processing by AI algorithms has attracted broad attention. The YOLOv5 model in the YOLO series has many advantages, such as high detection accuracy, fast speed, small size, and so on, and has been used in face recognition, image recognition and other fields. However, owing to the influence of seasonal weather, the environment and other factors, the dataset used in the training model decreases the accuracy of image recognition. To improve the accuracy, a large amount of data must be used for model training, but this will decrease the model training speed. Because it is impossible to use all data in training, there will inevitably be recognition errors in the detection process. In this study, the cherry images in a dataset were extracted by the flooding filling algorithm. The extracted cherry images were used as a new dataset for training and recognition, and the results were compared to those obtained with non-extracted images. The dataset generated by the flooding filling algorithm was used for model training. After 20 training epochs, the accuracy rate reached 99.6%. Without using the algorithm to extract images, the accuracy rate was only 78.6% after 300 training epochs.
format Online
Article
Text
id pubmed-9025714
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-90257142022-04-23 Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm Han, Wei Jiang, Fei Zhu, Zhiyuan Foods Communication Presently, the quality of cherries in the market is uneven, because human senses are used to distinguish cherry quality, which consumes a lot of time and energy and does not achieve good results in terms of accuracy. If the internal quality indices, such as the PH value, sugar–acid ratio, and vitamin C content, of cherries are extracted using chemical methods, the detection speed will decrease. With the development of artificial intelligence (AI), image processing by AI algorithms has attracted broad attention. The YOLOv5 model in the YOLO series has many advantages, such as high detection accuracy, fast speed, small size, and so on, and has been used in face recognition, image recognition and other fields. However, owing to the influence of seasonal weather, the environment and other factors, the dataset used in the training model decreases the accuracy of image recognition. To improve the accuracy, a large amount of data must be used for model training, but this will decrease the model training speed. Because it is impossible to use all data in training, there will inevitably be recognition errors in the detection process. In this study, the cherry images in a dataset were extracted by the flooding filling algorithm. The extracted cherry images were used as a new dataset for training and recognition, and the results were compared to those obtained with non-extracted images. The dataset generated by the flooding filling algorithm was used for model training. After 20 training epochs, the accuracy rate reached 99.6%. Without using the algorithm to extract images, the accuracy rate was only 78.6% after 300 training epochs. MDPI 2022-04-14 /pmc/articles/PMC9025714/ /pubmed/35454714 http://dx.doi.org/10.3390/foods11081127 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Han, Wei
Jiang, Fei
Zhu, Zhiyuan
Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm
title Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm
title_full Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm
title_fullStr Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm
title_full_unstemmed Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm
title_short Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm
title_sort detection of cherry quality using yolov5 model based on flood filling algorithm
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025714/
https://www.ncbi.nlm.nih.gov/pubmed/35454714
http://dx.doi.org/10.3390/foods11081127
work_keys_str_mv AT hanwei detectionofcherryqualityusingyolov5modelbasedonfloodfillingalgorithm
AT jiangfei detectionofcherryqualityusingyolov5modelbasedonfloodfillingalgorithm
AT zhuzhiyuan detectionofcherryqualityusingyolov5modelbasedonfloodfillingalgorithm