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<p>Agricultural pests and diseases critically impact the quality and yield of crops, thereby underscoring the practical importance of their automatic monitoring, identification, and timely management in agricultural production. This study develops a targeted detection model using a deep learning approach, specifically by enhancing the Faster R-CNN algorithm. Modifications were implemented in three key areas of the basic Faster R-CNN: First, the DIOU-NMS technique was employed to optimize the ancillary network during the feature extraction phase. Secondly, an attention mechanism along with an SE module was integrated within the DIOU-NMS to augment the network’s capability. During the training phase, optimization was facilitated through stochastic gradient descent. The efficacy of the refined Faster RCNN model was established via ablation studies, and its performance was benchmarked against existing methodologies for small and general target detection. Results indicate that the enhanced Faster R-CNN framework surpasses conventional small target and generic detection models in accuracy, achieving a 6.4% higher detection rate for various pest categories compared to its predecessor. The findings affirm the potential of the advanced Faster R-CNN in effective agricultural pest detection. Furthermore, this paper advocates a tripartite strategy for pest management, encompassing phytosanitary measures, agricultural interventions, and chemical controls.</p>
</abstract>

Agricultural pests and diseases critically impact the quality and yield of crops, thereby underscoring the practical importance of their automatic monitoring, identification, and timely management in agricultural production. This study develops a targeted detection model using a deep learning approach, specifically by enhancing the Faster R-CNN algorithm. Modifications were implemented in three key areas of the basic Faster R-CNN: First, the DIOU-NMS technique was employed to optimize the ancillary network during the feature extraction phase. Secondly, an attention mechanism along with an SE module was integrated within the DIOU-NMS to augment the network’s capability. During the training phase, optimization was facilitated through stochastic gradient descent. The efficacy of the refined Faster RCNN model was established via ablation studies, and its performance was benchmarked against existing methodologies for small and general target detection. Results indicate that the enhanced Faster R-CNN framework surpasses conventional small target and generic detection models in accuracy, achieving a 6.4% higher detection rate for various pest categories compared to its predecessor. The findings affirm the potential of the advanced Faster R-CNN in effective agricultural pest detection. Furthermore, this paper advocates a tripartite strategy for pest management, encompassing phytosanitary measures, agricultural interventions, and chemical controls.

Agricultural Pest Detection Methods and Control Measures Combining Deep Learning Algorithms

Department of Smart Agriculture Engineering

Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer Science

Nanjing University of Information Science & Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Agricultural pests and diseases critically impact the quality and yield of crops, thereby underscoring the practical importance of their automatic monitoring,...

Agricultural Pest Detection Methods and Control Measures Combining Deep Learning Algorithms

Publicado en línea: 07 jun 2024

Páginas: -

Recibido: 21 feb 2024

Aceptado: 21 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1449

Palabras clave
Target detection model, Faster R-CNN, Stochastic gradient descent method, Attention mechanism, Agricultural pest detection

© 2024 Pengyu Hu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Agricultural Pest Detection Methods and Control Measures Combining Deep Learning Algorithms

Publicado en línea: 07 jun 2024

Páginas: -

Recibido: 21 feb 2024

Aceptado: 21 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1449

Palabras claveTarget detection model, Faster R-CNN, Stochastic gradient descent method, Attention mechanism, Agricultural pest detection

© 2024 Pengyu Hu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Target detection model, Faster R-CNN, Stochastic gradient descent method, Attention mechanism, Agricultural pest detection