APest-YOLO: A Multi-Scale Agricultural Pest Detection Model Based on Deep Learning

Abstract

HighlightsWe propose a APest-YOLO model, an innovative agricultural pest detection model founded on a lightweight approach, thus improving the efficiency of pest detection while also reducing the model’s dimensions.The model incorporates a novel grouping atrous spatial pyramid pooling fast module with four convolution layers to enhance multi-scale pest feature representation, aiming for improved detection accuracy. Additionally, it utilizes a convolutional block attention module to reduce noise and complexity in background images, facilitating the extraction of more refined and smoother pest features for accurate detection.We conducted experiments on agricultural pest detection using a comprehensive multi-pest dataset. The APest-YOLO model surpasses existing detection models in terms of mAP0.5, mAP0.5:0.95.Abstract. Crop pests and diseases pose a significant threat to smart agriculture, making pest detection a critical component in agricultural applications. However, current detection methods often struggle to effectively identify multi-scale pest data. In response, we present a novel agricultural pest detection model (APest-YOLO) based on a lightweight approach. The APest-YOLO model enhances pest detection efficiency while reducing model size, which is different from the baseline models. Our model features an original grouping atrous spatial pyramid pooling fast module, comprising four convolution layers with varying rates to capture multi-scale and multi-level pest characteristics. Additionally, we incorporate a convolutional block attention module to extract smoother features from pest images with noisy and complex backgrounds. We evaluated the APest-YOLO model on a large-scale multi-pest dataset encompassing 37 pest species. Furthermore, the APest-YOLO model achieved 99.3% mAP0.5 and found that it outperforms baseline models, demonstrating effective pest species detection capabilities. Keywords: Attention mechanism, Convolutional neural network, Intelligent agriculture, Pest detection, YOLO.