An accurate semantic segmentation model for bean seedlings and weeds identification based on improved ERFnet

Abstract

In agricultural production activities, the growth of crops always accompanies the competition of weeds for nutrients and sunlight. In order to mitigate the adverse effects of weeds on yield, we apply semantic segmentation techniques to differentiate between seedlings and weeds, leading to precision weeding. The proposed EPAnet employs a loss function coupled with Cross-entropy loss and Dice loss to enhance attention to feature information. A multi-Decoder cooperative module based on ERFnet is designed to enhance information transfer during feature mapping. The SimAM is introduced to enhance position recognition. DO-CONV is used to replace the traditional convolution Feature Pyramid Networks (FPN) connection layer to integrate feature information, improving the model’s performance on leaf edge processing, and is named FDPN. Moreover, the Overall Accuracy has been improved by 0.65%, the mean Intersection over Union (mIoU) by 1.91%, and the Frequency-Weighted Intersection over Union (FWIoU) by 1.19%. Compared to other advanced methods, EPAnet demonstrates superior image segmentation results in complex natural environments with uneven lighting, leaf interference, and shadows.