The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-dema...The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.展开更多
Due to the lack of large-scale emotion databases,it is hard to obtain comparable improvement in multimodal emotion recognition of the deep neural network by deep learning,which has made great progress in other areas.W...Due to the lack of large-scale emotion databases,it is hard to obtain comparable improvement in multimodal emotion recognition of the deep neural network by deep learning,which has made great progress in other areas.We use transfer learning to improve its performance with pretrained models on largescale data.Audio is encoded using deep speech recognition networks with 500 hours’speech and video is encoded using convolutional neural networks with over 110,000 images.The extracted audio and visual features are fed into Long Short-Term Memory to train models respectively.Logistic regression and ensemble method are performed in decision level fusion.The experiment results indicate that 1)audio features extracted from deep speech recognition networks achieve better performance than handcrafted audio features;2)the visual emotion recognition obtains better performance than audio emotion recognition;3)the ensemble method gets better performance than logistic regression and prior knowledge from micro-F1 value further improves the performance and robustness,achieving accuracy of 67.00%for“happy”,54.90%for“an?gry”,and 51.69%for“sad”.展开更多
Breast cancer is a common cause of death among women worldwide.Ultrasonic imaging is a valuable diagnostic tool in breast cancer detection.However,the accuracy of computer-aided diagnosis systems for breast cancer cla...Breast cancer is a common cause of death among women worldwide.Ultrasonic imaging is a valuable diagnostic tool in breast cancer detection.However,the accuracy of computer-aided diagnosis systems for breast cancer classification is limited due to the lack of well-annotated datasets.This study proposes a deep learning(DL)-based framework for breast mass classification using ultrasound images,which incorporates a novel data augmentation technique,generative adversarial network(GAN),and transfer learning(TL).Automating early tumor identification and classification in breast cancer diagnosis can save lives by improving the accuracy of diagnoses and reducing the need for invasive procedures.However,the limited availability of wellannotated datasets for ultrasound images of breast cancer has hampered the development of accurate computer-aided diagnosis systems.The accuracy of breast mass classification using ultrasound images is limited due to the lack of well-annotated datasets.Conventional data augmentation techniques have limitations in applications with strict guidelines,such as medical datasets.Therefore,there is a need to develop a novel data augmentation technique to improve the accuracy of breast mass classification using ultrasound images.The proposed framework can be extended to other medical imaging applications,where the availability of well-annotated datasets is limited.The GAN-based data augmentation technique and TL-based feature extraction can be used to improve the accuracy of classification models in other medical imaging applications.Additionally,the proposed framework can be used to develop accurate computer-aided diagnosis systems for breast cancer detection in clinical settings.The proposed framework incorporates a DL-based approach for breast mass classification using ultrasound images.The framework includes a GAN-based data augmentation technique and TL for feature extraction.The dataset used for training and testing the model is the breast ultrasound images(BUSI)dataset,which includes 1311 images with normal and abnormal breast masses.The proposed framework achieved an accuracy of 99.6%for breast mass classification using ultrasound images,which outperformed existing methods.The GAN-based data augmentation technique and TL-based feature extraction improved the accuracy of the classification model.The results suggest that DL algorithms can be effectively applied for breast ultrasound categorization.The proposed framework presents a novel approach for breast mass classification using ultrasound images,which incorporates a GAN-based data augmentation technique and TL-based feature extraction.The results demonstrate that the proposed framework outperforms existing methods and achieves high accuracy in breast mass classification using ultrasound images.This framework can be useful for developing accurate computer-aided diagnosis systems for breast cancer detection.展开更多
Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for d...Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0°to±90°.We initially selected the most suitable feature vector size by integrating the Dlib,FaceNet(Inception-v2),and“Support Vector Machines(SVM)”+“K-nearest neighbors(KNN)”algorithms.To train and evaluate this feature vector,we used two datasets:the“Labeled Faces in the Wild(LFW)”benchmark data and the“Robust Shape-Based FR System(RSBFRS)”real-time data,which contained face images with varying yaw poses.After selecting the best feature vector,we developed a real-time FR system to handle yaw poses.The proposed FaceNet architecture achieved recognition accuracies of 99.7%and 99.8%for the LFW and RSBFRS datasets,respectively,with 128 feature vector dimensions and minimum Euclidean distance thresholds of 0.06 and 0.12.The FaceNet+SVM and FaceNet+KNN classifiers achieved classification accuracies of 99.26%and 99.44%,respectively.The 128-dimensional embedding vector showed the highest recognition rate among all dimensions.These results demonstrate the effectiveness of our proposed approach in enhancing FR accuracy,particularly in real-world scenarios with varying yaw poses.展开更多
Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavi...Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavily on laboratory data,which differ signifi-cantly from that under the complex conditions of field data,leading to a marked drop in recognition accuracy when they are applied to field PD diagnosis.This study addresses the challenge by integrating field data into the training process,utilising a deep transfer learning approach that combines laboratory and field data to improve diagnostic accuracy for GIS PD.The research collected PD data from laboratory models representing five defect types and field data gathered from operational GIS equipment.A deep residual network(ResNet50)was pretrained using laboratory data and fine-tuned with field data through deep transfer learning to optimise the recognition of PD in field conditions.The results show that the proposed model achieves a significantly higher recognition accuracy(93.7%)for field data compared to traditional methods(60%-70%).The integration of deep transfer learning ensures that both low-dimensional general features from labora-tory data and high-dimensional specific features from field data are effectively utilised.This research significantly contributes to improving the diagnostic accuracy of PD in GIS under field conditions,providing a robust method for defect detection in operational equipment.展开更多
This paper proposes an optimal,robust,and efficient guidance scheme for the perturbed minimum-time low-thrust transfer toward the geostationary orbit.The Earth’s oblateness perturbation and shadow are taken into acco...This paper proposes an optimal,robust,and efficient guidance scheme for the perturbed minimum-time low-thrust transfer toward the geostationary orbit.The Earth’s oblateness perturbation and shadow are taken into account.It is difficult for a Lyapunov-based or trajectory-tracking guidance method to possess multiple characteristics at the same time,including high guidance optimality,robustness,and onboard computational efficiency.In this work,a concise relationship between the minimum-time transfer problem with orbital averaging and its optimal solution is identified,which reveals that the five averaged initial costates that dominate the optimal thrust direction can be approximately determined by only four initial modified equinoctial orbit elements after a coordinate transformation.Based on this relationship,the optimal averaged trajectories constituting the training dataset are randomly generated around a nominal averaged trajectory.Five polynomial regression models are trained on the training dataset and are regarded as the costate estimators.In the transfer,the spacecraft can obtain the real-time approximate optimal thrust direction by combining the costate estimations provided by the estimators with the current state at any time.Moreover,all these computations onboard are analytical.The simulation results show that the proposed guidance scheme possesses extremely high guidance optimality,robustness,and onboard computational efficiency.展开更多
盐体是具有良好气密性的地质构造,有利于油气储存,实现精细化盐体的解释极为必要。然而,不同于断层,盐体的特征较为复杂且形态差异大,常规方法易导致混淆和误判。此外,基于数据驱动的盐体识别模型在实际数据集上的泛化能力较差,因此目...盐体是具有良好气密性的地质构造,有利于油气储存,实现精细化盐体的解释极为必要。然而,不同于断层,盐体的特征较为复杂且形态差异大,常规方法易导致混淆和误判。此外,基于数据驱动的盐体识别模型在实际数据集上的泛化能力较差,因此目前在地震勘探中进行盐体的解释及可视化仍存在挑战。文章将盐体解释视为地震图像的语义分割问题,提出了基于迁移学习的上下文融合与混合注意力的智能盐体分割(Multi-path structure Mixed Attention and Transfer Optimized Net,MMTONet)方法。同时设计了一种基于盐体上下文特征融合模块,进而建立了改进注意力卷积混合的跳跃连接机制,以更好地弥补由下采样造成的信息损失,从而提高模型对盐体边界与高振幅噪声的像素级辨别能力。在此基础上,还设计了迁移学习的适配器微调策略,提升了模型在实际数据上的泛化能力。在地震数据集上的实验结果表明,MMTONet在提高分割精度和减少计算量、参数量方面均优于主流的语义分割方法。展开更多
