Precise information on strawberry fruit distribution is of significant importance for optimizing planting density and formulating harvesting strategies. This study applied a combined analysis of kernel density estimation and nearest neighbor techniques to estimate fruit distribution density from YOLOdetected strawberry images. Initially, an improved yolov8n strawberry object detection model was employed to obtain the coordinates of the fruit centers in the images. The results indicated that the improved model achieved an accuracy of 94.7% with an mAP@0.5~0.95 of 87.3%. The relative error between the predicted and annotated coordinates ranged from 0.002 to 0.02, demonstrating high consistency between the model predictions and the annotated results. Subsequently, based on the strawberry center coordinates, the kernel density estimation algorithm was used to estimate the distribution density in the strawberry images. The results showed that with a bandwidth of 200, the kernel density estimation accurately reflected the actual strawberry density distribution, ensuring that all center points in high-density regions were consistently identified and delineated. Finally, to refine the strawberry distribution information, a comprehensive method based on nearest neighbor analysis was adopted, achieving target area segmentation and regional density estimation in the strawberry images. Experimental results demonstrated that when the distance threshold ϵ was set to 600 pixels, the correct grouping rate exceeded 94%, and the regional density estimation results indicated a significant positive correlation between the number of fruits and regional density. This study provides scientific evidence for optimizing strawberry planting density and formulating harvesting sequences, contributing to improved yield, harvesting efficiency, and reduced fruit damage. In future research, this study will further explore dynamic models that link fruit distribution density, planting density, and fruit growth status.

Xylotrechus pyrrhoderus Bates (Coleoptera: Cerambycidae) is a major viticultural pest whose larval feeding damages grapevine canes and reduces yields, yet its internal development and damage progression are poorly understood. Using field surveys combined with micro-computed tomography (micro-CT) and in situ X-ray microscopy, we non-destructively tracked both tissue damage and life-cycle dynamics within canes. Damage intensity increased from December to May, with affected canes showing reduced porosity compared to healthy ones. Continuous imaging revealed a distinct prepupal stage and extended adult residence in pupal chambers before emergence. Morphometric analysis showed that adult size is constrained by pupal chamber dimensions, indicating adaptive adjustment to limited intra-cane space. These findings demonstrate the value of advanced imaging tools for clarifying the biology of X. pyrrhoderus and support the development of targeted management strategies for this and related agricultural pests.