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

文献类型： 外文期刊

第一作者： Liu, Chen

作者： Liu, Chen;Zhu, Huaji;Guo, Wang;Han, Xiao;Chen, Cheng;Wu, Huarui;Liu, Chen;Liu, Chen;Zhu, Huaji;Guo, Wang;Han, Xiao;Chen, Cheng;Wu, Huarui

作者机构：

关键词： Precision agriculture; Cucumber disease leaf; Disease detection; Complex environment; ASFF; EfficientNet

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2021 年 189 卷

页码：

收录情况： SCI

摘要： 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 model size, 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.

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