A REAL-TIME DETECTION MODEL FOR IDETIFICATION OF CITRUS DURING DIFFERENT GROWTH STAGES IN ORCHARDS
DOI : https://doi.org/10.35633/inmateh-68-37
In order to solve the problem of citrus full growth cycle identification in complex scenes, this paper proposed a multi-scale detection model of citrus whole growth cycle in orchard environment. The weighted bi-directional feature pyramid network (BiFPN) is used to combine multiple feature information of high resolution and low- resolution feature layers, and the feature information is extracted by the depth-separable convolution and lightweight New-C3 module. The results show that the average accuracy of the multi-scale detection model proposed in this paper was 91.35%, 92.89%, 94.12%, 90.39% in the young citrus, expanding citrus, ripe citrus and full growth cycle citrus, and the average detection time was 92.60 FPS/s under 1920×1080 image pixels, which meets the real-time detection requirements of citrus orchard.
Abstract in Chinese