Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model

These authors contributed equally to this work and should be considered co-first authors. Abstract: Tea chrysanthemum detection at its flowering stage is one of the key components for selective chrysanthemum harvesting robot development. However, it is a challenge to detect flowering chrysanthemums under unstructured field environments given the variations on illumination, occlusion and object scale. In this context, we propose a highly fused and lightweight deep learning architecture based on YOLO for tea chrysanthemum detection (TC-YOLO). First, in the backbone component and neck component, the method uses the Cross-Stage Partially Dense Network (CSPDenseNet) as the main network, and embeds custom feature fusion modules to guide the gradient flow. In the final head component, the method combines the recursive feature pyramid (RFP) multiscale fusion reflow structure and the Atrous Spatial Pyramid Pool (ASPP) module with cavity convolution to achieve the detection task. The resulting model was tested on 300 field images, showing that under the NVIDIA Tesla P100 GPU environment, if the inference speed is 47.23 FPS for each image (416 416), TC-YOLO can achieve the average precision (AP) of 92.49% on our own tea chrysanthemum dataset. In addition, this method (13.6M) can be deployed on a single mobile GPU, and it could be further developed as a perception system for a selective chrysanthemum harvesting robot in the future. Keywords: Tea chrysanthemum; Flowering stage detection; Deep convolutional neural network; Agricultural robotics 1. Introduction Current studies show that tea chrysanthemums have significant commercial value (Liu et al., 2020; Liu et al., 2019). Not only that, but tea chrysanthemums can offer a range of health benefits (Hou et al., 2017; Yue et al., 2018). For example, they can significantly inhibit the activity of carcinogens and have distinct anti-aging, cholagogic and antihypertensive effects (Zheng et al., 2021).