Application of Experimental, Numerical, and Machine Learning Techniques to Improve Drying Performance and Decrease Energy Consumption Infrared Continuous Dryer

Case Studies in Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 106025 - 106025

Published: March 1, 2025

Language: Английский

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Improved YOLOv8-Based Segmentation Method for Strawberry Leaf and Powdery Mildew Lesions in Natural Backgrounds

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 525 - 525

Published: Feb. 21, 2025

This study addresses the challenge of segmenting strawberry leaves and lesions in natural backgrounds, which is critical for accurate disease severity assessment automated dosing. Focusing on powdery mildew, we propose an enhanced YOLOv8-based segmentation method leaf lesion detection. Four instance models (SOLOv2, YOLACT, YOLOv7-seg, YOLOv8-seg) were compared, using YOLOv8-seg as baseline. To improve performance, SCDown PSA modules integrated into backbone to reduce redundancy, decrease computational load, enhance detection small objects complex backgrounds. In neck, C2f module was replaced with C2fCIB module, SimAM attention mechanism incorporated target differentiation noise interference. The loss function combined CIOU MPDIOU adaptability challenging scenarios. Ablation experiments demonstrated a accuracy 92%, recall 85.2%, mean average precision (mAP) 90.4%, surpassing baseline by 4%, 2.9%, respectively. Compared SOLOv2, improved model’s mAP increased 14.8%, 5.8%, 3.9%, model reduces missed detections enhances localization, providing theoretical support subsequent applications intelligent, dosage-based management.

Language: Английский

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Computational intelligence and machine learning Approaches for performance evaluation of an infrared dryer: Quality analysis, drying kinetics, and thermal performance

Journal of Stored Products Research, Journal Year: 2025, Volume and Issue: 112, P. 102639 - 102639

Published: March 25, 2025

Language: Английский

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YOLO-BSMamba: A YOLOv8s-Based Model for Tomato Leaf Disease Detection in Complex Backgrounds

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 870 - 870

Published: March 30, 2025

The precise identification of diseases in tomato leaves is great importance for target pesticide application a complex background scenario. Existing models often have difficulty capturing long-range dependencies and fine-grained features images, leading to poor recognition where there are backgrounds. To tackle this challenge, study proposed using YOLO-BSMamba detection mode. We that Hybrid Convolutional Mamba module (HCMamba) integrated within the neck network, with aim improving feature representation by leveraging capture global contextual capabilities State Space Model (SSM) discerning localized spatial convolution. Furthermore, we introduced Similarity-Based Attention Mechanism into C2f improve model’s extraction focusing on disease-indicative leaf areas reducing noise. weighted bidirectional pyramid network (BiFPN) was utilized replace feature-fusion component thereby enhancing performance lesions exhibiting heterogeneous symptomatic gradations enabling model effectively integrate from different scales. Research results showed achieved an F1 score, [email protected], [email protected]:0.95 81.9%, 86.7%, 72.0%, respectively, which represents improvement 3.0%, 4.8%, 4.3%, compared YOLOv8s. Compared other YOLO series models, it achieves best [email protected] score. This provides robust reliable method disease recognition, expected efficiency, further enhance crop monitoring management precision agriculture.

Language: Английский

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Explainable AI in Healthcare Imaging for Medical Diagnosis

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 107 - 120

Published: March 7, 2025

The most cutting-edge machine learning and deep techniques in the healthcare industry are presented by Digital Revolution of AI, with an emphasis on explainable artificial intelligence (XAI). This chapter examines how XAI may advance medical field to increase end users' confidence. It covers new ideas uses XAI, making it a intellectual resource for scholars practitioners interested this developing provides comprehensive explanation AI precision medicine, including all aspects. importance Explainable (XAI) is main topic discussion. Also offers real-world case studies examples offer useful insights into use medicine.

Language: Английский

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