Assessment of Peacock Spot Disease (Fusicladium oleagineum) in Olive Orchards Through Agronomic Approaches and UAV-Based Multispectral Imaging

Horticulturae, Год журнала: 2025, Номер 11(1), С. 46 - 46

Опубликована: Янв. 6, 2025

Olive leaf spot (OLS), caused by Fusicladium oleagineum, is a significant disease affecting olive orchards, leading to reduced yields and compromising tree health. Early accurate detection of this critical for effective management. This study presents comprehensive assessment OLS progression in orchards integrating agronomic measurements multispectral imaging techniques. Key parameters—incidence, severity, diseased area, index—were systematically monitored from March October, revealing peak values 45% incidence April 35% severity May. Multispectral drone imagery, using sensors NIR, Red, Green, Red Edge spectral bands, enabled the calculation vegetation indices. Indices incorporating near-infrared such as SR705-750, exhibited strongest correlations with (correlation coefficients 0.72 0.68, respectively). combined approach highlights potential remote sensing early supports precision agriculture practices facilitating targeted interventions optimized orchard The findings underscore effectiveness traditional advanced analysis improve surveillance promote sustainable cultivation.

Язык: Английский

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Optical imaging combined with artificial intelligence in plant disease detection: a comprehensive review

Spectroscopy Letters, Год журнала: 2025, Номер unknown, С. 1 - 25

Опубликована: Фев. 23, 2025

Язык: Английский

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Insights into the effects of elevated atmospheric carbon dioxide on plant-virus interactions: A literature review

Environmental and Experimental Botany, Год журнала: 2024, Номер 221, С. 105737 - 105737

Опубликована: Март 15, 2024

Understanding and anticipating the impacts of climate change on plant-pathogen interactions are a major challenge for agriculture 21st century. Prediction models forecast an increase in atmospheric carbon dioxide (CO2) levels by 2100 that could reach 1045 ppm. Plant physiology is directly affected CO2 as plants living organisms consume through photosynthesis to produce organic matter. Since early days agriculture, plant diseases can alter not only quality productions but also be responsible important yield losses. viruses obligate, acellular pathogens cause serious epidemics agricultural crops with annual losses more than $ 30 billion. As elevated concentration (eCO2) modulates primary secondary metabolisms obligate pathogens, it likely eCO2 modulate molecular defenses viruses. In context, present review focuses effect physiological responses virus infections. First, we will compare different experimental methodologies used study impact enrichment plant-virus discuss designs applied experiments. We virus-infection parameters infected describe combined abiotic stresses, including temperature, interactions.

Язык: Английский

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Phytoremediation Strategies for Heavy Metal Contamination: A Review on Sustainable Approach for Environmental Restoration

Journal of Environmental Protection, Год журнала: 2024, Номер 15(04), С. 450 - 474

Опубликована: Янв. 1, 2024

Язык: Английский

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Advancements in Disposable Electrochemical Systems for Sustainable Agriculture Monitoring: Trends, Gaps, and Applied Examples

TrAC Trends in Analytical Chemistry, Год журнала: 2024, Номер 180, С. 117968 - 117968

Опубликована: Сен. 11, 2024

Язык: Английский

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A Novel Few-Shot Learning Framework Based on Diffusion Models for High-Accuracy Sunflower Disease Detection and Classification

Plants, Год журнала: 2025, Номер 14(3), С. 339 - 339

Опубликована: Янв. 23, 2025

The rapid advancement in smart agriculture has introduced significant challenges, including data scarcity, complex and diverse disease features, substantial background interference agricultural scenarios. To address these a detection method based on few-shot learning diffusion generative models is proposed. By integrating the high-quality feature generation capabilities of with extraction advantages learning, an end-to-end framework for been constructed. experimental results demonstrate that proposed achieves outstanding performance tasks. Across comprehensive experiments, model achieved scores 0.94, 0.92, 0.93, 0.92 precision, recall, accuracy, mean average precision (mAP@75), respectively, significantly outperforming other comparative models. Furthermore, incorporation attention mechanisms effectively enhanced quality representations improved model’s ability to capture fine-grained features.

