Potato late blight is a severe and highly epidemic disease caused by Phytophthora infestans that can affect all parts of the plant. This study mainly screened antagonistic strains for good control of potato late blight and identified strain SDTB038 as Bacillus velezensis according to its morphological and chemical properties and the 16S rRNA, gyrA, and gyrB gene sequences. This antagonistic strain achieved good control of potato late blight in greenhouses and fields and promoted potato plant growth. Two-year field trials (2018 and 2019) showed that B. velezensis SDTB038 can be used to reduce food losses caused by late blight, achieving late blight reductions of 40.79% (2018) and 37.67% (2019). In two-year field trials, the control effects of the highest concentrations of fluopimomide and B. velezensis SDTB038 were better than those of the other treatments. The control effect of 85 g ha⁻¹ fluopimomide and B. velezensis SDTB038 and that of 170 g ha⁻¹ fluopimomide alone showed no significant differences. These field results indicate that a low concentration of fungicide and a high concentration of SDTB038 can be effective in controlling potato late blight. Foliar detection showed that lipopeptides have an inhibitory effect on P. infestans. The amplification of lipopeptide genes revealed surfactin (srfAB and srfAC) and fengycin (fenB) genes in SDTB038, but only surfactin production by B. velezensis SDTB038 was observed by ultra-high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry analysis. Therefore, the strain B. velezensis SDTB038 can produce secondary metabolites that help potato plants resist late blight development, can effectively inhibit the infection of potato leaves by P. infestans, and has potential value for development as a biological pesticide against potato late blight.

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.

The external appearance of strawberry fruits serves as a critical criterion for their commercial value and grading standards. However, current research primarily emphasizes ripeness and surface defects, with limited attention given to the quantitative analysis of geometric characteristics such as deformity and symmetry. To address this gap, this study proposes a comprehensive evaluation framework that integrates deep learning-based segmentation with geometric analysis for strawberry appearance quality assessment. First, an enhanced YOLOv11 segmentation model incorporating a Squeeze-and-Excitation attention mechanism was developed to enable high-precision extraction of individual fruits, achieving Precision, Recall, AP₅₀, and F1 scores of 91.11%, 87.46%, 92.90%, and 88.45%, respectively. Second, a deformity quantification method was designed based on the number of deformity points (N_d), deformity rate (R_d), and spatial distance metrics (G_min and G_max). Experimental results demonstrated significant differences in R_d and G_max between deformed and normal strawberries, indicating strong classification capability. Finally, principal component analysis (PCA) was employed to extract the primary axis direction, and morphological symmetry was quantitatively evaluated using Intersection over Union (IoU) and Area Difference Ratio (AreaD_Ratio). The results revealed that most samples fell within an IoU range of 0.6–0.8 and AreaD_Ratio below 0.4, indicating noticeable inter-individual differences in fruit symmetry. This study aims to establish a three-stage analytical framework—segmentation, deformity quantification, and symmetry evaluation—for assessing strawberry appearance quality, with the goal of supporting key applications in automated grading and precision quality inspection.

China has the largest strawberry cultivation area worldwide and produces substantial quantities of the fruit. However, postharvest diseases of strawberries occur frequently, limiting their safe storage. In November 2025, a localized occurrence of postharvest fruit rot affecting strawberry (cv. Hongyan) was observed in Tai’an, China. A pathogenic fungus, designated CM-RB5, was isolated from diseased fruits and identified as Penicillium citrinum based on morphological characteristics and molecular analyses. This is the first report of P. citrinum causing postharvest fruit rot in strawberry. The genome of the pathogenic fungal strain CM-RB5 was sequenced using the Illumina MiSeq II and PacBio RS III platforms. Genome assembly analysis revealed the total sequence length of P. citrinum CM-RB5 to be 32,053,718 bp, with a GC content of 46.41%. Additionally, P. citrinum CM-RB5 was found to produce ochratoxin and citrinin. These findings provide insights that may facilitate the development of effective control strategies for postharvest fruit rot in strawberry, thereby ensuring the consumption of safe, high-quality fruit and strawberry-derived products.

Strawberry root rot is a serious soil-borne root disease with a high incidence rate that is extremely difficult to control. To identify the fungal pathogens responsible for strawberry root rot, diseased plants exhibiting root rot symptoms were collected from Jining, Shandong Province, China. Strain CMGF-RB13 was isolated from the roots of infected strawberry plants, and based on morphological characteristics and molecular analyses, was identified as Fusarium verticillioides . To our knowledge, this is the first study of strawberry root rot disease caused by F. verticillioides in China. The genome of this pathogenic fungal strain was sequenced using the Illumina MiSeq II and PacBio RS III platforms, and genome assembly analysis revealed a total length of 43,257,301 bp with a GC content of 47.89%. Analysis of potential secondary metabolites indicated that F. verticillioides CMGF-RB13 have potential biosynthetic capacity of fumonisin B1, choline, fusaric acid, oxyjavanicin, and bikaverin, and these are putative genomic predictions based on biosynthetic gene clusters, rather than experimentally confirmed metabolites. The genome sequence was presented primarily as a genomic resource supporting the characterization of the strawberry-associated isolate. The genomic information may serve as a basis for future diagnostic and disease-management studies.