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

Abstract

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.

Weight and volume estimation of single and occluded tomatoes using machine vision

“…[28] SVR models' strengths are that their training is easy, are explicitly controlled, offer better generalization performance (accuracy), provide direct geometrical interpretation, lack an optimal local solution, elegant mathematical tractability, and prevent overfitting by not necessitating a massive set of training samples. [9,29,30] Similarly, the SVR models rely only on a subset of the training dataset since the cost function does not consider the training points outside the margin during model construction, thereby conveniently preventing overfitting. [31] SVR is typically a quadratic programming problem (QP) to distinguish support vectors from the other training data vectors.…”

Image dataset of tea chrysanthemums in complex outdoor scenes

“…as well as differences in maturity, colour, and the direction of chrysanthemums' flower heads. Many researchers have identified chrysanthemums by overcoming some of the above (Yang et al, 2018;Yuan et al, 2018;Liu et al, 2019;Yang et al, 2019;et al, 2020;Qi et al, 2021;Qi et al, 2022a;Qi et al, 2022b), and these studies indicate that:…”

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

“…The YOLOv4-LITE version uses MobileNetv2 as the model's backbone and uses the Do-Conv convolution [57]. The Fusion-YOLO version uses CSPDenseNet and function fusion modules to maximize gradient flow differences, and the CSPResNeXt network to reduce excess gradient flow [58]. Computer vision systems used in agriculture should be as adaptive as possible and easily integrated into production processes [59].…”