DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images
Maize tassels are critical phenotypic organs in maize, and their quantity is essential for determining tasseling stages, estimating yield potential, monitoring growth status, and supporting crop breeding programs. However, tassel identification in complex field environments presents significant chal...
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MDPI AG
2025-06-01
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| author | Dongbin Liu Jiandong Fang Yudong Zhao |
| author_facet | Dongbin Liu Jiandong Fang Yudong Zhao |
| author_sort | Dongbin Liu |
| collection | DOAJ |
| description | Maize tassels are critical phenotypic organs in maize, and their quantity is essential for determining tasseling stages, estimating yield potential, monitoring growth status, and supporting crop breeding programs. However, tassel identification in complex field environments presents significant challenges due to occlusion, variable lighting conditions, multi-scale target complexities, and the asynchronous and irregular growth patterns characteristic of maize tassels. In response to these challenges, this paper presents a DMSF-YOLO model for maize tassel detection. In the network’s backbone front, conventional convolutions are replaced with conditional parameter convolutions (CondConv) to enhance feature extraction capabilities. A novel DMSF-P2 network architecture is designed, including a multi-scale fusion module (SSFF-D), a scale-splicing module (TFE), and a small object detection layer (P2), which further enhances the model’s feature fusion capabilities. By integrating a dynamic detection head (Dyhead), superior recognition accuracy for maize tassels across various scales is achieved. Additionally, the Wise-IoU loss function is used to improve localization precision and strengthen the model’s adaptability. Experimental results demonstrate that on our self-built maize tassel detection dataset, the proposed DMSF-YOLO model shows remarkable superiority compared with the baseline YOLOv8n model, with precision (P), recall (R), <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>50</mn></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mrow><mn>50</mn><mo>:</mo><mn>95</mn></mrow></msub></mrow></semantics></math></inline-formula> increasing by 0.5%, 3.4%, 2.4%, and 3.9%, respectively. This approach enables accurate and reliable maize tassel detection in complex field environments, providing effective technical support for precision field management of maize crops. |
| format | Article |
| id | doaj-art-b03f6f7042d249968c8462987195f7b1 |
| institution | Matheson Library |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-b03f6f7042d249968c8462987195f7b12025-06-25T13:19:42ZengMDPI AGAgriculture2077-04722025-06-011512125910.3390/agriculture15121259DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing ImagesDongbin Liu0Jiandong Fang1Yudong Zhao2College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaInner Mongolia Key Laboratory of Intelligent Perception and System Engineering, Hohhot 010080, ChinaMaize tassels are critical phenotypic organs in maize, and their quantity is essential for determining tasseling stages, estimating yield potential, monitoring growth status, and supporting crop breeding programs. However, tassel identification in complex field environments presents significant challenges due to occlusion, variable lighting conditions, multi-scale target complexities, and the asynchronous and irregular growth patterns characteristic of maize tassels. In response to these challenges, this paper presents a DMSF-YOLO model for maize tassel detection. In the network’s backbone front, conventional convolutions are replaced with conditional parameter convolutions (CondConv) to enhance feature extraction capabilities. A novel DMSF-P2 network architecture is designed, including a multi-scale fusion module (SSFF-D), a scale-splicing module (TFE), and a small object detection layer (P2), which further enhances the model’s feature fusion capabilities. By integrating a dynamic detection head (Dyhead), superior recognition accuracy for maize tassels across various scales is achieved. Additionally, the Wise-IoU loss function is used to improve localization precision and strengthen the model’s adaptability. Experimental results demonstrate that on our self-built maize tassel detection dataset, the proposed DMSF-YOLO model shows remarkable superiority compared with the baseline YOLOv8n model, with precision (P), recall (R), <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>50</mn></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mrow><mn>50</mn><mo>:</mo><mn>95</mn></mrow></msub></mrow></semantics></math></inline-formula> increasing by 0.5%, 3.4%, 2.4%, and 3.9%, respectively. This approach enables accurate and reliable maize tassel detection in complex field environments, providing effective technical support for precision field management of maize crops.https://www.mdpi.com/2077-0472/15/12/1259UAVmaize tasselYOLOv8deep learningmulti-scale targettarget detection |
| spellingShingle | Dongbin Liu Jiandong Fang Yudong Zhao DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images Agriculture UAV maize tassel YOLOv8 deep learning multi-scale target target detection |
| title | DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images |
| title_full | DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images |
| title_fullStr | DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images |
| title_full_unstemmed | DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images |
| title_short | DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images |
| title_sort | dmsf yolo a dynamic multi scale fusion method for maize tassel detection in uav low altitude remote sensing images |
| topic | UAV maize tassel YOLOv8 deep learning multi-scale target target detection |
| url | https://www.mdpi.com/2077-0472/15/12/1259 |
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