CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

Site-Dependent Luminescence and Thermal Stability of Eu²⁺ Doped Fluorophosphate toward White LEDs for Plant Growth

Eu²⁺ activated fluorophosphate Ba₃GdNa­(PO₄)₃F (BGNPF) with blue and red double-color emitting samples were prepared via a solid-state method in a reductive atmosphere. Their crystal structure and cationic sites were identified in light of X-ray diffraction pattern Rietveld refinement. Three different Ba²⁺ sites, coordinated by six O atoms referred to as Ba1, two F and five O atoms as Ba2, and two F and six O atoms as Ba3, were partially substituted by Eu²⁺. Photoluminescence emission (PL) and excitation (PLE) spectra of phosphor BGNPF:Eu²⁺ along with the lifetimes were characterized at the liquid helium temperature (LHT), which further confirm the existence of three Eu²⁺ emitting centers resulting in 436, 480, and 640 nm emission from the 5d → 4f transitions of Eu²⁺ in three different Ba²⁺ crystallographic sites. These emissions overlap with the absorption spectra of carotenoids and chlorophylls from plants, which could directly promote the photosynthesis. Temperature-dependent PL spectra were used to investigate the thermal stability of phosphor, which indicates that the PL intensity of BGNPF:0.9% Eu²⁺ with optimal composition at 150 °C still keeps 60% of its PL intensity at room temperature, in which blue emission has higher thermal-stability than the red emission. Furthermore, the approaching white LED devices have also been manufactured with a 365 nm n-UV LED chip and present phosphor, which make operators more comfortable than that of the plant growth purple emitting LEDs system composed of blue and red light. Results indicate that this phosphor is an attractive dual-responsive candidate phosphor in the application n-UV light-excited white LEDs for plant growth