Pest species identification algorithm based on improved YOLOv4 network

Song, Limei; Liu, Mengya; Liu, Shenghui; Wang, Hongyi; Luo, Jing

doi:10.1007/s11760-023-02534-x

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…DL has garnered significant attention and application in the domain of pest monitoring [52,53]. Recent researches [54][55][56][57][58][59][60] exhibit the YOLO series' efficacy in addressing diverse crop-related challenges. However, CNNs base on DL approaches, have limitations in practical pest detection scenarios [61], restricting their real-world applicability.…”

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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Section: Discussionmentioning

confidence: 99%

A Novel Crop Pest Detection Model Based on YOLOv5

Yang,

Qiu

2024

Agriculture

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“…In addition to pest identification in the field of jute, in other areas of crop pest identification, we also learned that pest species identification has problems such as small targets being easily lost, dense distribution of pests, individual recognition rate, etc. To improve the efficiency of pest detection further, Song, Limei et al [17] proposed an algorithm for pest species identification based on the YOLOv4 network, DF-YOLO; they introduced the DenseNet network into the YOLOv4 backbone network CSPDarknet53 to introduce DenseNet network to enhance the feature extractor capability of the model, improve the individual recognition rate of densely distributed targets, use the focal loss function to improve the effect of sample imbalance on training and optimize the mining process of complex samples, the algorithm achieved 94.89% mAP after testing on the homemade pest dataset, which is better than the improved the previous YOLOv4 by 4.66%. Wang, Xinming et al [18] compared the performance of two well-known target detection and classification models, YOLOv4 and YOLOv7, in detecting different leaf diseases.…”

Section: Related Workmentioning

confidence: 99%

JutePest-YOLO: A Deep Learning Network for Jute Pest Identification and Detection

Zhang,

Wang,

Zhang

et al. 2024

IEEE Access

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In recent years, jute, as an important natural fiber crop, has become more and more significant in the production process of insect pests, causing serious harm to agricultural production. Especially in the field of crop pest identification with complex backgrounds, fuzzy features, and multiple small targets, the lack of datasets specifically for jute pests has led to the large limitations of traditional pest identification models in terms of generalization. At the same time, the research on models specifically for jute pest detection is still in its infancy. To solve this problem, we constructed a large-scale image dataset containing nine types of jute pests, which was highly targeted and could effectively support model training and evaluation. In this study, we developed a deep convolutional neural network model based on YOLOv7, namely JutePest-YOLO. The model has optimized the Backbone, Head, and loss functions of the baseline model, and introduced the new ELAN-P module and P6 detection layer, which effectively improved the model's ability to identify jute pests in complex backgrounds. The experimental results showed that compared with the baseline model, the Precision, Recall, and F1 scores of the JutePest-YOLO model were improved by 3.45%, 1.76%, and 2.58%, respectively; the

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