向日葵-YOLO:无人机遥感图像中向日葵头状花序的检测

IF 4.5 1区 农林科学 Q1 AGRONOMY European Journal of Agronomy Pub Date : 2024-09-01 DOI:10.1016/j.eja.2024.127332
Rui Jing, Qinglin Niu, Yuyu Tian, Heng Zhang, Qingqing Zhao, Zongpeng Li, Xinguo Zhou, Dongwei Li
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引用次数: 0

摘要

向日葵头状花序的准确识别和监测对于田间表型分析、栽培管理、物候监测和产量预测至关重要。然而,由于田间环境的复杂性和向日葵头状花序形态的多样性,人工观测面临着巨大的挑战。无人飞行器(UAV)因其低成本和高时空分辨率而成为向日葵头状花序监测的理想平台。本研究介绍了基于 YOLOv7-tiny 的增强型模型 Sunflower-YOLO,该模型专为在无人机遥感图像中检测向日葵头状花序而设计。该模型可有效识别向日葵头状花序,并区分三种特定状态:开放、半开和花蕾。向日葵-YOLO 包含几项关键改进:SiLU 激活函数取代了原来的 LeakyReLU,增强了模型的非线性表达能力;在特征融合阶段引入了浅层高分辨率特征图和针对小目标的附加检测头,提高了对小蒴果的检测性能;整合了可变形卷积和 SimAM 注意机制,增强了骨干中的 ELAN 结构,创建了新的 DeformAtt-ELAN 结构,提高了模型捕捉形态变化的能力,并减少了噪声干扰。实验结果表明,向日葵-YOLO 的精确度、召回率和 [email protected] 分别达到了 92.3 %、89.7 % 和 93 %,与原始 YOLOv7-tiny 模型相比分别提高了 4.2 %、4.2 % 和 3.7 %。三种生长状态的平均精度(AP)分别为 98.7%、93.4% 和 87%,半开状态和花蕾状态的平均精度分别提高了 6.5% 和 4.7%。该模型的 FLOPs 为 17.7 G,大小为 13.8 MB,FPS 为 188.52。与目前最先进的主流(SOTA)物体检测模型相比,向日葵-YOLO 在检测多种向日葵头状花序方面取得了最高的 [email protected]。构建的向日葵头状花序密度图为观察向日葵的生长状况提供了一个实用的视角。这项研究凸显了无人机遥感技术与 YOLO 目标检测算法相结合在监测向日葵头状花序及其生长过程方面的巨大潜力,为精准农业实践提供了一种创新而有效的方法。
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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.

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来源期刊
European Journal of Agronomy
European Journal of Agronomy 农林科学-农艺学
CiteScore
8.30
自引率
7.70%
发文量
187
审稿时长
4.5 months
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.
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