Язык: Английский

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Sustainable Management of Major Fungal Phytopathogens in Sorghum (Sorghum bicolor L.) for Food Security: A Comprehensive Review

Journal of Fungi, Год журнала: 2025, Номер 11(3), С. 207 - 207

Опубликована: Март 6, 2025

Sorghum (Sorghum bicolor L.) is a globally important energy and food crop that becoming increasingly integral to security the environment. However, its production significantly hampered by various fungal phytopathogens affect yield quality. This review aimed provide comprehensive overview of major affecting sorghum, their impact, current management strategies, potential future directions. The diseases covered include anthracnose, grain mold complex, charcoal rot, downy mildew, rust, with an emphasis on pathogenesis, symptomatology, overall economic, social, environmental impacts. From initial use fungicides shift biocontrol, rotation, intercropping, modern tactics breeding resistant cultivars against mentioned are discussed. In addition, this explores disease management, particular focus role technology, including digital agriculture, predictive modeling, remote sensing, IoT devices, in early warning, detection, management. It also key policy recommendations support farmers advance research thus emphasizing need for increased investment research, strengthening extension services, facilitating access necessary inputs, implementing effective regulatory policies. concluded although pose significant challenges, combined effort innovative policies can mitigate these issues, enhance resilience sorghum facilitate global issues.

Язык: Английский

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Biotechnological Revolution in Agrifood Systems: Multidisciplinary Approaches for the Diagnosis, Management, and Epidemiology of Plant Diseases

Horticulturae, Год журнала: 2025, Номер 11(3), С. 300 - 300

Опубликована: Март 10, 2025

Agrifood systems have been disrupted for centuries across the globe by a plethora of plant pathogens such as bacteria, viruses, and fungi [...]

Язык: Английский

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Redes Neuronales Convolucionales para la Clasificación de la Mancha Negra en los Cítricos

Опубликована: Март 16, 2025

Se presenta un innovador modelo de visión artificial basado en redes neuronales convolucionales (CNN) para la clasificación mancha negra los cítricos. Este estudio adopta una metodología que fusiona Investigación y Desarrollo con principios ágiles Scrum. La evaluación comparativa modelos existentes cítricos diferentes contextos demuestra nuestro muestra diferencias significativas precisión respecto a B C. El análisis estadístico, incluyendo prueba McNemar, confirma eficacia del modelo, resaltando su fiabilidad competitividad detección enfermedades Los resultados obtenidos no solo proporcionan eficiente cítricos, sino también promueven el avance aplicación inteligencia agricultura. enfoque sugiere nuevas direcciones investigación subraya importancia mejora salud cultivos. implementación este puede reducir pérdidas económicas optimizar productividad, aportando beneficios significativos tanto agricultores como industria agrícola.

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YOLOv11-RCDWD: A New Efficient Model for Detecting Maize Leaf Diseases Based on the Improved YOLOv11

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4535 - 4535

Опубликована: Апрель 20, 2025

Detecting pests and diseases on maize leaves is challenging. This especially true under complex conditions, such as variable lighting occlusion. Current methods suffer from low detection accuracy. They also lack sufficient real-time performance. Hence, this study introduces the lightweight method YOLOv11-RCDWD based an improved YOLOv11 model. The proposed approach enhances model by incorporating RepLKNet module backbone, which significantly model’s capacity to capture characteristics of leaf diseases. Additionally, CBAM embedded within neck feature extraction network further refine representation augment capability identify select essential features introducing attention mechanisms in both channel spatial dimensions, thereby improving accuracy expression. We have DynamicHead module, WIoU loss function, DynamicATSS label assignment strategy, collectively enhance accuracy, efficiency, robustness through optimized mechanisms, better handling low-quality samples, dynamic sample selection during training. experimental findings indicate that effectively detected leaves. precision reached 92.6%, while recall was 85.4%. F1 score 88.9%, [email protected] [email protected]~0.95 demonstrated improvement 4.9% 9.0% over baseline YOLOv11s. Notably, outperformed other architectures Faster R-CNN, SSD, various models YOLO series, demonstrating superior capabilities terms speed, parameter count, computational memory utilization. achieves optimal balance between performance resource efficiency. Overall, reduces time usage maintaining high supporting automated diseases, offering a robust solution for intelligent monitoring agricultural pests.

Язык: Английский

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