Manual detection of strawberry fruit and leaf diseases is susceptible to environmental constraints. In this study, an intelligent detection model (named YOLOv8n-SFLD) was proposed to integrate with multi-scale collaborative attention using an improved YOLOv8n framework. A high-quality dataset of 8,745 images was constructed with seven phenotypes of strawberry disease (angular leaf spot, anthracnose, blossom blight, gray mold, leaf spot, powdery mildew on leaves and fruits) from the Kaggle platform and field collections. Data augmentation was then performed with geometric transformations, illumination adjustments, noise addition, occlusion simulation, and Mosaic enhancement. A four-fold optimization was applied to the baseline YOLOv8n: 1) Universal Receptive Large Kernel Network (UniRepLKNet) module was integrated into the C2f structure to extract deep semantic features. 2) Channel Prior Convolutional Attention (CPCA) was introduced into the backbone to improve lesion detection accuracy under complex backgrounds. 3) Multi-Scale Collaborative Attention (MSCA) with parallel 3×3, 5×5, and 7×7 convolutional branches was incorporated into the neck architecture to capture multi-scale lesion features. 4) Wise-IoU loss function replaced CIoU to optimize bounding box regression for small targets via dynamic non-monotonic focusing. Experimental results demonstrate that the YOLOv8n-SFLD achieved superior performance. Attention mechanism showed that the embedding CPCA in the backbone obtained the best precision (88.6%), while the MSCA in the neck was achieved in the highest mAP50 (87.1%). Dataset analysis revealed that there was the significant variation in the performance over the different categories: The blossom blight, healthy fruit, angular leaf spot, and leaf spot were achieved in the mAP50 of 98.9%, 99.5%, 90.1%, and 95.0%, with the miss rates below 12.5%, while the anthracnose and healthy leaf shared the lower mAP50 (62.8%, and 58.8%) and higher miss rates (44.0%, and 37.2%), due to the sample imbalance. Ablation experiments demonstrated the synergistic effects of all modules: The complete YOLOv8n-SFLD (C2f-URBlock+CPCA+MSCA+Wise-IoU) was attained the precision of 91.9%, mAP50 of 87.0%, and mAP50-95 of 67.5%, which was improved by 7.4, 2.3, and 1.5 percentage points over baseline, with the model weight of 13.4 MB and FLOPs of 16.0 G. Furthermore, the YOLOv8n-SFLD outperformed all in the precision (5.2-10.6 percentage points higher) and mAP50 (2.0-5.8 percentage points higher), compared with the RT-DETR, YOLOv8n, YOLOv8s,YOLOv9t, YOLOv10n, YOLOv11, and YOLOv12. Visualization analysis showed that the superiority was achieved in the gray mold detection with multi-disease co-infection. All lesions were accurately detected, while the misdetection was observed in the dried leaves or miss fruit powdery mildew. In leaf spot with dense small lesions, the complete coverage was obtained with the tightly fitting boxes; The high precision and accurate localization were maintained in the powdery mildew under low-contrast backgrounds. The better performance was attributed to the synergistic optimization: C2f-URBlock was used to enhance the deep feature extraction, CPCA was used to strengthen the lesion discrimination under complex backgrounds, MSCA was suitable for the multi-scale variations, and Wise-IoU was used to improve the small-target regression. Furthermore, the improved YOLOv8n-SFLD effectively alleviated the multi-scale lesion variation, low-contrast features, and complex background interference. There was an optimal balance between accuracy and efficiency for deployment on the resource-constrained edge devices. High generalization was also obtained to detect similar diseases of the crops. The finding can provide reliable technical support for the precision pesticide in smart agriculture. Future work can focus on dataset expansion, model compression for mobile deployment, and UAV integration for real-time monitoring.
