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Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device
Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus d...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747137/ https://www.ncbi.nlm.nih.gov/pubmed/35009602 http://dx.doi.org/10.3390/s22010059 |
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author | Huang, Heqing Huang, Tongbin Li, Zhen Lyu, Shilei Hong, Tao |
author_facet | Huang, Heqing Huang, Tongbin Li, Zhen Lyu, Shilei Hong, Tao |
author_sort | Huang, Heqing |
collection | PubMed |
description | Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model’s performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield. |
format | Online Article Text |
id | pubmed-8747137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87471372022-01-11 Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device Huang, Heqing Huang, Tongbin Li, Zhen Lyu, Shilei Hong, Tao Sensors (Basel) Article Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model’s performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield. MDPI 2021-12-23 /pmc/articles/PMC8747137/ /pubmed/35009602 http://dx.doi.org/10.3390/s22010059 Text en © 2021 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 | Article Huang, Heqing Huang, Tongbin Li, Zhen Lyu, Shilei Hong, Tao Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device |
title | Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device |
title_full | Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device |
title_fullStr | Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device |
title_full_unstemmed | Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device |
title_short | Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device |
title_sort | design of citrus fruit detection system based on mobile platform and edge computer device |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747137/ https://www.ncbi.nlm.nih.gov/pubmed/35009602 http://dx.doi.org/10.3390/s22010059 |
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