A Novel Deep Learning Model for Accurate Pest Detection and Edge Computing Deployment

Abstract

In this work, an attention-mechanism-enhanced method based on a single-stage object detection model was proposed and implemented for the problem of rice pest detection. A multi-scale feature fusion network was first constructed to improve the model’s predictive accuracy when dealing with pests of different scales. Attention mechanisms were then introduced to enable the model to focus more on the pest areas in the images, significantly enhancing the model’s performance. Additionally, a small knowledge distillation network was designed for edge computing scenarios, achieving a high inference speed while maintaining a high accuracy. Experimental verification on the IDADP dataset shows that the model outperforms current state-of-the-art object detection models in terms of precision, recall, accuracy, mAP, and FPS. Specifically, a mAP of 87.5% and an FPS value of 56 were achieved, significantly outperforming other comparative models. These results sufficiently demonstrate the effectiveness and superiority of the proposed method.

Intelligent Cotton Pest and Disease Detection: Edge Computing Solutions with Transformer Technology and Knowledge Graphs

“…By deploying devices with certain computational capabilities, such as smart cameras or sensors, in cotton fields, real-time capture of cotton image data can be achieved, and preliminary image processing and pest detection can be conducted on the device itself. This method not only enables real-time monitoring and rapid response to pest situations, but also reduces the data-processing burden on traditional central servers, enhancing the efficiency and response speed of the entire pest identification system [37]. When implementing edge computing, effective data collection through edge devices is the first step, involving the use of cameras and other equipment to capture real-time images in the cotton fields.…”

Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

“…These techniques are employed for agricultural images for different resolutions, for instance, classification of crop pest and disease sites and varieties. DL-based target detection and classification algorithms are newly developed in traditional investigation, modifying classical image detection methods [9]. These techniques enable machines to adjustably learn image features with no manual feature extraction, therefore, allowing proficient implementation of identification and classification tasks.…”