Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection

Jiao, Lin; Li, Gaoqiang; Chen, Peng; Wang, Rujing; Du, Juanjuan; Liu, Haiyun; Dong, Shifeng

doi:10.3389/fpls.2022.895944

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…The numbers in brackets after the name of each class of insects indicate the category ID in FSIP52. At the same time, we also find no intersection between our data set and 27 common stationary-light-trap agricultural pest classes that appear in Jiao et al (2022) and belong to the rarer pest species in the data set. Nevertheless, the FSIP52 data set contains various sizes and poses, and our pre-trained data set and base class data set include three of China's top 10 most harmful, invasive insect species in agroecosystems (Wan and Yang 2016), which indicate that ours is a non-trivial practical approach to preventing the invasion of foreign insect pests.…”

Section: Data Preparationmentioning

confidence: 50%

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Prior knowledge auxiliary for few-shot pest detection in the wild

Wang

Xie

et al. 2023

Front. Plant Sci.

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One of the main techniques in smart plant protection is pest detection using deep learning technology, which is convenient, cost-effective, and responsive. However, existing deep-learning-based methods can detect only over a dozen common types of bulk agricultural pests in structured environments. Also, such methods generally require large-scale well-labeled pest data sets for their base-class training and novel-class fine-tuning, and these significantly hinder the further promotion of deep convolutional neural network approaches in pest detection for economic crops, forestry, and emergent invasive pests. In this paper, a few-shot pest detection network is introduced to detect rarely collected pest species in natural scenarios. Firstly, a prior-knowledge auxiliary architecture for few-shot pest detection in the wild is presented. Secondly, a hierarchical few-shot pest detection data set has been built in the wild in China over the past few years. Thirdly, a pest ontology relation module is proposed to combine insect taxonomy and inter-image similarity information. Several experiments are presented according to a standard few-shot detection protocol, and the presented model achieves comparable performance to several representative few-shot detection algorithms in terms of both mean average precision (mAP) and mean average recall (mAR). The results show the promising effectiveness of the proposed few-shot detection architecture.

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Section: Data Preparationmentioning

confidence: 50%

Section: Data Preparationmentioning

confidence: 50%

Prior knowledge auxiliary for few-shot pest detection in the wild

Wang

Xie

et al. 2023

Front. Plant Sci.

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“…DL techniques, particularly the YOLO series, facilitate rapid feature extraction from vast datasets, ensuring timely and accurate pest detection. The complexities exist in real-world agricultural settings [68], such as intricate backgrounds, overlapping, and occlusion, present considerable challenges for effective pest detection. To address these, we propose YOLOv5spest.…”

Section: Discussionmentioning

confidence: 99%

A Novel Crop Pest Detection Model Based on YOLOv5

Yang,

Qiu

2024

Agriculture

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The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model named YOLOv5s-pest is proposed. Firstly, we design a hybrid spatial pyramid pooling fast (HSPPF) module, which enhances the model’s capability to capture multi-scale receptive field information. Secondly, we design a new convolutional block attention module (NCBAM) that highlights key features, suppresses redundant features, and improves detection precision. Thirdly, the recursive gated convolution (g3Conv) is introduced into the neck, which extends the potential of self-attention mechanism to explore feature representation to arbitrary-order space, enhances model capacity and detection capability. Finally, we replace the non-maximum suppression (NMS) in the post-processing part with Soft-NMS, which improves the missed problem of detection in crowded and dense scenes. The experimental results show that the mAP@0.5 (mean average precision at intersection over union (IoU) threshold of 0.5) of YOLOv5s-pest achieves 92.5% and the mAP@0.5:0.95 (mean average precision from IoU 0.5 to 0.95) achieves 72.6% on the IP16. Furthermore, we also validate our proposed method on other datasets, and the outcomes indicate that YOLOv5s-pest is also effective in other detection tasks.

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“…With the development of machine learning and deep learning, 2 these methods have shown considerable application prospects in pest detection in agriculture. As Lin et al 3 proposed an endto-end convolutional neural network (CNN) detection algorithm combining deformable residual network and global context awareness module to extract pest features. The average accuracy of this method was 77.8% for 21 types of agricultural pests, such as corn borer and cabbage noctuid moth.…”

Section: Introductionmentioning

confidence: 99%

Research on lightweight target detection algorithm of farmland insect pests based on YOLO-PPLCBot

Lü

Zhou

2023

J. Electron. Imag.

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In the agricultural field environment, in order to meet the strict requirements of modern intelligent agriculture on the accuracy and speed of pest detection algorithms, we proposed a lightweight farmland pest detection algorithm based on an improved YOLO v5. First, we constructed a new backbone by combining PPLCNet and BotNet to reduce network parameters and enhance the ability of image feature extraction. Second, the soft non-maximum suppression (Soft-NMS) algorithm is used instead of the NMS algorithm to improve the detection of target feature areas and minimize duplicate detection of the same areas. Then, the scylla intersection over union is applied to replace the original loss function complete intersection over union in the model to obtain higher quality anchors. Besides, mosaic data enhancement, scaling, translation, rotation, and color transformation methods are used to enhance the data volume, and letterbox is employed to unify data set sizes. Finally, we performed performance analysis experiments on a dataset containing 15 species of agricultural pests. From a large number of experimental results, we can conclude that our model improves the precision by 4.3% compared with YOLOv5n, while reducing the reasoning speed and model size by 10.1% and 50%. Furthermore, compared with faster R-CNN, cascade R-CNN, CenterNet, RetinaNet, YOLOv3, YOLOv4, YOLOv5, and YOLOx, our model shows the highest detection accuracy and the fastest reasoning speed. The

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Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection

Cited by 14 publications

References 21 publications

Prior knowledge auxiliary for few-shot pest detection in the wild

Prior knowledge auxiliary for few-shot pest detection in the wild

A Novel Crop Pest Detection Model Based on YOLOv5

Research on lightweight target detection algorithm of farmland insect pests based on YOLO-PPLCBot

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