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Detecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model

Department of Agricultural Machinery (AM), College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
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Academic Editor: Gniewko Niedbała
Agronomy 2021, 11(5), 834; https://doi.org/10.3390/agronomy11050834
Received: 13 March 2021 / Revised: 12 April 2021 / Accepted: 16 April 2021 / Published: 23 April 2021
Detecting the flowering stage of tea chrysanthemum is a key mechanism of the selective chrysanthemum harvesting robot. However, under complex, unstructured scenarios, such as illumination variation, occlusion, and overlapping, detecting tea chrysanthemum at a specific flowering stage is a real challenge. This paper proposes a highly fused, lightweight detection model named the Fusion-YOLO (F-YOLO) model. First, cutout and mosaic input components are equipped, with which the fusion module can better understand the features of the chrysanthemum through slicing. In the backbone component, the Cross-Stage Partial DenseNet (CSPDenseNet) network is used as the main network, and feature fusion modules are added to maximize the gradient flow difference. Next, in the neck component, the Cross-Stage Partial ResNeXt (CSPResNeXt) network is taken as the main network to truncate the redundant gradient flow. Finally, in the head component, the multi-scale fusion network is adopted to aggregate the parameters of two different detection layers from different backbone layers. The results show that the F-YOLO model is superior to state-of-the-art technologies in terms of object detection, that this method can be deployed on a single mobile GPU, and that it will be one of key technologies to build a selective chrysanthemum harvesting robot system in the future. View Full-Text
Keywords: tea chrysanthemum; flowing stage detection; deep convolutional neural network; F-YOLO tea chrysanthemum; flowing stage detection; deep convolutional neural network; F-YOLO
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MDPI and ACS Style

Qi, C.; Nyalala, I.; Chen, K. Detecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model. Agronomy 2021, 11, 834. https://doi.org/10.3390/agronomy11050834

AMA Style

Qi C, Nyalala I, Chen K. Detecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model. Agronomy. 2021; 11(5):834. https://doi.org/10.3390/agronomy11050834

Chicago/Turabian Style

Qi, Chao, Innocent Nyalala, and Kunjie Chen. 2021. "Detecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model" Agronomy 11, no. 5: 834. https://doi.org/10.3390/agronomy11050834

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