Molecular Diagnostics of Soil-Borne and Foliar Diseases of Mulberry: Present Trends and Future Perspective

Gnanesh, Belaghihalli N.; Arunakumar, G. S.; Tejaswi, A.; Supriya, M.; Pappachan, Anil; Harshitha, Maddi

doi:10.1007/978-3-031-28478-6_9

Cited by 2 publications

(1 citation statement)

References 157 publications

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“…During the growth of mulberry leaves, they are inevitably susceptible to various pathogens, which can cause diseases. If these diseases are not detected and controlled early, the infected leaves can quickly spread the pathogens to healthy leaves, leading to a significant reduction in yield or even death of the mulberry plants, causing substantial economic losses to the sericulture industry [3]. Currently, the detection and treatment of mulberry leaf diseases are mainly performed manually.…”

Section: Introductionmentioning

confidence: 99%

Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8

Zhang,

Yuan,

Liu

et al. 2024

Preprint

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Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed Adown down sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the complete intersection over union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieves an mAP50 of 94.3% and an mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3% respectively compared to the original model. The model size is reduced by 0.53 MB to just 5.45 MB, and the number of floating-point operations is reduced by 0.3 G to only 7.8 G. The improved model meets the deployment requirements of mobile embedded devices and can provide a theoretical reference for the automated spraying operations of mulberry leaves.

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Section: Introductionmentioning

confidence: 99%

Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8

Zhang,

Yuan,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8

Zhang,

Yuan,

Liu

et al. 2024

Forests

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Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi-Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial, and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed the ADown down-sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the Complete Intersection over Union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieved a mAP50 of 94.3% and a mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3%, respectively, compared to the original model. The model size was reduced by 0.53 MB to just 5.45 MB, and the GFLOPs were reduced by 0.3 to only 7.8. YOLOv8-RFMD has displayed great potential for application in real-world mulberry leaf disease detection systems and automatic spraying operations.

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Molecular Diagnostics of Soil-Borne and Foliar Diseases of Mulberry: Present Trends and Future Perspective

Cited by 2 publications

References 157 publications

Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8

Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8

Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8

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