Artificial intelligence(AI),which has recently gained popularity,is being extensively employed in modern fault diagnostic research to preserve the reliability and productivity of machines.The effectiveness of AI is in...Artificial intelligence(AI),which has recently gained popularity,is being extensively employed in modern fault diagnostic research to preserve the reliability and productivity of machines.The effectiveness of AI is influenced by the quality of the labeled training data.However,in engineering scenarios,available data on mechanical equipment are scarce,and collecting massive amounts of well-annotated fault data to train AI models is expensive and difficult.In response to the inadequacy of training samples,a numerical simulation-based partial transfer learning method for machinery fault diagnosis is proposed.First,a suitable simulation model of critical components in a mechanical system is developed using the finite element method(FEM),and numerical simulation is performed to acquire FEM simulation samples containing different fault types.Second,several synthetic simulation samples are generated to form complete source domain training samples using a generative adversarial network.Subsequently,the partial transfer learning network is trained to extract shared fault characteristics between the simulation and measured samples in the case of class imbalance.Finally,the resulting model is used to diagnose unknown samples from real-world mechanical systems in operation.The proposed method is tested on actual fault samples of bearings and gears obtained from a public dataset and experimental test rig available in our laboratory,achieving average classification accuracy of 99.54%and 99.64%,respectively.Comparison investigations reveal that the proposed method has superior classification and generalization ability when detecting faults in real mechanical systems.展开更多
Grains are the most important food consumed globally,yet their yield can be severely impacted by pest infestations.Addressing this issue,scientists and researchers strive to enhance the yield-to-seed ratio through eff...Grains are the most important food consumed globally,yet their yield can be severely impacted by pest infestations.Addressing this issue,scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods.Traditional approaches often rely on preprocessed datasets,but there is a growing need for solutions that utilize real-time images of pests in their natural habitat.Our study introduces a novel twostep approach to tackle this challenge.Initially,raw images with complex backgrounds are captured.In the subsequent step,feature extraction is performed using both hand-crafted algorithms(Haralick,LBP,and Color Histogram)and modified deep-learning architectures.We propose two models for this purpose:PestNet-EF and PestNet-LF.PestNet-EF uses an early fusion technique to integrate handcrafted and deep learning features,followed by adaptive feature selection methods such as CFS and Recursive Feature Elimination(RFE).PestNet-LF utilizes a late fusion technique,incorporating three additional layers(fully connected,softmax,and classification)to enhance performance.These models were evaluated across 15 classes of pests,including five classes each for rice,corn,and wheat.The performance of our suggested algorithms was tested against the IP102 dataset.Simulation demonstrates that the Pestnet-EF model achieved an accuracy of 96%,and the PestNet-LF model with majority voting achieved the highest accuracy of 94%,while PestNet-LF with the average model attained an accuracy of 92%.Also,the proposed approach was compared with existing methods that rely on hand-crafted and transfer learning techniques,showcasing the effectiveness of our approach in real-time pest detection for improved agricultural yield.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52008402)the Central South University autonomous exploration project(Grant No.2021zzts0790).
文摘The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.
文摘Due to the lack of large-scale emotion databases,it is hard to obtain comparable improvement in multimodal emotion recognition of the deep neural network by deep learning,which has made great progress in other areas.We use transfer learning to improve its performance with pretrained models on largescale data.Audio is encoded using deep speech recognition networks with 500 hours’speech and video is encoded using convolutional neural networks with over 110,000 images.The extracted audio and visual features are fed into Long Short-Term Memory to train models respectively.Logistic regression and ensemble method are performed in decision level fusion.The experiment results indicate that 1)audio features extracted from deep speech recognition networks achieve better performance than handcrafted audio features;2)the visual emotion recognition obtains better performance than audio emotion recognition;3)the ensemble method gets better performance than logistic regression and prior knowledge from micro-F1 value further improves the performance and robustness,achieving accuracy of 67.00%for“happy”,54.90%for“an?gry”,and 51.69%for“sad”.
