PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification

Abstract

Multi-class pest detection is one of the crucial components in pest management involving localization in addition to classification which is much more difficult than generic object detection because of the apparent differences among pest species. This paper proposes a region-based end-to-end approach named PestNet for large-scale multi-class pest detection and classification based on deep learning. PestNet consists of three major parts. First, a novel module channel-spatial attention (CSA) is proposed to be fused into the convolutional neural network (CNN) backbone for feature extraction and enhancement. The second one is called region proposal network (RPN) that is adopted for providing region proposals as potential pest positions based on extracted feature maps from images. Position-sensitive score map (PSSM), the third component, is used to replace fully connected (FC) layers for pest classification and bounding box regression. Furthermore, we apply contextual regions of interest (RoIs) as contextual information of pest features to improve detection accuracy. We evaluate PestNet on our newly collected large-scale pests' image dataset, Multi-class Pests Dataset 2018 (MPD2018) captured by our designed task-specific image acquisition equipment, covering more than 80k images with over 580k pests labeled by agricultural experts and categorized in 16 classes. The experimental results show that the proposed PestNet performs well on multi-class pest detection with 75.46% mean average precision (mAP), which outperforms the state-of-theart methods. INDEX TERMS Channel-spatial attention, convolutional neural network, multi-class pest detection, position-sensitive score map, region proposal network.

Mobile Real-Time Grasshopper Detection and Data Aggregation Framework

“…In addition their 11 method was conducted on a computer with a powerful graphics card and CPU that may not be easily available to a wide diversity of human operators. Liu et al 12 used a novel detection network to detect a number of pests in laboratory settings. Their algorithm achieves 75% mean accuracy precision for their bounding boxes.…”

Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review

“…The model outperformed a CNN trained from scratch, likely due to the relatively small dataset size, which did not contain enough information for proper full model training. A four-part deep learning approach was used in [47] for classification of 16 butterfly species in images of traps. First, channel-spatial attention (CSA) was fused into the CNN backbone for feature extraction and enhancement.…”

“…Situations like this can be very challenging, often leading pest numbers to be considerably underestimated [34,39,40]. Some image processing techniques are capable of partially separating clusters [62], and some deep learning models can successfully deal with crowded images [47]. However, depending on the degree of overlapping, the only solution may be applying statistical correction techniques.…”

Insect Identification Among Deep Learning’s Meta-architectures Using TensorFlow

“…The Random Subspace Classifier (RSC) and Limited Receptive Area (LIRA) recognition rate of 89% and 88% respectively during the study of automatic pest detection on bean and potato crops [7]. There was 75.46% mean average precision (mAP) recorded during similar work of PestNet with large dataset of 80k images with over 580k pests categorized in 16 classes [17].…”