FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields
Effective management of malignant weeds is critical to soybean growth. This study focuses on addressing the critical challenges of targeted spraying operations for malignant weeds such as <i>Cirsium setosum</i>, which severely threaten soybean yield in soybean fields. Specifically, this...
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MDPI AG
2024-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/12/2357 |
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| author | Zishang Yang Lele Wang Chenxu Li He Li |
| author_facet | Zishang Yang Lele Wang Chenxu Li He Li |
| author_sort | Zishang Yang |
| collection | DOAJ |
| description | Effective management of malignant weeds is critical to soybean growth. This study focuses on addressing the critical challenges of targeted spraying operations for malignant weeds such as <i>Cirsium setosum</i>, which severely threaten soybean yield in soybean fields. Specifically, this research aims to tackle key issues in plant protection operations, including the precise identification of weeds, the lightweight deployment of segmentation models, real-time requirements for spraying operations, and the generalization ability of models in diverse field environments. To address these challenges, this study proposes an improved weed instance segmentation model based on YOLOv8s-Seg, named FCB-YOLOv8s-Seg, for targeted spraying operations in soybean fields. The FCB-YOLOv8s-Seg model incorporates a lightweight backbone network to accelerate computations and reduce model size, with optimized Squeeze-and-Excitation Networks (SENet) and Bidirectional Feature Pyramid Network (BiFPN) modules integrated into the neck network to enhance weed recognition accuracy. Data collected from real soybean field scenes were used for model training and testing. The results of ablation experiments revealed that the FCB-YOLOv8s-Seg model achieved a mean average precision of 95.18% for bounding box prediction and 96.63% for segmentation, marking an increase of 5.08% and 7.43% over the original YOLOv8s-Seg model. While maintaining a balanced model scale, the object detection and segmentation accuracy of this model surpass other existing classic models such as YOLOv5s-Seg, Mask-RCNN, and YOLACT. The detection results in different scenes show that the FCB-YOLOv8s-Seg model performs well in fine-grained feature segmentation in complex scenes. Compared with several existing classical models, the FCB-YOLOv8s-Seg model demonstrates better performance. Additionally, field tests on plots with varying weed densities and operational speeds indicated an average segmentation rate of 91.30%, which is 6.38% higher than the original model. The proposed algorithm shows higher accuracy and performance in practical field instance segmentation tasks and is expected to provide strong technical support for promoting targeted spray operations. |
| format | Article |
| id | doaj-art-e604638965b74f14b38f9e74ee9e67e8 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-e604638965b74f14b38f9e74ee9e67e82024-12-27T14:03:28ZengMDPI AGAgriculture2077-04722024-12-011412235710.3390/agriculture14122357FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean FieldsZishang Yang0Lele Wang1Chenxu Li2He Li3College of Mechanical and Electrical Engineering, Henan Agriculture University, Zhengzhou 450002, ChinaCollege of Mechanical and Electrical Engineering, Henan Agriculture University, Zhengzhou 450002, ChinaCollege of Mechanical and Electrical Engineering, Henan Agriculture University, Zhengzhou 450002, ChinaCollege of Mechanical and Electrical Engineering, Henan Agriculture University, Zhengzhou 450002, ChinaEffective management of malignant weeds is critical to soybean growth. This study focuses on addressing the critical challenges of targeted spraying operations for malignant weeds such as <i>Cirsium setosum</i>, which severely threaten soybean yield in soybean fields. Specifically, this research aims to tackle key issues in plant protection operations, including the precise identification of weeds, the lightweight deployment of segmentation models, real-time requirements for spraying operations, and the generalization ability of models in diverse field environments. To address these challenges, this study proposes an improved weed instance segmentation model based on YOLOv8s-Seg, named FCB-YOLOv8s-Seg, for targeted spraying operations in soybean fields. The FCB-YOLOv8s-Seg model incorporates a lightweight backbone network to accelerate computations and reduce model size, with optimized Squeeze-and-Excitation Networks (SENet) and Bidirectional Feature Pyramid Network (BiFPN) modules integrated into the neck network to enhance weed recognition accuracy. Data collected from real soybean field scenes were used for model training and testing. The results of ablation experiments revealed that the FCB-YOLOv8s-Seg model achieved a mean average precision of 95.18% for bounding box prediction and 96.63% for segmentation, marking an increase of 5.08% and 7.43% over the original YOLOv8s-Seg model. While maintaining a balanced model scale, the object detection and segmentation accuracy of this model surpass other existing classic models such as YOLOv5s-Seg, Mask-RCNN, and YOLACT. The detection results in different scenes show that the FCB-YOLOv8s-Seg model performs well in fine-grained feature segmentation in complex scenes. Compared with several existing classical models, the FCB-YOLOv8s-Seg model demonstrates better performance. Additionally, field tests on plots with varying weed densities and operational speeds indicated an average segmentation rate of 91.30%, which is 6.38% higher than the original model. The proposed algorithm shows higher accuracy and performance in practical field instance segmentation tasks and is expected to provide strong technical support for promoting targeted spray operations.https://www.mdpi.com/2077-0472/14/12/2357targeted sprayingweed recognitioninstance segmentationYOLOv8-SegFasterNet |
| spellingShingle | Zishang Yang Lele Wang Chenxu Li He Li FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields Agriculture targeted spraying weed recognition instance segmentation YOLOv8-Seg FasterNet |
| title | FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields |
| title_full | FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields |
| title_fullStr | FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields |
| title_full_unstemmed | FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields |
| title_short | FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields |
| title_sort | fcb yolov8s seg a malignant weed instance segmentation model for targeted spraying in soybean fields |
| topic | targeted spraying weed recognition instance segmentation YOLOv8-Seg FasterNet |
| url | https://www.mdpi.com/2077-0472/14/12/2357 |
| work_keys_str_mv | AT zishangyang fcbyolov8ssegamalignantweedinstancesegmentationmodelfortargetedsprayinginsoybeanfields AT lelewang fcbyolov8ssegamalignantweedinstancesegmentationmodelfortargetedsprayinginsoybeanfields AT chenxuli fcbyolov8ssegamalignantweedinstancesegmentationmodelfortargetedsprayinginsoybeanfields AT heli fcbyolov8ssegamalignantweedinstancesegmentationmodelfortargetedsprayinginsoybeanfields |