Citrus leaf diseases significantly affect production efficiency and fruit quality in the citrus industry. To effectively identify and classify citrus leaf diseases, this study proposed a classification approach leveraging deep learning techniques (YOLOV8 equipped with CSPPC, MultiDimen, SpatialConv, YOLOV8-CMS). Additionally, a segmentation method was utilized to extract leaf and lesion areas for disease severity grading based on their pixel ratio. By collecting and preprocessing a citrus leaf image dataset, the YOLOV8-CMS model was trained for disease classification. The model integrated MultiDimen attention, SpatialConv, and the CSPPC module to enhance performance. Furthermore, a segmentation approach was applied to precisely segment both leaf and lesion areas, enabling a quantitative assessment of disease severity. To verify the effectiveness of the proposed approach, multiple YOLO-based architectures, including different YOLOV8 series models, YOLOV5, and YOLOV3, were compared and analyzed. Results demonstrated that the proposed method achieved outstanding performance in citrus leaf disease classification, with an mAP50 of 98.2% in distinguishing healthy and diseased leaves and an accuracy of 97.9% in multi-class disease classification tasks. The proposed YOLOV8-CMS model outperformed traditional methods in citrus leaf disease classification, while the segmentation-based approach enabled an accurate and quantitative assessment of disease severity. These findings highlighted the potential of deep learning in precision agriculture, contributing to more effective disease management in citrus production.

Radar surveillance of noncooperative UAV swarm is challenging and is involved in many critical surveillance scenarios. The multimodality property of dynamic UAV swarm targets presents larger radar signature complexity and elevates the radar detection difficulty. The swarm unit number ambiguity from dense UAV grouping also inhibits radar monitoring accuracy. Inspired by the coherent integration essence of swarm target signals, this paper proposes a radar signal processing framework based on complex valued independent component analysis (cICA) for swarm target identification and quantification. The target detection threshold is determined from pure clutter signals after cICA processing. A customized clustering algorithm is applied on independent components for swarm target quantification. Target detection and quantification methods are verified with various multimodality UAV swarm flight plans. The results indicate that the detection performance of the proposed method is comparable with conventional CFAR algorithms with better stability performance. The target quantification procedure could estimate swarm unit numbers with acceptable numerical deviations. More discussions are given on the relevance between quantification accuracy and swarm configurations with respect to signal independency mechanisms. Efficiency discussions reveal the bottleneck of the proposed method for future optimization works.

Bird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods are required for the harmonious coexistence of airport management and friendly ecology. Existing works indicate the close relationship between bird activities and weather. The relevance of bird activity and weather is favorable for intuitive understanding of ecological environments and providing constructive wildlife management references. This paper introduces a bird activity characterization and forecasting method based on weather information. Bird activities are modeled and quantified into different activity grades. Their relevance with weather parameters is first explored independently to support the multivariable relevance study. Two groups of machine learning strategies are adopted to test their feasibility for bird activity prediction. Radar datasets from diurnal and nocturnal activity study areas are constructed from an avian radar system deployed at the airport. Experimental results verify that both machine learning strategies could achieve bird activity forecasting based on weather information with acceptable accuracy. The random forest model is a better choice for its robustness and adjustability to feature inconsistencies. Weather information deviation between bird activity airspace and ground measurement is a predominant factor limiting the prediction accuracy. The data sufficiency dependency of the prediction model is discussed. Existing works indicate the reasonability and feasibility of the proposed activity modeling and prediction method; more improvements on weather information accuracy and data sufficiency are necessary to further elevate the application significance of the prediction model.

Swarm formulation is a new UAV (Unmanned Aerial Vehicle) derivation with high application significances and military threats. UAV swarms are nonrigid deformable extended radar targets. Their multimodality characters bring higher Radar Cross Section (RCS) variety and statistical uncertainty. Conventional full-wave electromagnetic solvers are problematic in resource consumptions for swarm target RCS modellings and simulations. This paper introduces an efficient UAV swarm RCS restoration framework by integrating EPA (Equivalent Principle Algorithm) and EIM (Empirical Interpolation Method) with particular customization strategies. Each UAV within a swarm is enclosed by an equivalent surface to reduce meshing complexity and maintain unknown consistency. Interpolation points and basis functions are extracted from training spaces to restore electric and magnetic surface currents for arbitrary swarm modalities efficiently. New training parameter selection and solution approximation methods are developed to accommodate swarm target scattering problems. Quadcopter drone and fixed wing UAV swarm models are developed for verification. The proposed method is validated and analyzed from multiple aspects. RCS results present good numerical consistency with benchmark results from simulation and measurement solutions. Its efficiency superiority is distinctive in both memory and time consumptions compared with numerical solvers. This enables the RCS study for arbitrary dynamic UAV swarm configurations. Numerical performance dependence on UAV swarm configurations is studied to explore its applicability conditions. Training parameter optimization and EIM strategy modification are promising to elevate RCS restoration accuracy and efficiency performance of the proposed framework.

High-Resolution Range Profile (HRRP) is effective in various Radar Automatic Target Recognition problems. Multi-scale techniques have been verified in aircraft target HRRP feature enrichment to achieve aircraft recognition performance optimization. The involvement of multiple training-classification procedures in existing multi-scale methods results in tremendous resource consumption which challenges their real-time classification performance and application significance. This paper introduces a novel method that enables multi-scale HRRP features to be manipulated under a single scale for efficiency enhancement. Numerical analysis indicates that multi-scale intensity variations for particular HRRP scatterers could be modeled as Gaussian noises. Linear Discriminant Analysis and Singular Value Decomposition techniques are applied on reconstructed feature vectors for better separability and noise tolerance capability. Experimental results from dynamic aircraft recognition experiments verify the expected efficiency enhancement of the proposed method while maintaining comparable classification accuracy and better noise tolerance performance.

Uncertainty quantification is an important issue in the field of radar cross section (RCS) research. To quantify the impact of specific uncertainty factor on RCS, a novel approach based on the feature selective validation (FSV) method combined with Monte Carlo (MC) method is proposed in this paper. MC method is applied as the basic framework for uncertainty analysis, and FSV is initially employed to compare the results derived from sufficient uncertainty simulations. To facilitate and enhance the massive data assessment, a novel single and direct indicator of FSV is proposed as a quantitative descriptor of data uncertainty. The feasibility of the proposed method in RCS uncertainty quantification is benchmarked through many RCS evaluation examples. The impact of attitude uncertainty on the target RCS, including the scene of dynamic flight, is also studied by the proposed method.