Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

Li, Wei; Zhu, Tengfei; Li, Xiaoyü; Dong, Jianzhang; Li, Jun

doi:10.3390/agriculture12071065

Cited by 31 publications

(9 citation statements)

References 55 publications

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“…Based on our research, we found several studies comparing YOLOv5 to previous versions of YOLO, such as YOLOv4 or YOLOv3, as well as YOLOv5. According to a study provided by Li et al [ 21 ], YOLOv5 was more efficient and about 2.5 times faster. YOLOv3-AFF was compared to YOLOv3, and mAP was increased by 10.18% [ 22 ].…”

Section: Experimental Results and Analysesmentioning

confidence: 99%

“…In recent years, YOLO-based target detection algorithms have been successfully applied in pest detection. For instance, YOLOv5 was more efficient at detecting insect pests with higher accuracy and about 2.5 times faster when the background images were simple, such as the Baidu AI insect detection dataset [ 21 ]. To improve the accuracy of insect identification, the YOLOv3-AFF network with the adaptive feature fusion was employed to reuse features at various scales, and it achieved 72.10% accuracy on the Pest24 dataset [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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A New Pest Detection Method Based on Improved YOLOv5m

Dai

Dorjoy

Miao

et al. 2023

Insects

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Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection in plants with higher accuracy is proposed by an improved YOLOv5m-based method. First, the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms are introduced into the YOLOv5m network so that they can capture more global features and can increase the receptive field. Then, in the backbone, ResSPP is considered to make the network extract more features. Furthermore, the global features of the feature map are extracted in the feature fusion phase and forwarded to the detection phase via a modification of the three output necks C3 into SWinTR. Finally, WConcat is added to the fusion feature, which increases the feature fusion capability of the network. Experimental results demonstrate that the improved YOLOv5m achieved 95.7% precision rate, 93.1% recall rate, 94.38% F1 score, and 96.4% Mean Average Precision (mAP). Meanwhile, the proposed model is significantly better than the original YOLOv3, YOLOv4, and YOLOv5m models. The improved YOLOv5m model shows greater robustness and effectiveness in detecting pests, and it could more precisely detect different pests from the dataset.

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Section: Experimental Results and Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Pest Detection Method Based on Improved YOLOv5m

Dai

Dorjoy

Miao

et al. 2023

Insects

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“…Therefore, researchers have used DL-based object-detection models to overcome the limitation of the CNN-based methods. For instance, Li et al [ 25 ] utilized the IPI02 dataset to detect insects in fields using various DCN networks, including Faster-RCNN, Mask-RCNN, and YOLOv5. They obtained promising results and demonstrated that YOLOv5 outperformed Faster-RCNN and Mask-RCNN, which attained 99% accuracy, whereas YOLOv5 gained 97%.…”

Section: Literature Reviewmentioning

confidence: 99%

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Khan,

Malebary,

Dang

et al. 2024

Plants

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Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.

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“…A precision of 53.6% was reached in this investigation using the IP102 dataset. Li et al [42] implemented the Mask-RCNN ResNet50, Faster-RCNN ResNet101, and Yolov5 Darknet53 models for pest recognition. These methods each reached accuracy levels of 99.6%, 99.4%, and 97.6%.…”

Section: Literature Reviewmentioning

confidence: 99%

Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification

Ali,

Qayyum,

Iqbal

2023

IEEE Access

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One of the most significant risks impacting crops is pests, which substantially decrease food production. Further, prompt and precise recognition of pests can help harvesters save damage and enhance the quality of crops by enabling them to take appropriate preventive action. The apparent resemblance between numerous kinds of pests makes examination laborious and takes time. The limitations of physical pest inspection are required to be addressed, and a novel deep-learning approach called the Faster-PestNet is proposed in this work. Descriptively, an improved Faster-RCNN approach is designed using the MobileNet as its base network and tuned on the pest samples to recognize the crop pests of various categories and given the name of Fatser-PestNet. Initially, the MobileNet is employed for extracting a distinctive set of sample attributes, later recognized by the 2-step locator of the improved Faster-RCNN model. We have accomplished a huge experimentation analysis over a complicated data sample named the IP102 and acquired an accuracy of 82.43%. Further, a local crops dataset is also collected and tested on the trained Faster-PestNet approach to show the generalization capacity of the proposed model. We have confirmed through analysis that the presented work can tackle numerous sample distortions like noise, blurring, light variations, and size alterations and can accurately locate the pest along with the associated class label on the leaf of numerous types and sizes. Both visual and stated performance values confirm the effectiveness of our model.

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Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection

Cited by 31 publications

References 55 publications

A New Pest Detection Method Based on Improved YOLOv5m

A New Pest Detection Method Based on Improved YOLOv5m

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification

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