Abstract
Machine vision has been used to grade the potted anthurium plant in large-scale production recently. Images are taken to measure the number and size of anthurium spathes. However, due to the limitation of the shooting angle, the occlusion problem reduces the accuracy of measurement. It is necessary to segment the overlapping spathes and repair the incomplete ones. The traditional image completion model has good performance on missing small areas, but it is not satisfactory for missing large areas. In this article, a multi-scale fusion Recurrent Feature Reasoning (RFR) network was proposed to repair the spathe images. Unlike the traditional RFR, a multi-layer component was used in the feature reasoning module. This network can combine multi-scale features to complete the learning task and obtain more details of the spathe, which makes the network more advantageous in image completion when missing large areas of spathes. In this study, a comparison experiment between this network and the widely used image completion network was performed, and the results showed that this network performed well in all types of image completion, especially with large-area incomplete images.