Leaf spot, a major apple disease, manifests in diverse symptoms. In this study, the pathogen was isolated from diseased ‘Yanfu 3’ apple leaves in Yantai, Shandong Province, and identified as Neopestalotiopsis clavispora through morphological observation, molecular identification, and multi-gene (ITS, TEF1α, and TUB2) phylogenetic analysis. Three isolates (YTNK01, YTNK02, and YTNK03) were selected for pathogenicity tests to verify Koch’s postulates. To our knowledge, this is the first report of N. clavispora being responsible for apple leaf spots in China, and the disease has been named ‘apple Neopestalotiopsis leaf spot’. Additionally, N. clavispora was found to infect crabapple, sweet cherry, grape, peach, and pear under laboratory conditions, indicating that these fruit trees may be potential hosts for N. clavispora in the field. The in vitro toxicity of ten fungicides to the pathogen was assessed using the mycelial growth rate method. All ten fungicides were effective in inhibiting the growth of N. clavispora. Among them, those based on pylocyanonitrile, propiconazole, pyraclostrobin, tebuconazole, diphenoxazole, and osthole showed higher toxicity to N. clavispora, with EC₅₀ values of 0.11, 0.41, 0.47, 1.32, 1.85, and 3.82 µg/mL, respectively. These fungicides could be used as alternatives to prevent this disease in production. Overall, these findings provide valuable insights into the characteristics of N. clavispora causing apple leaf spot and are crucial for developing effective management strategies.

Apple Alternaria blotch disease, caused by Alternaria alternata (Fr.) Keissl, is one of the most famous leaf diseases. When the disease is prevalent, it causes leaf abscission and influences the formation of flower buds and photosynthesis. Therefore, a simple, rapid, high-specificity and sensitivity method for monitoring infected leaves at early developmental stages is urgently needed, so that the occurrence and expansion of A. alternata can be controlled in time. In our research, a rapid, specific and efficient loop-mediated isothermal amplification (LAMP) method was developed to detect A. alternata within 60 min. Six primers of LAMP detection can only specifically amplify the aapg-1 gene in A. alternata but not in four other important fungi in apples. The aapg-1 gene encodes endopolygalacturonase in A. alternata, and there are significant differences among different species. Thus, it was applied as the target for LAMP primers. Compared to conventional PCR detection, our LAMP method had the same sensitivity as that of detecting as little as 1 fg of pure genomic DNA of A. alternata. When leaves were inoculated with A. alternata conidia, LAMP detected 1 × 10² conidia/mL as the minimum concentration. However, the traditional tissue isolation and identification method only isolated A. alternata from leaves inoculated with 1 × 10⁵ and 1 × 10⁶ conidia/mL, indicating that the LAMP method was more sensitive than the traditional tissue isolation and identification method for A. alternata before symptoms. Further tests also indicated that LAMP detection was more accurate and sensitive than the traditional tissue isolation and identification method for A. alternata in leaves with the Alternaria blotch symptom collected from the field. Our results showed that the LAMP-targeting the aapg-1 gene has the advantages of high sensitivity, specificity and simplicity and can be used for rapid detection and early monitoring of A. alternata in the field. LAMP is instructive for us to effectively prevent and control apple Alternaria blotch disease.

The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. This enhances the recognition accuracy of subtle surface defects and large-scale damages on cherries. Second, a channel attention feature fusion mechanism (CAFM) is incorporated at the front end of the detection head, which enhances the model’s ability to identify fine defects on the cherry surface. Additionally, to improve bounding box regression accuracy, powerful-IoU (PIoU) replaces the traditional CIoU loss function. Finally, self-distillation technology is introduced to further improve the mode’s generalization capability and detection accuracy through knowledge transfer. Experimental results show that the YOLOv8-DCPF model achieves precision, mAP, recall, and F1 score rates of 92.6%, 91.2%, 89.4%, and 89.0%, respectively, representing improvements of 6.9%, 5.6%, 6.1%, and 5.0% over the original YOLOv8n baseline network. The proposed model demonstrates high accuracy in cherry defect detection, providing an efficient and precise solution for intelligent cherry sorting in agricultural engineering applications.

In response to the issues of low-velocity zones and non-uniform jet velocity distribution in the airflow field of traditional air-assisted orchard sprayers, an arc-shaped air outlet suitable for axial-flow air-assisted systems is designed. This article employs the method of CFD numerical simulation and experimental verification to compare and analyze the internal flow field of the air-assisted system and validates the reliability of the numerical simulation results through calculation error and chi-square test. The wind speed of the cross-section is measured at different distances from the outlet, and the distribution characteristics of the outflow field wind speed before and after the structural optimization of the air-assisted system are compared. The horizontal distribution of fog droplets is collected using a fog collection chamber. The experimental results show that the design of the arc-shaped outlet increases the average wind speed of the annular outlet from 14.95 m/s to 18.20 m/s and reduces the proportion of low-speed area from 20.83% to 0.71%. When the rounded corner radius of the air outlet is 50 mm, optimal parameters are attained. The maximum error between the simulated and experimental values is 9.52%. At a significance level of 0.05, the χ² value is 0.252, indicating that the simulated values follow the distribution of the actual measurement values. On the cross-sections located at distances of 0.5, 0.75, 1, 1.25, and 1.5 m from the air outlet, the wind speed distribution with no arc-shaped air outlets exhibits a “low left and high right” type, tending to shift towards the right as a whole. Fog droplets also display a drift tendency towards the right side. The wind speed distribution with arc-shaped air outlets shows a symmetric “high in the middle and low on the sides” type. Fog droplets concentrate in the central position. The optimized air-assisted system can reduce the air field’s low-flow area, increase the airflow distribution uniformity, improve the average wind speed at the outlet, and decrease fog droplet drift. This provides a reference for the structural design of air-assisted systems in current orchard sprayers of the same type.