TY - JOUR
T1 - Improved YOLOX-Tiny network for detection of tobacco brown spot disease
AU - Lin, Jianwu
AU - Yu, Dianzhi
AU - Pan, Renyong
AU - Cai, Jitong
AU - Liu, Jiaming
AU - Zhang, Licai
AU - Wen, Xingtian
AU - Peng, Xishun
AU - Cernava, Tomislav
AU - Oufensou, Safa
AU - Migheli, Quirico
AU - Chen, Xiaoyulong
AU - Zhang, Xin
N1 - Publisher Copyright:
Copyright © 2023 Lin, Yu, Pan, Cai, Liu, Zhang, Wen, Peng, Cernava, Oufensou, Migheli, Chen and Zhang.
PY - 2023
Y1 - 2023
N2 - Introduction: Tobacco brown spot disease caused by Alternaria fungal species is a major threat to tobacco growth and yield. Thus, accurate and rapid detection of tobacco brown spot disease is vital for disease prevention and chemical pesticide inputs. Methods: Here, we propose an improved YOLOX-Tiny network, named YOLO-Tobacco, for the detection of tobacco brown spot disease under open-field scenarios. Aiming to excavate valuable disease features and enhance the integration of different levels of features, thereby improving the ability to detect dense disease spots at different scales, we introduced hierarchical mixed-scale units (HMUs) in the neck network for information interaction and feature refinement between channels. Furthermore, in order to enhance the detection of small disease spots and the robustness of the network, we also introduced convolutional block attention modules (CBAMs) into the neck network. Results: As a result, the YOLO-Tobacco network achieved an average precision (AP) of 80.56% on the test set. The AP was 3.22%, 8.99%, and 12.03% higher than that obtained by the classic lightweight detection networks YOLOX-Tiny network, YOLOv5-S network, and YOLOv4-Tiny network, respectively. In addition, the YOLO-Tobacco network also had a fast detection speed of 69 frames per second (FPS). Discussion: Therefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants.
AB - Introduction: Tobacco brown spot disease caused by Alternaria fungal species is a major threat to tobacco growth and yield. Thus, accurate and rapid detection of tobacco brown spot disease is vital for disease prevention and chemical pesticide inputs. Methods: Here, we propose an improved YOLOX-Tiny network, named YOLO-Tobacco, for the detection of tobacco brown spot disease under open-field scenarios. Aiming to excavate valuable disease features and enhance the integration of different levels of features, thereby improving the ability to detect dense disease spots at different scales, we introduced hierarchical mixed-scale units (HMUs) in the neck network for information interaction and feature refinement between channels. Furthermore, in order to enhance the detection of small disease spots and the robustness of the network, we also introduced convolutional block attention modules (CBAMs) into the neck network. Results: As a result, the YOLO-Tobacco network achieved an average precision (AP) of 80.56% on the test set. The AP was 3.22%, 8.99%, and 12.03% higher than that obtained by the classic lightweight detection networks YOLOX-Tiny network, YOLOv5-S network, and YOLOv4-Tiny network, respectively. In addition, the YOLO-Tobacco network also had a fast detection speed of 69 frames per second (FPS). Discussion: Therefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants.
KW - convolutional block attention modules
KW - hierarchical mixed-scale units
KW - object detection
KW - tobacco brown spot disease
KW - YOLOX-Tiny network
UR - http://www.scopus.com/inward/record.url?scp=85149575615&partnerID=8YFLogxK
U2 - 10.3389/fpls.2023.1135105
DO - 10.3389/fpls.2023.1135105
M3 - Article
AN - SCOPUS:85149575615
SN - 1664-462X
VL - 14
JO - Frontiers in Plant Science
JF - Frontiers in Plant Science
M1 - 1135105
ER -