{"title":"向日葵-YOLO:无人机遥感图像中向日葵头状花序的检测","authors":"","doi":"10.1016/j.eja.2024.127332","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate identification and monitoring of sunflower capitula are crucial for field phenotypic analysis, cultivation management, phenological monitoring, and yield prediction. Manual observation, however, faces significant challenges due to the complexity of field environments and the morphological diversity of sunflower capitula. Unmanned Aerial Vehicles (UAVs) have emerged as an ideal platform for monitoring sunflower capitula due to their low cost and high spatiotemporal resolution. This study introduces Sunflower-YOLO, an enhanced model based on YOLOv7-tiny, designed for detecting sunflower capitula in UAV remote sensing images. The model effectively identifies sunflower capitula and distinguishes between three specific states: open, half-open, and bud. Sunflower-YOLO incorporates several key improvements: the SiLU activation function replaces the original LeakyReLU, enhancing the model’s nonlinear expression capability; a shallow high-resolution feature map and an additional detection head for small targets are introduced during the feature fusion stage to improve the detection performance of small capitula; and the integration of deformable convolution and the SimAM attention mechanism enhances the ELAN structure in the backbone, creating a new DeformAtt-ELAN structure that improves the model’s ability to capture morphological variations and reduces noise interference. Experimental results demonstrate that Sunflower-YOLO achieves precision, recall, and [email protected] of 92.3 %, 89.7 %, and 93 %, respectively, marking improvements of 4.2 %, 4.2 %, and 3.7 % over the original YOLOv7-tiny model. The average precision (AP) for the three growth states is 98.7 %, 93.4 %, and 87 %, with AP for the half-open and bud states improving by 6.5 % and 4.7 %, respectively. The model’s FLOPs is 17.7 G, its size is 13.8MB, and it achieves an FPS of 188.52. Compared to current mainstream state-of-the-art (SOTA) models for object detection, Sunflower-YOLO achieves the highest [email protected] in detecting multiple types of sunflower capitula. The constructed capitulum density map offers a practical view for observing sunflower growth status. This study highlights the immense potential of combining UAV remote sensing technology with YOLO object detection algorithms in monitoring sunflower capitula and their growth processes, providing an innovative and effective approach for precision agriculture practices.</p></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sunflower-YOLO: Detection of sunflower capitula in UAV remote sensing images\",\"authors\":\"\",\"doi\":\"10.1016/j.eja.2024.127332\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate identification and monitoring of sunflower capitula are crucial for field phenotypic analysis, cultivation management, phenological monitoring, and yield prediction. Manual observation, however, faces significant challenges due to the complexity of field environments and the morphological diversity of sunflower capitula. Unmanned Aerial Vehicles (UAVs) have emerged as an ideal platform for monitoring sunflower capitula due to their low cost and high spatiotemporal resolution. This study introduces Sunflower-YOLO, an enhanced model based on YOLOv7-tiny, designed for detecting sunflower capitula in UAV remote sensing images. The model effectively identifies sunflower capitula and distinguishes between three specific states: open, half-open, and bud. Sunflower-YOLO incorporates several key improvements: the SiLU activation function replaces the original LeakyReLU, enhancing the model’s nonlinear expression capability; a shallow high-resolution feature map and an additional detection head for small targets are introduced during the feature fusion stage to improve the detection performance of small capitula; and the integration of deformable convolution and the SimAM attention mechanism enhances the ELAN structure in the backbone, creating a new DeformAtt-ELAN structure that improves the model’s ability to capture morphological variations and reduces noise interference. Experimental results demonstrate that Sunflower-YOLO achieves precision, recall, and [email protected] of 92.3 %, 89.7 %, and 93 %, respectively, marking improvements of 4.2 %, 4.2 %, and 3.7 % over the original YOLOv7-tiny model. The average precision (AP) for the three growth states is 98.7 %, 93.4 %, and 87 %, with AP for the half-open and bud states improving by 6.5 % and 4.7 %, respectively. The model’s FLOPs is 17.7 G, its size is 13.8MB, and it achieves an FPS of 188.52. Compared to current mainstream state-of-the-art (SOTA) models for object detection, Sunflower-YOLO achieves the highest [email protected] in detecting multiple types of sunflower capitula. The constructed capitulum density map offers a practical view for observing sunflower growth status. This study highlights the immense potential of combining UAV remote sensing technology with YOLO object detection algorithms in monitoring sunflower capitula and their growth processes, providing an innovative and effective approach for precision agriculture practices.</p></div>\",\"PeriodicalId\":51045,\"journal\":{\"name\":\"European Journal of Agronomy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Agronomy\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1161030124002533\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Agronomy","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1161030124002533","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
Sunflower-YOLO: Detection of sunflower capitula in UAV remote sensing images
Accurate identification and monitoring of sunflower capitula are crucial for field phenotypic analysis, cultivation management, phenological monitoring, and yield prediction. Manual observation, however, faces significant challenges due to the complexity of field environments and the morphological diversity of sunflower capitula. Unmanned Aerial Vehicles (UAVs) have emerged as an ideal platform for monitoring sunflower capitula due to their low cost and high spatiotemporal resolution. This study introduces Sunflower-YOLO, an enhanced model based on YOLOv7-tiny, designed for detecting sunflower capitula in UAV remote sensing images. The model effectively identifies sunflower capitula and distinguishes between three specific states: open, half-open, and bud. Sunflower-YOLO incorporates several key improvements: the SiLU activation function replaces the original LeakyReLU, enhancing the model’s nonlinear expression capability; a shallow high-resolution feature map and an additional detection head for small targets are introduced during the feature fusion stage to improve the detection performance of small capitula; and the integration of deformable convolution and the SimAM attention mechanism enhances the ELAN structure in the backbone, creating a new DeformAtt-ELAN structure that improves the model’s ability to capture morphological variations and reduces noise interference. Experimental results demonstrate that Sunflower-YOLO achieves precision, recall, and [email protected] of 92.3 %, 89.7 %, and 93 %, respectively, marking improvements of 4.2 %, 4.2 %, and 3.7 % over the original YOLOv7-tiny model. The average precision (AP) for the three growth states is 98.7 %, 93.4 %, and 87 %, with AP for the half-open and bud states improving by 6.5 % and 4.7 %, respectively. The model’s FLOPs is 17.7 G, its size is 13.8MB, and it achieves an FPS of 188.52. Compared to current mainstream state-of-the-art (SOTA) models for object detection, Sunflower-YOLO achieves the highest [email protected] in detecting multiple types of sunflower capitula. The constructed capitulum density map offers a practical view for observing sunflower growth status. This study highlights the immense potential of combining UAV remote sensing technology with YOLO object detection algorithms in monitoring sunflower capitula and their growth processes, providing an innovative and effective approach for precision agriculture practices.
期刊介绍:
The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics:
crop physiology
crop production and management including irrigation, fertilization and soil management
agroclimatology and modelling
plant-soil relationships
crop quality and post-harvest physiology
farming and cropping systems
agroecosystems and the environment
crop-weed interactions and management
organic farming
horticultural crops
papers from the European Society for Agronomy bi-annual meetings
In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.