文摘Breast cancer is a common cause of death among women worldwide.Ultrasonic imaging is a valuable diagnostic tool in breast cancer detection.However,the accuracy of computer-aided diagnosis systems for breast cancer classification is limited due to the lack of well-annotated datasets.This study proposes a deep learning(DL)-based framework for breast mass classification using ultrasound images,which incorporates a novel data augmentation technique,generative adversarial network(GAN),and transfer learning(TL).Automating early tumor identification and classification in breast cancer diagnosis can save lives by improving the accuracy of diagnoses and reducing the need for invasive procedures.However,the limited availability of wellannotated datasets for ultrasound images of breast cancer has hampered the development of accurate computer-aided diagnosis systems.The accuracy of breast mass classification using ultrasound images is limited due to the lack of well-annotated datasets.Conventional data augmentation techniques have limitations in applications with strict guidelines,such as medical datasets.Therefore,there is a need to develop a novel data augmentation technique to improve the accuracy of breast mass classification using ultrasound images.The proposed framework can be extended to other medical imaging applications,where the availability of well-annotated datasets is limited.The GAN-based data augmentation technique and TL-based feature extraction can be used to improve the accuracy of classification models in other medical imaging applications.Additionally,the proposed framework can be used to develop accurate computer-aided diagnosis systems for breast cancer detection in clinical settings.The proposed framework incorporates a DL-based approach for breast mass classification using ultrasound images.The framework includes a GAN-based data augmentation technique and TL for feature extraction.The dataset used for training and testing the model is the breast ultrasound images(BUSI)dataset,which includes 1311 images with normal and abnormal breast masses.The proposed framework achieved an accuracy of 99.6%for breast mass classification using ultrasound images,which outperformed existing methods.The GAN-based data augmentation technique and TL-based feature extraction improved the accuracy of the classification model.The results suggest that DL algorithms can be effectively applied for breast ultrasound categorization.The proposed framework presents a novel approach for breast mass classification using ultrasound images,which incorporates a GAN-based data augmentation technique and TL-based feature extraction.The results demonstrate that the proposed framework outperforms existing methods and achieves high accuracy in breast mass classification using ultrasound images.This framework can be useful for developing accurate computer-aided diagnosis systems for breast cancer detection.
基金funding for the project,excluding research publication,from the Board of Research in Nuclear Sciences(BRNS)under Grant Number 59/14/05/2019/BRNS.
文摘Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0°to±90°.We initially selected the most suitable feature vector size by integrating the Dlib,FaceNet(Inception-v2),and“Support Vector Machines(SVM)”+“K-nearest neighbors(KNN)”algorithms.To train and evaluate this feature vector,we used two datasets:the“Labeled Faces in the Wild(LFW)”benchmark data and the“Robust Shape-Based FR System(RSBFRS)”real-time data,which contained face images with varying yaw poses.After selecting the best feature vector,we developed a real-time FR system to handle yaw poses.The proposed FaceNet architecture achieved recognition accuracies of 99.7%and 99.8%for the LFW and RSBFRS datasets,respectively,with 128 feature vector dimensions and minimum Euclidean distance thresholds of 0.06 and 0.12.The FaceNet+SVM and FaceNet+KNN classifiers achieved classification accuracies of 99.26%and 99.44%,respectively.The 128-dimensional embedding vector showed the highest recognition rate among all dimensions.These results demonstrate the effectiveness of our proposed approach in enhancing FR accuracy,particularly in real-world scenarios with varying yaw poses.
基金Key Program of Joint Funds of the National Natural Science Foundation of China,Grant/Award Number:U22B20118。
文摘Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavily on laboratory data,which differ signifi-cantly from that under the complex conditions of field data,leading to a marked drop in recognition accuracy when they are applied to field PD diagnosis.This study addresses the challenge by integrating field data into the training process,utilising a deep transfer learning approach that combines laboratory and field data to improve diagnostic accuracy for GIS PD.The research collected PD data from laboratory models representing five defect types and field data gathered from operational GIS equipment.A deep residual network(ResNet50)was pretrained using laboratory data and fine-tuned with field data through deep transfer learning to optimise the recognition of PD in field conditions.The results show that the proposed model achieves a significantly higher recognition accuracy(93.7%)for field data compared to traditional methods(60%-70%).The integration of deep transfer learning ensures that both low-dimensional general features from labora-tory data and high-dimensional specific features from field data are effectively utilised.This research significantly contributes to improving the diagnostic accuracy of PD in GIS under field conditions,providing a robust method for defect detection in operational equipment.
基金supported by the National Natural Science Foundation of China(No.12022214)the National Key R&D Program of China(No.2020YFC2201200)。
文摘This paper proposes an optimal,robust,and efficient guidance scheme for the perturbed minimum-time low-thrust transfer toward the geostationary orbit.The Earth’s oblateness perturbation and shadow are taken into account.It is difficult for a Lyapunov-based or trajectory-tracking guidance method to possess multiple characteristics at the same time,including high guidance optimality,robustness,and onboard computational efficiency.In this work,a concise relationship between the minimum-time transfer problem with orbital averaging and its optimal solution is identified,which reveals that the five averaged initial costates that dominate the optimal thrust direction can be approximately determined by only four initial modified equinoctial orbit elements after a coordinate transformation.Based on this relationship,the optimal averaged trajectories constituting the training dataset are randomly generated around a nominal averaged trajectory.Five polynomial regression models are trained on the training dataset and are regarded as the costate estimators.In the transfer,the spacecraft can obtain the real-time approximate optimal thrust direction by combining the costate estimations provided by the estimators with the current state at any time.Moreover,all these computations onboard are analytical.The simulation results show that the proposed guidance scheme possesses extremely high guidance optimality,robustness,and onboard computational efficiency.
