A Detection Algorithm for Citrus Huanglongbing Disease Based on an Improved YOLOv8n.

Given the severe impact of Citrus Huanglongbing on orchard production, accurate detection of the disease is crucial in orchard management. In the natural environments, due to factors such as varying light intensities, mutual occlusion of citrus leaves, the extremely small size of Huanglongbing leaves, and the high similarity between Huanglongbing and other citrus diseases, there remains an issue of low detection accuracy when using existing mainstream object detection models for the detection of citrus Huanglongbing. To address this issue, we propose YOLO-EAF (You Only Look Once-Efficient Asymptotic Fusion), an improved model based on YOLOv8n. Firstly, the Efficient Multi-Scale Attention Module with cross-spatial learning (EMA) is integrated into the backbone feature extraction network to enhance the feature extraction and integration capabilities of the model. Secondly, the adaptive spatial feature fusion (ASFF) module is used to enhance the feature fusion ability of different levels of the model so as to improve the generalization ability of the model. Finally, the focal and efficient intersection over union (Focal-EIOU) is utilized as the loss function, which accelerates the convergence process of the model and improves the regression precision and robustness of the model. In order to verify the performance of the YOLO-EAF method, we tested it on the self-built citrus Huanglongbing image dataset. The experimental results showed that YOLO-EAF achieved an 8.4% higher precision than YOLOv8n on the self-built dataset, reaching 82.7%. The F1-score increased by 3.33% to 77.83%, and the mAP (0.5) increased by 3.3% to 84.7%. Through experimental comparisons, the YOLO-EAF model proposed in this paper offers a new technical route for the monitoring and management of Huanglongbing in smart orange orchards.

Biological effects of gamma-ray radiation on tulip (Tulipa gesneriana L.).

Tulip, being an important ornamental plant, generally requires lengthy and laborious procedures to develop new varieties using traditional breeding methods requires. But ionizing radiation potentially accelerates the breeding process of ornamental plant species. The biological effects of γ-ray irradiation on tulip, therefore, were investigated through establishing an irradiation-mediated mutation breeding protocol to accelerate its breeding process. ISSR-PCR molecular marker technique was further used to identify the mutants of phenotypic variation plants. This study showed that low irradiation doses (5 Gy) stimulated bulb germination to improve the survival rate of tulip, while high irradiation doses (20 to 100 Gy) significantly (P < 0.05) inhibited its seed germination and growth, and decreased the flowering rate, petal number, flower stem length and flower diameter. More than 40 Gy significantly (P < 0.05) decreased the total chlorophyll content and increased the malondialdehyde (MDA) content in tulips. Interestingly, three types of both stigma variations and flower pattern variations, and four types of flower colour variations were observed. With increasing the irradiation dose from 5 to 100 Gy, the anthocyanin and flavonoid contents continuously decreased. Scanning electron microscopy (SEM) analysis evidenced that high irradiation doses altered the micromorphology of leaf stomata. Microscopic observations of tulip root apical mitosis further showed the abnormal chromosomal division behaviour occurring at different mitotic phases under irradiation treatment (80 Gy). Increasing the irradiation dose from 20 to 100 Gy enhanced the micronucleus rate. Moreover, the suspected genetic variation in tulips was evaluated by inter-simple sequence repeat (ISSR) analysis, and the percentage of polymorphic bands was 68%. Finally, this study concludes that that 80 Gy may be an appropriate radiation does to better enhance the efficiency of mutagenic breeds in tulip plants. Using γ-ray irradiation, therefore, is expected to offer a theoretical basis for mutation breeding in tulips.