Chinese chive (Allium tuberosum) suffers severe yield and quality losses from white leaf spot disease caused by Alternaria alternata. Spray-induced gene silencing (SIGS) presents a sustainable alternative to traditional chemical fungicides. To maximize the biocontrol efficacy of this approach, we designed dsRNAs targeting two candidate virulence-associated genes of Alternaria alternata: AaGH10, encoding a cell wall-degrading enzyme critical for host penetration, and AaSOD, an antioxidant enzyme crucial for reactive oxygen species (ROS) scavenging. We comprehensively evaluated the antifungal efficacy by assessing mycelial growth inhibition, spore germination, and lesion development through in vitro and in vivo assays. The results demonstrated that both single and combinatorial treatments effectively inhibited fungal growth and spore germination, thereby reducing disease incidence on detached leaves and intact greenhouse plants. The application of the dual-target dsRNA formulation achieved an 86.7% control efficacy on detached leaves. Furthermore, it significantly alleviated in vivo disease severity, decreasing the average number of necrotic lesions from 15.6 to 1.3 per leaf. These results demonstrate that the dual-target combination exerts an enhanced protective effect compared with individual interventions. This specific dual-target dsRNA formulation establishes a robust foundation for the control of Chinese chive white leaf spot disease.
Lian-Zhe Wang, Ke-Hao Huang, Yi-Xian Gou et al.· Journal of Fungi· 0 citations
Introduction Accurate and rapid diagnosis of plant leaf disease symptoms is critical for sustainable agricultural crop production, yet traditional methods often lack efficiency and robustness under field conditions. Methods Here, we propose a deep learning framework based on an improved Vision Transformer architecture that integrates a dynamic sparse attention mechanism, termed KBTNet, for targeted feature extraction in symptom-affected regions of leaf images. The model incorporates a learnable nonlinear enhancement module to capture subtle visual disease symptom variations such as lesions, discoloration patterns, and spot distributions, and a lightweight Transformer design to reduce computational cost. Results Evaluated on a multisource dataset containing soybean and tomato leaf images representing diverse disease symptom patterns, our approach achieved 93.19% classification accuracy, outperforming current state-of-the-art models. Additional evaluations on public plant disease datasets from multiple crops further demonstrate the model's ability to recognize disease symptom patterns across diverse crop species. Discussion The proposed framework achieves stable performance across diverse crop and disease symptom categories, maintains high efficiency under reduced parameter complexity, and exhibits strong potential for realtime field diagnostics on edge devices. This work provides a scalable and efficient tool for plant disease symptom detection and classification and supports the integration of visionbased intelligence into crop disease monitoring and management systems.
Yunqin Shen, Mengyuan Zhu, Tao Hu et al.· Frontiers in Plant Science· 0 citations
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