<b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b>
<p dir="ltr">Addressing the complexity and diversity of field symptoms related to citrus Huanglong disease (HLD), which significantly impacts yield and quality, while also tackling the challenges associated with the low efficiency and inadequate accuracy of traditional detection meth...
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2024
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| _version_ | 1852024398270169088 |
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| author | yizong wang (20387247) |
| author_facet | yizong wang (20387247) |
| author_role | author |
| dc.creator.none.fl_str_mv | yizong wang (20387247) |
| dc.date.none.fl_str_mv | 2024-12-16T14:49:23Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.28034312.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/_b_A_study_on_an_efficient_citrus_Huanglong_disease_detection_algorithm_based_on_three-channel_aggregated_attention_b_/28034312 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Image processing YOLOv8 citrus huanglong disease c2f Attention IRMB module powerneck module lightweighting Intelligent Disease Detection |
| dc.title.none.fl_str_mv | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p dir="ltr">Addressing the complexity and diversity of field symptoms related to citrus Huanglong disease (HLD), which significantly impacts yield and quality, while also tackling the challenges associated with the low efficiency and inadequate accuracy of traditional detection methods, this study introduces a field symptom detection model based on an enhanced version of YOLOv8. Initially, image enhancement techniques and other preprocessing steps were applied to samples of citrus Huanglong disease collected from various locations in Fujian Province to improve the model's capability to detect HLB in complex environments. Subsequently, a novel C2f Attention IRMB module was developed to replace the original cross-stage local layer convolution C2f, thereby providing a more comprehensive consideration of target position and size differences, improving target localization, and enhancing the model's ability to extract relevant features of citrus Huanglong disease while achieving model lightweighting. Additionally, a three-channel aggregated attention module was integrated into the Neck Powerneck, effectively capturing richer global and local image information, fostering interaction and fusion among different feature layers, and reducing redundancy and repeated computations to enhance the overall efficiency and performance of the model. The model's capacity to express features of citrus Huanglong disease was further strengthened by embedding a finely designed attention mechanism. Lastly, an efficient detection head was constructed to accelerate the inference process. Experimental results demonstrate that on a dataset comprising 12 diseases and 2 healthy states, the mean Average Precision at IoU 0.50 (mAP50) of this model achieved 97%, with a Precision of 91.5%; these figures reflect improvements of 1.1% and 1.2%, respectively, compared to the original YOLOv8 algorithm. Additionally, the inference speed increased by 14.6%, fully meeting the real-time requirements for detecting diseases in citrus fields and illustrating the effectiveness and advanced nature of the improved algorithm, thereby providing robust support for the rapid identification of diseases in the citrus cultivation process.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_e2e2d4541bcdee8f7295af2b4d2dd595 |
| identifier_str_mv | 10.6084/m9.figshare.28034312.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28034312 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b>yizong wang (20387247)Image processingYOLOv8citrus huanglong diseasec2f Attention IRMB modulepowerneck modulelightweightingIntelligent Disease Detection<p dir="ltr">Addressing the complexity and diversity of field symptoms related to citrus Huanglong disease (HLD), which significantly impacts yield and quality, while also tackling the challenges associated with the low efficiency and inadequate accuracy of traditional detection methods, this study introduces a field symptom detection model based on an enhanced version of YOLOv8. Initially, image enhancement techniques and other preprocessing steps were applied to samples of citrus Huanglong disease collected from various locations in Fujian Province to improve the model's capability to detect HLB in complex environments. Subsequently, a novel C2f Attention IRMB module was developed to replace the original cross-stage local layer convolution C2f, thereby providing a more comprehensive consideration of target position and size differences, improving target localization, and enhancing the model's ability to extract relevant features of citrus Huanglong disease while achieving model lightweighting. Additionally, a three-channel aggregated attention module was integrated into the Neck Powerneck, effectively capturing richer global and local image information, fostering interaction and fusion among different feature layers, and reducing redundancy and repeated computations to enhance the overall efficiency and performance of the model. The model's capacity to express features of citrus Huanglong disease was further strengthened by embedding a finely designed attention mechanism. Lastly, an efficient detection head was constructed to accelerate the inference process. Experimental results demonstrate that on a dataset comprising 12 diseases and 2 healthy states, the mean Average Precision at IoU 0.50 (mAP50) of this model achieved 97%, with a Precision of 91.5%; these figures reflect improvements of 1.1% and 1.2%, respectively, compared to the original YOLOv8 algorithm. Additionally, the inference speed increased by 14.6%, fully meeting the real-time requirements for detecting diseases in citrus fields and illustrating the effectiveness and advanced nature of the improved algorithm, thereby providing robust support for the rapid identification of diseases in the citrus cultivation process.</p>2024-12-16T14:49:23ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.28034312.v1https://figshare.com/articles/dataset/_b_A_study_on_an_efficient_citrus_Huanglong_disease_detection_algorithm_based_on_three-channel_aggregated_attention_b_/28034312CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/280343122024-12-16T14:49:23Z |
| spellingShingle | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> yizong wang (20387247) Image processing YOLOv8 citrus huanglong disease c2f Attention IRMB module powerneck module lightweighting Intelligent Disease Detection |
| status_str | publishedVersion |
| title | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> |
| title_full | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> |
| title_fullStr | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> |
| title_full_unstemmed | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> |
| title_short | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> |
| title_sort | <b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b> |
| topic | Image processing YOLOv8 citrus huanglong disease c2f Attention IRMB module powerneck module lightweighting Intelligent Disease Detection |