I3-YOLOv8s: An improved YOLOv8s for infrequent irregular imbalanced detection and segmentation of rape stomata

For precise quantification of the stomatal phenotype across rape, automatic detection and segmentation of stomata from microscopic images is crucial. However, this task poses several challenges: (1) I nfrequent: The stomata of rape are domain-specific objects, rendering pre-trained feature extractors from transfer learning unreliable; (2) I rregular: The detection and segmentation of stomata is complicated due to disparities in their shape, size, and tilt angle; (3) I mbalanced: The number of samples in detection and segmentation task suffer from imbalance issues between low-quality/high-quality bounding boxes and foreground/background pixels, respectively. In this research, a novel multi-task model named I3-YOLOv8s is proposed, aiming at detecting and segmenting stomata of rape during its bolting stage. Specifically, for the Infrequent problem, a self-supervised learning method based on masked image reconstruction is designed to pre-train domain-specific backbone network; then, for the Irregular problem, a CA block based on the coordinate attention mechanism is developed in the multi-scale neck network; finally, for the Imbalanced problem, a novel loss function is proposed in the decoupled head based on the focal EIoU&focal loss. Experimental results indicate that, the proposed I3-YOLOv8s achieves an F1 score of 93.29 % and a single image inference delay of 14.1 ms for detection; its F1 score is 92.51 % and a single image inference delay of 14.8 ms for segmentation. The I3-YOLOv8s achieves the state-of-the-art performance and an optimal trade-off between accuracy and speed. Experimental analyses further substantiate the efficacy of each module, and attest to the dependability of implementing I3-YOLOv8s on edge computing devices for agricultural production.