YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery
Accurate detection of pest species in cotton fields is vital for effective agricultural management and the development of pest-resistant crops. However, achieving high-throughput and precise pest detection in cotton fields remains a challenging task. Although unmanned aerial vehicle (UAV) enable the...
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IEEE
2025-01-01
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| author | Chenglei Sun Afizan Bin Azman Zaiyun Wang Xiaoxiao Gao Kai Ding |
| author_facet | Chenglei Sun Afizan Bin Azman Zaiyun Wang Xiaoxiao Gao Kai Ding |
| author_sort | Chenglei Sun |
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| description | Accurate detection of pest species in cotton fields is vital for effective agricultural management and the development of pest-resistant crops. However, achieving high-throughput and precise pest detection in cotton fields remains a challenging task. Although unmanned aerial vehicle (UAV) enable the rapid acquisition of extensive crop images, detecting pests accurately from these images is difficult due to the small size of pests and background interference. This study introduces YOLO-PEST, a novel pest detection model based on YOLOv8n, utilizing a personal cotton field imagery dataset acquired by UAVs. YOLO-PEST incorporates a custom-designed SC3 module to enhance low-level feature extraction and employs the GeLU activation function to address the vanishing gradient issue. Additionally, the model optimizes the neck design to re-duce semantic discrepancies between feature layers, improving small-target detection, and integrates large-kernel separable convolutions to bolster high-level feature processing. Experimental results demonstrate that YOLO-PEST outperforms original YOLOv8n models, with a 3.46 percentage points increase in mAP50, a 5.16 percentage points increase in Precision, and a 7.81 percentage points increase in Recall. YOLO-PEST also shows superior Precision compared to DenseNet and FasterNet, with improvements of 1.14 and 6.13 percentage points, respectively. Overall, YOLO-PEST offers high accuracy and a compact parameter footprint, making it highly effective for pest detection in UAV-acquired crop images. |
| format | Article |
| id | doaj-art-b4359d25c91f41b083a834e782b9311d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b4359d25c91f41b083a834e782b9311d2025-01-31T23:05:19ZengIEEEIEEE Access2169-35362025-01-0113199371994510.1109/ACCESS.2025.352987310843195YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light ImageryChenglei Sun0https://orcid.org/0009-0004-5316-7979Afizan Bin Azman1https://orcid.org/0009-0001-8612-6020Zaiyun Wang2Xiaoxiao Gao3Kai Ding4School of Computer Science, Taylor’s University, Subang Jaya, Selangor, MalaysiaSchool of Computer Science, Taylor’s University, Subang Jaya, Selangor, MalaysiaDepartment of Electronics and Communications, Shandong Vocational College of Information Technology, Weifang, Shandong, ChinaDepartment of Animal Science and Technology, Shandong Vocational Animal Science and Veterinary College, Weifang, Shandong, ChinaDepartment of Digital and Media, Shandong Vocational College of Information Technology, Weifang, Shandong, ChinaAccurate detection of pest species in cotton fields is vital for effective agricultural management and the development of pest-resistant crops. However, achieving high-throughput and precise pest detection in cotton fields remains a challenging task. Although unmanned aerial vehicle (UAV) enable the rapid acquisition of extensive crop images, detecting pests accurately from these images is difficult due to the small size of pests and background interference. This study introduces YOLO-PEST, a novel pest detection model based on YOLOv8n, utilizing a personal cotton field imagery dataset acquired by UAVs. YOLO-PEST incorporates a custom-designed SC3 module to enhance low-level feature extraction and employs the GeLU activation function to address the vanishing gradient issue. Additionally, the model optimizes the neck design to re-duce semantic discrepancies between feature layers, improving small-target detection, and integrates large-kernel separable convolutions to bolster high-level feature processing. Experimental results demonstrate that YOLO-PEST outperforms original YOLOv8n models, with a 3.46 percentage points increase in mAP50, a 5.16 percentage points increase in Precision, and a 7.81 percentage points increase in Recall. YOLO-PEST also shows superior Precision compared to DenseNet and FasterNet, with improvements of 1.14 and 6.13 percentage points, respectively. Overall, YOLO-PEST offers high accuracy and a compact parameter footprint, making it highly effective for pest detection in UAV-acquired crop images.https://ieeexplore.ieee.org/document/10843195/UAVYOLOv8pest detectioncottonsmall target |
| spellingShingle | Chenglei Sun Afizan Bin Azman Zaiyun Wang Xiaoxiao Gao Kai Ding YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery IEEE Access UAV YOLOv8 pest detection cotton small target |
| title | YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery |
| title_full | YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery |
| title_fullStr | YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery |
| title_full_unstemmed | YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery |
| title_short | YOLO-UP: A High-Throughput Pest Detection Model for Dense Cotton Crops Utilizing UAV-Captured Visible Light Imagery |
| title_sort | yolo up a high throughput pest detection model for dense cotton crops utilizing uav captured visible light imagery |
| topic | UAV YOLOv8 pest detection cotton small target |
| url | https://ieeexplore.ieee.org/document/10843195/ |
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