EFDet: An efficient detection method for cucumber disease under natural complex environments

• Published in: Computers and electronics in agriculture Vol. 189; p. 106378
• Main Authors: Liu, Chen, Zhu, Huaji, Guo, Wang, Han, Xiao, Chen, Cheng, Wu, Huarui
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2021; Elsevier BV
• Subjects: ASFF; Complex environment; Complexity; Computer networks; Cucumber disease leaf; Cucumbers; Disease detection; EfficientNet; Feature maps; Precision agriculture; Precision agriculture; Cucumber disease leaf; EfficientNet; Complex environment; ASFF; Disease detection
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2021.106378

•Images with different complexity backgrounds were collected. To measure the detection performance on various images, a method was proposed to quantify image background complexity.•We constructed an efficient detection model EFDet to detect cucumber disease in complex images.•EFDet has high robustness for complex environments and fewer parameters and computation. Improving the application capability of the disease detection model is a key issue in the field of agricultural informatization. The complex backgrounds, image diversity, and model complexity are the main factors that affect the realization of automatic disease recognition. This study constructs an efficient detection model (EFDet), which mainly consists of the efficient backbone network, a feature fusion module, and a predictor. EFDet improves the detection effect for cucumber leaves in complex backgrounds by fusing feature maps at different levels. We collected three category cucumber leaves including downy mildew, bacterial angular spot, and health to construct the cucumber disease dataset. It contains 7,488 images with three complexity levels for model training and evaluation. YOLO V3-V5, EfficientDet-D1, YOLO V3-ASFF, and other six detection models as the comparison models, we verify the EFDet performance in terms of modelsize,FLOPs, and mAP. Experimental results show that EFDet has strong robustness for cucumber disease leaf in complex environments. It also has smaller parameters and calculations that are suitable for actual applications.