CEFW-YOLO: A High-Precision Model for Plant Leaf Disease Detection in Natural Environments DOI Creative Commons
J. Tao, Xiaoli Li, Yong He

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(8), P. 833 - 833

Published: April 12, 2025

The accurate and rapid detection of apple leaf diseases is a critical component precision management in orchards. existing deep-learning-based algorithms for typically demand high computational resources, which limits their practical applicability orchard environments. Furthermore, the natural settings faces significant challenges due to diversity disease types, varied morphology affected areas, influence factors such as lighting variations, occlusions, differences severity. To address above challenges, we constructed an (ALD) dataset, was collected from real-world scenarios, applied data augmentation techniques, resulting total 9808 images. Based on ALD proposed lightweight YOLO11n-based network, named CEFW-YOLO, designed tackle current issues identification. First, novel channel-wise squeeze convolution (CWSConv), employs channel compression standard reduce resource consumption, enhance small objects, improve model’s adaptability morphological complex backgrounds. Second, developed enhanced cross-channel attention (ECCAttention) module integrated it into C2PSA_ECCAttention module. By extracting global information, combining horizontal vertical convolutions, strengthening interactions, this enables model more accurately capture features leaves, thereby enhancing accuracy robustness. Additionally, introduced new fine-grained multi-level linear (FMLAttention) module, utilizes asymmetric convolutions mechanisms ability local details detection. Finally, incorporated Wise-IoU (WIoU) loss function, enhances differentiate overlapping targets across multiple scales. A comprehensive evaluation CEFW-YOLO conducted, comparing its performance against state-of-the-art (SOTA) models. achieved 20.6% reduction complexity. Compared original YOLO11n, improved by 3.7%, with [email protected] [email protected]:0.95 increasing 7.6% 5.2%, respectively. Notably, outperformed advanced SOTA detection, underscoring application potential scenarios.

Language: Английский

CEFW-YOLO: A High-Precision Model for Plant Leaf Disease Detection in Natural Environments DOI Creative Commons
J. Tao, Xiaoli Li, Yong He

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(8), P. 833 - 833

Published: April 12, 2025

The accurate and rapid detection of apple leaf diseases is a critical component precision management in orchards. existing deep-learning-based algorithms for typically demand high computational resources, which limits their practical applicability orchard environments. Furthermore, the natural settings faces significant challenges due to diversity disease types, varied morphology affected areas, influence factors such as lighting variations, occlusions, differences severity. To address above challenges, we constructed an (ALD) dataset, was collected from real-world scenarios, applied data augmentation techniques, resulting total 9808 images. Based on ALD proposed lightweight YOLO11n-based network, named CEFW-YOLO, designed tackle current issues identification. First, novel channel-wise squeeze convolution (CWSConv), employs channel compression standard reduce resource consumption, enhance small objects, improve model’s adaptability morphological complex backgrounds. Second, developed enhanced cross-channel attention (ECCAttention) module integrated it into C2PSA_ECCAttention module. By extracting global information, combining horizontal vertical convolutions, strengthening interactions, this enables model more accurately capture features leaves, thereby enhancing accuracy robustness. Additionally, introduced new fine-grained multi-level linear (FMLAttention) module, utilizes asymmetric convolutions mechanisms ability local details detection. Finally, incorporated Wise-IoU (WIoU) loss function, enhances differentiate overlapping targets across multiple scales. A comprehensive evaluation CEFW-YOLO conducted, comparing its performance against state-of-the-art (SOTA) models. achieved 20.6% reduction complexity. Compared original YOLO11n, improved by 3.7%, with [email protected] [email protected]:0.95 increasing 7.6% 5.2%, respectively. Notably, outperformed advanced SOTA detection, underscoring application potential scenarios.

Language: Английский

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