New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

Popescu, Dan; Dinca, Alexandru; Ichim, Loretta; Angelescu, Nicoleta

doi:10.3389/fpls.2023.1268167

Cited by 8 publications

(1 citation statement)

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“…In agricultural production, pests are a continuous concern as they pose potential threats to crop yield and quality. 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.…”

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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