Intelligent Recognition of Goji Berry Pests Using CNN With Multi‐Graphic‐Occlusion Data Augmentation and Multiple Attention Fusion Mechanisms DOI

Jing Ni

Archives of Insect Biochemistry and Physiology, Journal Year: 2025, Volume and Issue: 118(4)

Published: April 22, 2025

ABSTRACT Goji berry is an important economic crop, yet pest infestations pose a significant threat to its yield and quality. Traditional identification mainly relies on manual inspection by experts with specialized knowledge, which subjective, time‐consuming, labor‐intensive. To address these issues, this experiment proposes improved convolutional neural network (CNN) for accurate of 17 types goji pests. Firstly, the original data set augmented using multi‐graph‐occlusion augmentation method. Subsequently, imported into CNN training. Based ResNet18 model, new CNN, named GojiNet, constructed embedding multi‐attention fusion modules at appropriate locations. Experimental results demonstrate that GojiNet achieves average recognition accuracy 95.35%, representing 2.60% improvement over network. Notably, compared network, training time model increases only slightly, while size reduced, enhanced. The verifies performance through series evaluation indicators. This study confirms tremendous potential application prospects deep learning in identification, providing referential solution intelligent precise identification.

Language: Английский

Intelligent Recognition of Goji Berry Pests Using CNN With Multi‐Graphic‐Occlusion Data Augmentation and Multiple Attention Fusion Mechanisms DOI

Jing Ni

Archives of Insect Biochemistry and Physiology, Journal Year: 2025, Volume and Issue: 118(4)

Published: April 22, 2025

ABSTRACT Goji berry is an important economic crop, yet pest infestations pose a significant threat to its yield and quality. Traditional identification mainly relies on manual inspection by experts with specialized knowledge, which subjective, time‐consuming, labor‐intensive. To address these issues, this experiment proposes improved convolutional neural network (CNN) for accurate of 17 types goji pests. Firstly, the original data set augmented using multi‐graph‐occlusion augmentation method. Subsequently, imported into CNN training. Based ResNet18 model, new CNN, named GojiNet, constructed embedding multi‐attention fusion modules at appropriate locations. Experimental results demonstrate that GojiNet achieves average recognition accuracy 95.35%, representing 2.60% improvement over network. Notably, compared network, training time model increases only slightly, while size reduced, enhanced. The verifies performance through series evaluation indicators. This study confirms tremendous potential application prospects deep learning in identification, providing referential solution intelligent precise identification.

Language: Английский

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