- Article type
- Year
- Co-author
Open Access
Research Article
Issue
Erysiphe necator is a destructive fungal pathogen that compromises grapevine yield and quality, leading to substantial economic losses. Therefore, elucidating host resistance mechanisms is essential. In this study, we identified an ethylene response factor, VqERF1B, that exhibits sustained high expression during E. necator infection in Chinese wild grape Vitis quinquangularis accession ‘Danfeng-2’. Transient overexpression of VqERF1B in grape leaves enhanced resistance to E. necator by elevating transcript levels of pathogenesis-related (PR) genes, including PR1, PR2, PR5, and PR10. Conversely, silencing VqERF1B resulted in increased susceptibility. Moreover, transgenic Arabidopsis lines stably overexpressing VqERF1B exhibited enhanced resistance to powdery mildew, associated with elevated PR gene expression and increased accumulation of reactive oxygen species (ROS). A series of assays identified VqMAPK3, a phosphorylated mitogen-activated protein kinase, as a direct interactor of VqERF1B. Furthermore, VqERF1B was shown to bind directly to the promoters of VqPRs, thereby activating their transcription. Notably, the VqMAPK3-VqERF1B complex exhibited greater transactivation activity on VqPR promoters than VqERF1B alone, indicating that VqMAPK3 positively modulates VqERF1B-mediated transcription of PR genes. This work advances understanding of the molecular basis of grape resistance to E. necator and provides a foundation for molecular breeding strategies.
Asparagus officinalis L. is a perennial plant with a long harvesting cycle and fast growth rate. The harvesting period of tender stems is relatively concentrated, and the shelf life of tender stems is very short. Therefore, the harvested asparagus needs to be classified according to the specifications of asparagus in a short time and then packaged and sold. However, at this stage, the classification of asparagus specifications basically depends on manual work, and it is difficult for asparagus of different specifications to rely on sensory grading, which requires a lot of money and labor. To save labor costs, an algorithm based on asparagus stem diameter classification was developed using deep learning and computer vision technology. YOLOv11 was selected as the baseline model and several improvements were made to propose a lightweight model for accurate grading of post-harvest asparagus.
Dataset was obtained by cell phone photography of post-harvest asparagus using fixed camera positions. In order to improve the generalization ability of the model, the training set was augmented with data by increasing contrast, mirroring, and adjusting brightness. The data-enhanced training set included a total of 2160 images for training the model, and the test set and validation set included 90 and 540 images respectively for inference and validation of the model. In order to enhance the performance of the improved model, the following four improvements were made to the baseline model, respectively. First, the efficient channel attention (ECA) module was added to the twelfth layer of the YOLOv11 backbone network. The ECA enhanced asparagus stem diameter feature extraction by dynamically adjusting channel weights in the convolutional neural network and improved the recognition accuracy of the improved model. Second, the bi-directional feature pyramid network (BiFPN) module was integrated into the neck network. This module modified the original feature fusion method to automatically emphasize key asparagus features and improved the grading accuracy through multi-scale feature fusion. What's more, BiFPN dynamically adjusted the importance of each layer to reduce redundant computations. Next, the slim-neck module was applied to optimize the neck network. The slim-neck module consisted of GSConv and VoVGSCSP. The GSConv module replaced the traditional convolutional. And the VoVGSCSP module replaced the C2k3 module. This optimization reduced computational costs and model size while improving the recognition accuracy. Finally, the original YOLOv11 detection head was replaced with an EfficientDet Head. EfficientDet Head had the advantages of light weight and high accuracy. This head co-training with BiFPN to enhance the effect of multi-scale fusion and improve the performance of the model.