文摘盐体是具有良好气密性的地质构造,有利于油气储存,实现精细化盐体的解释极为必要。然而,不同于断层,盐体的特征较为复杂且形态差异大,常规方法易导致混淆和误判。此外,基于数据驱动的盐体识别模型在实际数据集上的泛化能力较差,因此目前在地震勘探中进行盐体的解释及可视化仍存在挑战。文章将盐体解释视为地震图像的语义分割问题,提出了基于迁移学习的上下文融合与混合注意力的智能盐体分割(Multi-path structure Mixed Attention and Transfer Optimized Net,MMTONet)方法。同时设计了一种基于盐体上下文特征融合模块,进而建立了改进注意力卷积混合的跳跃连接机制,以更好地弥补由下采样造成的信息损失,从而提高模型对盐体边界与高振幅噪声的像素级辨别能力。在此基础上,还设计了迁移学习的适配器微调策略,提升了模型在实际数据上的泛化能力。在地震数据集上的实验结果表明,MMTONet在提高分割精度和减少计算量、参数量方面均优于主流的语义分割方法。
基金supported by the National Natural Science Foundation of China (Grant No.U1909217)the Zhejiang Natural Science Foundation of China (Grant No.LD21E050001)the Wenzhou Major Science and Technology Innovation Project of China (Grant No.ZG2020051)。
文摘Artificial intelligence(AI),which has recently gained popularity,is being extensively employed in modern fault diagnostic research to preserve the reliability and productivity of machines.The effectiveness of AI is influenced by the quality of the labeled training data.However,in engineering scenarios,available data on mechanical equipment are scarce,and collecting massive amounts of well-annotated fault data to train AI models is expensive and difficult.In response to the inadequacy of training samples,a numerical simulation-based partial transfer learning method for machinery fault diagnosis is proposed.First,a suitable simulation model of critical components in a mechanical system is developed using the finite element method(FEM),and numerical simulation is performed to acquire FEM simulation samples containing different fault types.Second,several synthetic simulation samples are generated to form complete source domain training samples using a generative adversarial network.Subsequently,the partial transfer learning network is trained to extract shared fault characteristics between the simulation and measured samples in the case of class imbalance.Finally,the resulting model is used to diagnose unknown samples from real-world mechanical systems in operation.The proposed method is tested on actual fault samples of bearings and gears obtained from a public dataset and experimental test rig available in our laboratory,achieving average classification accuracy of 99.54%and 99.64%,respectively.Comparison investigations reveal that the proposed method has superior classification and generalization ability when detecting faults in real mechanical systems.
基金supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)in part by the NRF grant funded by the Korean government(MSIT)(NRF-2022R1A2C1004401).
文摘Grains are the most important food consumed globally,yet their yield can be severely impacted by pest infestations.Addressing this issue,scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods.Traditional approaches often rely on preprocessed datasets,but there is a growing need for solutions that utilize real-time images of pests in their natural habitat.Our study introduces a novel twostep approach to tackle this challenge.Initially,raw images with complex backgrounds are captured.In the subsequent step,feature extraction is performed using both hand-crafted algorithms(Haralick,LBP,and Color Histogram)and modified deep-learning architectures.We propose two models for this purpose:PestNet-EF and PestNet-LF.PestNet-EF uses an early fusion technique to integrate handcrafted and deep learning features,followed by adaptive feature selection methods such as CFS and Recursive Feature Elimination(RFE).PestNet-LF utilizes a late fusion technique,incorporating three additional layers(fully connected,softmax,and classification)to enhance performance.These models were evaluated across 15 classes of pests,including five classes each for rice,corn,and wheat.The performance of our suggested algorithms was tested against the IP102 dataset.Simulation demonstrates that the Pestnet-EF model achieved an accuracy of 96%,and the PestNet-LF model with majority voting achieved the highest accuracy of 94%,while PestNet-LF with the average model attained an accuracy of 92%.Also,the proposed approach was compared with existing methods that rely on hand-crafted and transfer learning techniques,showcasing the effectiveness of our approach in real-time pest detection for improved agricultural yield.
