Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(3)

Published: Jan. 17, 2025

Abstract Plant diseases cause significant damage to agriculture, leading substantial yield losses and posing a major threat food security. Detection, identification, quantification, diagnosis of plant are crucial parts precision agriculture crop protection. Modernizing improving production efficiency significantly affected by using computer vision technology for disease diagnosis. This is notable its non-destructive nature, speed, real-time responsiveness, precision. Deep learning (DL), recent breakthrough in vision, has become focal point agricultural protection that can minimize the biases manually selecting spot features. study reviews techniques tools used automatic state-of-the-art DL models, trends DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, reference datasets more than 278 research articles were analyzed subsequently highlighted accordance with architecture deep models. Key findings include effectiveness imaging sensors like RGB, multispectral, hyperspectral cameras early detection. Researchers also evaluated various architectures, such as convolutional neural networks, transformers, generative adversarial language foundation Moreover, connects academic practical applications, providing guidance on suitability these models environments. comprehensive review offers valuable insights into current state future directions detection, making it resource researchers, academicians, practitioners agriculture.

Language: Английский

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Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(7), P. 1821 - 1821

Published: March 29, 2023

The world has seen an increase in the number of wildland fires recent years due to various factors. Experts warn that will continue coming years, mainly because climate change. Numerous safety mechanisms such as remote fire detection systems based on deep learning models and vision transformers have been developed recently, showing promising solutions for these tasks. To best our knowledge, there are a limited published studies literature, which address implementation classification, detection, segmentation As such, this paper, we present up-to-date comprehensive review analysis methods their performances. First, previous works related including reviewed. Then, most popular public datasets used tasks presented. Finally, discusses challenges existing works. Our shows how approaches outperform traditional machine can significantly improve performance detecting, segmenting, classifying wildfires. In addition, main research gaps future directions researchers develop more accurate fields.

Language: Английский

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Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Agronomy, Journal Year: 2024, Volume and Issue: 14(2), P. 341 - 341

Published: Feb. 7, 2024

Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention recent years since it been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change sustainability, have promoted pushed forward the use of agroindustry environmental applications. However, issues with noise confounding signals make these tools non-trivial technical challenge. Great advances artificial intelligence, more particularly machine learning, provided new that allowed researchers improve quality functionality nanosensor systems. This short review presents latest work analysis data from using learning agroenvironmental It consists an introduction topics application field nanosensors. The rest paper examples techniques utilisation electrochemical, luminescent, SERS colourimetric classes. final section discussion conclusion concerning relevance material discussed future sector.

Language: Английский

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Deep Learning Approaches for Medical Image Analysis and Diagnosis

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: May 2, 2024

In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability adaptability of algorithms enable their deployment across diverse clinical settings, ranging from radiology departments point-of-care facilities. Furthermore, ongoing research efforts focus on addressing challenges data heterogeneity, model interpretability, regulatory compliance, paving way for seamless integration solutions into routine practice. As field continues evolve, collaborations between clinicians, scientists, industry stakeholders will be paramount in harnessing full advancing medical image analysis diagnosis. with other technologies, including natural language processing computer vision, may foster multimodal decision support systems care. future diagnosis is promising. With each success advancement, this technology getting closer being leveraged purposes. Beyond analysis, care pathways like imaging, imaging genomics, intelligent operating rooms or intensive units can benefit models.

Language: Английский

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Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray

Journal of X-Ray Science and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Tuberculosis disease is the that causes significant morbidity and mortality worldwide. Thus, early detection of crucial for proper treatment controlling spread disease. Chest X-ray imaging one most widely used diagnostic tools detecting Tuberculosis, which time-consuming, prone to errors. Nowadays, deep learning model provides automated classification medical images with promising outcome. this research introduced a based segmentation model. Initially, Adaptive Gaussian Filtering pre-processing data augmentation performed remove artefacts biased Then, Attention UNet (A_UNet) proposed segmenting required region X-ray. Using segmented outcome, Enhanced Swin Transformer (EnSTrans) designed Residual Pyramid Network Multi-layer perceptron (MLP) layer enhancing accuracy. Lotus Effect Optimization (EnLeO) Algorithm employed loss function optimization EnSTrans The methods acquired Accuracy, Recall, Precision, F-score, Specificity 99.0576%, 98.9459%, 99.145%, 98.96%, 99.152% respectively.

Language: Английский

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Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review

EClinicalMedicine, Journal Year: 2025, Volume and Issue: 81, P. 103089 - 103089

Published: Feb. 18, 2025

Language: Английский

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An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

J — Multidisciplinary Scientific Journal, Journal Year: 2024, Volume and Issue: 7(1), P. 48 - 71

Published: Jan. 22, 2024

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity recent years shown promising results automated medical image analysis, particularly field chest radiology. This paper presents novel DL framework specifically designed for multi-class diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, COVID-19 using images, aiming address need efficient accessible diagnostic tools. The employs convolutional neural network (CNN) architecture with custom blocks enhance feature maps learn discriminative features from images. proposed is evaluated on large-scale dataset, demonstrating superior performance lung. In order evaluate effectiveness presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, specificity improvements. findings study showcased remarkable achieving 98.88%. metrics precision, recall, F1-score, Area Under Curve (AUC) averaged 0.9870, 0.9904, 0.9887, 0.9939 across six-class categorization system. research contributes provides foundation future advancements DL-based systems diseases.

Language: Английский

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Deep learning in medicine: advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: July 5, 2024

Language: Английский

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Ultrasound Image Analysis with Vision Transformers—Review

Diagnostics, Journal Year: 2024, Volume and Issue: 14(5), P. 542 - 542

Published: March 4, 2024

Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges, such as low quality high variability. There is need to develop advanced automatic US image analysis methods enhance diagnostic accuracy objectivity. Vision transformers, recent innovation machine learning, have demonstrated significant potential various research fields, including general computer vision, due their capacity process large datasets learn complex patterns. Their suitability for tasks, classification, detection, segmentation, been recognized. This review provides an introduction vision transformers discusses applications specific while also addressing the open challenges future trends application medical analysis. shown promise enhancing efficiency of ultrasound are expected play increasingly important role diagnosis treatment conditions using technology progresses.

Language: Английский

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Ultrasound Image Analysis with Vision Transformers – Review

Published: Jan. 4, 2024

Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges such as low quality high variability. There is critical need to develop advanced automatic US image analysis methods enhance diagnostic accuracy objectivity. Vision transformer, recent innovation machine learning, demonstrated significant potential various research fields, including general computer vision, due capacity process large datasets learn complex patterns. Its suitability for tasks, classification, detection, segmentation, been recognized. This review provides an introduction vision transformer discusses applications specific while also addressing the open future trends application medical analysis. shown promise enhancing efficiency of ultrasound expected play increasingly important role diagnosis treatment conditions using technology progresses.

Language: Английский

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