Online detection of potato drying stages based on improved YOLOv7-tiny model

To realize accurate online identification of different stages of the agricultural product drying process and overcome the limitations of empirical models, this study proposes a method for online identification of agricultural product drying stages based on machine vision, which enhances the YOLOv7-tiny model by adding an attention mechanism module to the feature layer and the up-adoption process. The recognition results were compared and evaluated with those of other versions of YOLO, Faster R-CNN, SSD, EfficientDet, and an unimproved YOLOv7-tiny network. The results showed that the average recognition accuracy of this method for the constant drying stage, first drying stage deceleration and second drying stage deceleration of potato slices reached 98.8%, which was superior to that of the model without the attentional mechanism module. This lays the foundation for the establishment of an on-line adaptive drying model for agricultural products.