We conducted a small RNA sequencing of Sweet cherry (Prunus avium L. cv. Red-lamp) exhibiting mosaic symptoms for virus screening. After sequencing data analysis, Cherry virus A, Prunus necrotic ringspot virus, Little cherry virus 1 and Citrus leaf blotch virus were detected. The partial sequence of this CLBV was sequenced and analyzed with other members of Betaflexiviridae. We conclude that this virus from sweet cherry is a novel strain of CLBV. To our knowledge, this is the first report of CLBV infection in sweet cherry trees in the world.

Fruit size is a key trait in small-fruit breeding, yet its measurement remains labor intensive and prone to human error. To address this, we developed a non-destructive, automated size measurement system based on machine vision and LabVIEW, designed for small fruits such as cherry, blueberry, and walnut. The system integrates a modular architecture, including flat-field correction, calibration, and pattern matching sub-VIs, to ensure user-friendly operation. These sub-VIs also enable the system’s core data analysis, such as real-size conversion through calibration and noise reduction for data accuracy through flat-field correction. An optimized image processing pipeline (grayscale conversion, Canny edge detection, morphological operations) enables precise contour extraction, even for fruits with stems or irregular surfaces. The system supports multi-species adaptation through lightweight parameter adjustments, without hardware modification. Experiments involved 15 samples per species (cherry ‘Tieton’, blueberry ‘Northland’, walnut ‘Xiangling’). A gold-standard protocol was established using a pre-calibrated digital caliper operated by two experienced technicians, with the mean of six replicates per fruit defined as the true value. Results demonstrated low root mean square errors, with coefficients of determination (R²) exceeding 0.98. Paired t-tests confirmed no significant differences from the gold standard. The system achieved a measurement speed of 0.4 s per fruit, six times faster than manual methods, and complied with the precision requirements of GB/T 26906-2024 (Sweet Cherry). This system offers a cost-effective, high-throughput solution for fruit breeding and phenotyping, effectively overcoming the limitations of manual measurement.

Red-leaf trait rarely occurs in Juglans regia, and the genetic mechanism underlying this phenomenon is still unknown. In this study, we attempted to provide insight into the comprehensive transcriptome of red-leaf J. regia by RNA-Seq using Illumina SeqTM2000 platform. A total of 33,488,602 high-quality reads (3.35G cleans bases) were obtained and assembled into 53,782 unigenes. A total of 3,683 unigenes were annotated by using basic local alignment search tool to search against protein databases, All the matched unigenes were categorized by gene ontology analysis, and 3,466 were assigned to metabolism, among which 74 were mapped to anthocyanin, carotenoid, and betalain biosynthetic pathways by Kyoto Encyclopedia of Genes and Genomes analysis. Approximately 656 transcription factors were isolated including MYB, NAC, and bHLH. Additionally, a total of 13,981 simple sequence repeats, 41,088 single nucleotide polymorphisms, and 5,860 insertions and deletions were determined from J. regia transcriptome. Therefore, the current J. regia transcriptome provides deep insight into the molecular basis of red-leaf breeding of J. regia.

For small to medium-sized fruits, such as cherries and blueberries, phenotypic data such as fruit size and various diameters are key indicators for fruit breeding, providing essential references for the breeding process. Currently, manual measurements that are widely used are inefficient and introduce significant uncertainties. To address this, this study designed breeding equipment that leveraged machine vision technology to capture fruit images and perform size measurements and evaluations using LabVIEW programming. Using cherries as an example, the RGB images were converted to grayscale using a weighted average method, followed by flat-field correction to remove artifacts and noise. The Canny algorithm was then applied to extract the fruit contour, and the Otsu algorithm was employed for automatic thresholding in the image binarization. Morphological processing separated the fruit from the stem, and template-matching technology was used to define the region of interest (ROI) for measurement. The system achieved an average relative error of only 0.35%, with a standard deviation of 0.0828 and a measurement time of 2.547 s per cherry. This method significantly improved the efficiency and saved time compared to traditional manual measurements. The device successfully measured fruits such as cherries and blueberries, providing a new tool for fruit size measurement in the breeding process and offering valuable insights for the design of future equipment.