In order to verify the validity of the individual modules introduced in the improved YOLOv11 model and the superiority of the performance of the improved model, ablation experiments and comparison experiments were conducted respectively. The results of the comparison test between different attentional mechanisms added to the baseline model showed that the ECA module had better performance than other attentional mechanisms in the post-harvest asparagus grading task. The YOLOv11-ECA had higher recognition accuracy and smaller model size, so the selection of the ECA module had a certain degree of reliability. Ablation experiments demonstrated that the improved YOLOv11 achieved 96.8% precision (P), 96.9% recall (R), and 92.5% mean average precision (mAP), with 4.6 GFLOPs, 1.67 × 106 parameters, and a 3.6 MB model size. The results of the asparagus grading test indicated that the localization frames of the improved model were more accurate and had a higher confidence level. Compared with the original YOLOv11 model, the improved YOLOv11 model increased the precision, recall, and mAP by 2.6, 1.4, and 2.2 percentage points, respectively. And the floating-point operation, parameter quantity, and model size were reduced by 1.7 G, 9.1 × 105, and 1.6 MB, respectively. Moreover, various improvements to the model could increase the accuracy of the model while ensuring that the model was light weight. In addition, the results of the comparative tests showed that the performance of the improved YOLOv11 model was better than those of SSD, YOLOv5s, YOLOv8n, YOLOv11, and YOLOv12. Overall, the improved YOLOv11 had the best overall performance, but still had some shortcomings. In terms of the real-time performance of the model, the inference speed of the improved model was not optimal, and the inference speed of the improved YOLOv11 was inferior to that of YOLOv5s and YOLOv8n. The inference speed of improved YOLOv11 and YOLOv11 evaluate using the aggregate test. The results of the Wilcoxon signed-rank test showed that the improved YOLOv11 had a significant improvement in inference speed compared to the original YOLOv11 model.
The improved YOLOv11 model demonstrated better recognition, lower parameters and floating-point operations, and smaller model size in the asparagus grading task. The improved YOLOv11 could provide a theoretical foundation for intelligent post-harvest asparagus grading. Deploying the improved YOLOv11 model on asparagus grading equipment enables fast and accurate grading of post-harvest asparagus.
Soil microbes at the rhizosphere are one of the key factors in maintaining soil health. However, it is unclear on the effect of different water-biochar coupling on the microbial environment of soil at the rhizosphere. Particularly, asparagus is one type of perennial herbaceous plant. There is a seriously continuous cropping disorder in asparagus, with the growth of years, leading to weak growth, low yield, and quality. The crop failure can be attributed to the soil's physicochemical properties and the unbalance of the rhizosphere soil microbial community. This study aims to explore the effects of water-biochar coupling on the structure of soil microbial communities in the asparagus rhizosphere. The mechanisms were also given to regulate the rhizosphere ecosystem. The field positioning experiment of asparagus planting was carried out on four irrigation levels (W0: severe deficit irrigation, I = 320 mm; W1: middle deficit irrigation, I = 375 mm; W2: mild deficit irrigation, I = 430 mm; W3: adequate irrigation, I = 480 mm, I represents the amount of irrigation) and four biochar application levels (0 t/hm2 (B0), 10 t/hm2 (B10), 20 t/hm2 (B20), and 30 t/hm2 (B30)). Illumina next-generation sequencing (NGS) technology was used to investigate the changes in the structure and abundance of the soil microbial community in the asparagus rhizosphere using water-biochar coupling treatments. The results showed that moderate deficit irrigation was promoted to form the bacterial abundance and diversity of asparagus rhizosphere soil. The relative abundance of soil fungi increased by 4.48% to 22.05% in the asparagus rhizosphere, with the increasing soil moisture; Biochar application increased the abundance and diversity of rhizosphere soil microorganisms. Furthermore, the number of rhizosphere bacterial communities increased, whereas, the number of fungal communities decreased under the B20 (20 t/hm2) treatment, compared with the B0 and B10 treatments. There was a significant inhibitory in the application of 30 t/hm2 biochar. In addition, there was no significant effect on the structure of bacterial and fungal communities under different water-biochar coupling. The biochar application reduced the growth and reproduction of the root rot causal fungus Fusarium, indicating a better soil microenvironment asparagus rhizosphere. Optimal deficit irrigation was maintained on the microbial diversity and community stability. The higher numbers of soil bacterial communities were observed in the medium and mild deficit irrigation than those in the rest. There was the greatest community richness and diversity of rhizosphere soil bacteria at the medium deficit irrigation level. Therefore, the W1B20 treatment was more favorable to regulating the asparagus rhizosphere ecosystem, where the growth of the bacterial community was promoted to suppress the abundance of harmful fungal communities. This finding can also provide the theoretical basis to regulate the soil microcosm of the asparagus rhizosphere in continuous cropping, particularly for sustainable agriculture in the seasonal arid areas of Southwest China.
京公网安备11010802044758号