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

• Published in: Agronomy (Basel) Vol. 11; no. 5; p. 834
• Main Authors: Qi, Chao, Nyalala, Innocent, Chen, Kunjie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2021
• Subjects: Accuracy; Algorithms; Backbone; Decision theory; deep convolutional neural network; Deep learning; Design; F-YOLO; Flowering; flowing stage detection; Gradient flow; Modules; Neural networks; Object recognition; Occlusion; Regularization methods; Robots; Semantics; Slicing; Tea; tea chrysanthemum
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy11050834

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.