Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s
In order to solve the problems of high planting density, similar color, and serious occlusion between spikes in sorghum fields, such as difficult identification and detection of sorghum spikes, low accuracy and high false detection, and missed detection rates, this study proposes an improved sorghum...
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2024-12-01
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| author | Shujin Qiu Jian Gao Mengyao Han Qingliang Cui Xiangyang Yuan Cuiqing Wu |
| author_facet | Shujin Qiu Jian Gao Mengyao Han Qingliang Cui Xiangyang Yuan Cuiqing Wu |
| author_sort | Shujin Qiu |
| collection | DOAJ |
| description | In order to solve the problems of high planting density, similar color, and serious occlusion between spikes in sorghum fields, such as difficult identification and detection of sorghum spikes, low accuracy and high false detection, and missed detection rates, this study proposes an improved sorghum spike detection method based on YOLOv8s. The method involves augmenting the information fusion capability of the YOLOv8 model’s neck module by integrating the Gold feature pyramid module. Additionally, the SPPF module is refined with the LSKA attention mechanism to heighten focus on critical features. To tackle class imbalance in sorghum detection and expedite model convergence, a loss function incorporating Focal-EIOU is employed. Consequently, the YOLOv8s-Gold-LSKA model, based on the Gold module and LSKA attention mechanism, is developed. Experimental results demonstrate that this improved method significantly enhances sorghum spike detection accuracy in natural field settings. The improved model achieved a precision of 90.72%, recall of 76.81%, mean average precision (mAP) of 85.86%, and an F1-score of 81.19%. Comparing the improved model of this study with the three target detection models of YOLOv5s, SSD, and YOLOv8, respectively, the improved model of this study has better detection performance. This advancement provides technical support for the rapid and accurate recognition of multiple sorghum spike targets in natural field backgrounds, thereby improving sorghum yield estimation accuracy. It also contributes to increased sorghum production and harvest, as well as the enhancement of intelligent harvesting equipment for agricultural machinery. |
| format | Article |
| id | doaj-art-7bda9c41ec5445b682ff42e9484b9840 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-7bda9c41ec5445b682ff42e9484b98402025-01-10T13:20:53ZengMDPI AGSensors1424-82202024-12-0125110410.3390/s25010104Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8sShujin Qiu0Jian Gao1Mengyao Han2Qingliang Cui3Xiangyang Yuan4Cuiqing Wu5College of Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Agriculture, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaIn order to solve the problems of high planting density, similar color, and serious occlusion between spikes in sorghum fields, such as difficult identification and detection of sorghum spikes, low accuracy and high false detection, and missed detection rates, this study proposes an improved sorghum spike detection method based on YOLOv8s. The method involves augmenting the information fusion capability of the YOLOv8 model’s neck module by integrating the Gold feature pyramid module. Additionally, the SPPF module is refined with the LSKA attention mechanism to heighten focus on critical features. To tackle class imbalance in sorghum detection and expedite model convergence, a loss function incorporating Focal-EIOU is employed. Consequently, the YOLOv8s-Gold-LSKA model, based on the Gold module and LSKA attention mechanism, is developed. Experimental results demonstrate that this improved method significantly enhances sorghum spike detection accuracy in natural field settings. The improved model achieved a precision of 90.72%, recall of 76.81%, mean average precision (mAP) of 85.86%, and an F1-score of 81.19%. Comparing the improved model of this study with the three target detection models of YOLOv5s, SSD, and YOLOv8, respectively, the improved model of this study has better detection performance. This advancement provides technical support for the rapid and accurate recognition of multiple sorghum spike targets in natural field backgrounds, thereby improving sorghum yield estimation accuracy. It also contributes to increased sorghum production and harvest, as well as the enhancement of intelligent harvesting equipment for agricultural machinery.https://www.mdpi.com/1424-8220/25/1/104sorghum spike detectionYOLOv8sGold moduleLSKA attention mechanism |
| spellingShingle | Shujin Qiu Jian Gao Mengyao Han Qingliang Cui Xiangyang Yuan Cuiqing Wu Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s Sensors sorghum spike detection YOLOv8s Gold module LSKA attention mechanism |
| title | Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s |
| title_full | Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s |
| title_fullStr | Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s |
| title_full_unstemmed | Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s |
| title_short | Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s |
| title_sort | sorghum spike detection method based on gold feature pyramid module and improved yolov8s |
| topic | sorghum spike detection YOLOv8s Gold module LSKA attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/1/104 |
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