Research on the Application of Convolutional Neural Network Based on YOLO Algorithm in Pest Small Target Detection

Kang, Gwangwon; Hou, Le; Zhao, Zhuo; Lang, Bingbing

doi:10.1109/acctcs58815.2023.00074

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2023

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A High Accuracy Deep Learning and Iot Network for Pest Detection Using Analysis of The Pest Sound in Large Agriculture Area

Ali,

Sharma,

Dhanaraj

2023

2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)

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A High Accuracy Deep Learning and Iot Network for Pest Detection Using Analysis of The Pest Sound in Large Agriculture Area

Ali,

Sharma,

Dhanaraj

2023

2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)

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LCA-Net: A Lightweight Cross-Stage Aggregated Neural Network for Fine-Grained Recognition of Crop Pests and Diseases

Kong,

Xiao,

Jin

et al. 2023

Agriculture

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In the realm of smart agriculture technology’s rapid advancement, the integration of various sensors and Internet of Things (IoT) devices has become prevalent in the agricultural sector. Within this context, the precise identification of pests and diseases using unmanned robotic systems assumes a crucial role in ensuring food security, advancing agricultural production, and maintaining food reserves. Nevertheless, existing recognition models encounter inherent limitations such as suboptimal accuracy and excessive computational efforts when dealing with similar pests and diseases in real agricultural scenarios. Consequently, this research introduces the lightweight cross-layer aggregation neural network (LCA-Net). To address the intricate challenge of fine-grained pest identification in agricultural environments, our approach initially enhances the high-performance large-scale network through lightweight adaptation, concurrently incorporating a channel space attention mechanism. This enhancement culminates in the development of a cross-layer feature aggregation (CFA) module, meticulously engineered for seamless mobile deployment while upholding performance integrity. Furthermore, we devised the Cut-Max module, which optimizes the accuracy of crop pest and disease recognition via maximum response region pruning. Thorough experimentation on comprehensive pests and disease datasets substantiated the exceptional fine-grained performance of LCA-Net, achieving an impressive accuracy rate of 83.8%. Additional ablation experiments validated the proposed approach, showcasing a harmonious balance between performance and model parameters, rendering it suitable for practical applications in smart agricultural supervision.

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Research on the Application of Convolutional Neural Network Based on YOLO Algorithm in Pest Small Target Detection

Cited by 2 publications

References 0 publications

A High Accuracy Deep Learning and Iot Network for Pest Detection Using Analysis of The Pest Sound in Large Agriculture Area

A High Accuracy Deep Learning and Iot Network for Pest Detection Using Analysis of The Pest Sound in Large Agriculture Area

LCA-Net: A Lightweight Cross-Stage Aggregated Neural Network for Fine-Grained Recognition of Crop Pests and Diseases

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