Birds are a huge hazard to agriculture all around the world,causing harm to profitable field crops.Growers use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This...Birds are a huge hazard to agriculture all around the world,causing harm to profitable field crops.Growers use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This study presents a comprehensive overview of current bird repellant approaches used in agricultural contexts,as well as potential new ways. The bird repellent techniques include Internet of Things technology,Deep Learning,Convolutional Neural Network,Unmanned Aerial Vehicles,Wireless Sensor Networks and Laser biotechnology. This study’s goal is to find and review about previous approach towards repellent of birds in the crop fields using various technologies.展开更多
Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection appro...Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection approaches are often time-consuming,labor-intensive,and require expertise.Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference.Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques.Image processing techniques are used to extract features from diseased leaf images,such as the color,texture,vein patterns,and shape of lesions.Machine learning techniques are used to detect diseases based on the extracted features.In contrast,deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks.This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection,such as Transfer Learning,Ensemble Learning,and Hybrid approaches.This review also discusses the effectiveness of these approaches in addressing various challenges.This review discusses the details of various models and hyperparameter settings used,model fine-tuning techniques followed,and performance evaluation metrics utilized in various studies.This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.展开更多
Adversarial Malware Example(AME)-based adversarial training can effectively enhance the robustness of Machine Learning(ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement....Adversarial Malware Example(AME)-based adversarial training can effectively enhance the robustness of Machine Learning(ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement.Generative Adversarial Network(GAN)is a kind of AME generation method,but the existing GAN-based AME generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose a novel approach(denote as LSGAN-AT)to enhance ML-based malware detector robustness against Adversarial Examples,which includes LSGAN module and AT module.LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square(LS)loss to optimize boundary samples.AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector(RMD).Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack.The results also verify the performance of the generated RMD in the recognition rate of AME.展开更多
Adversarial Malware Example(AME)-based adversarial training can effectively enhance the robustness of Machine Learning(ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement....Adversarial Malware Example(AME)-based adversarial training can effectively enhance the robustness of Machine Learning(ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement.Generative Adversarial Network(GAN)is a kind of AME generation method,but the existing GAN-based AME generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose a novel approach(denote as LSGAN-AT)to enhance ML-based malware detector robustness against Adversarial Examples,which includes LSGAN module and AT module.LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square(LS)loss to optimize boundary samples.AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector(RMD).Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack.The results also verify the performance of the generated RMD in the recognition rate of AME.展开更多
文摘Birds are a huge hazard to agriculture all around the world,causing harm to profitable field crops.Growers use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This study presents a comprehensive overview of current bird repellant approaches used in agricultural contexts,as well as potential new ways. The bird repellent techniques include Internet of Things technology,Deep Learning,Convolutional Neural Network,Unmanned Aerial Vehicles,Wireless Sensor Networks and Laser biotechnology. This study’s goal is to find and review about previous approach towards repellent of birds in the crop fields using various technologies.
文摘Rice is an essential food crop that is cultivated in many countries.Rice leaf diseases can cause significant damage to crop cultivation,leading to reduced yields and economic losses.Traditional disease detection approaches are often time-consuming,labor-intensive,and require expertise.Automatic leaf disease detection approaches help farmers detect diseases without or with less human interference.Most of the earlier studies on rice leaf disease detection depended on image processing and machine learning techniques.Image processing techniques are used to extract features from diseased leaf images,such as the color,texture,vein patterns,and shape of lesions.Machine learning techniques are used to detect diseases based on the extracted features.In contrast,deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks.This systematic review explores various deep learning approaches used in the literature for rice leaf disease detection,such as Transfer Learning,Ensemble Learning,and Hybrid approaches.This review also discusses the effectiveness of these approaches in addressing various challenges.This review discusses the details of various models and hyperparameter settings used,model fine-tuning techniques followed,and performance evaluation metrics utilized in various studies.This review also discusses the limitations of existing studies and presents future directions for further developing more robust and efficient rice leaf disease detection techniques.
基金Chinese National Key Laboratory of Science and Technology on Information System Security and National Natural Science Foundation of China under Grant No.U1836105The research of R.J.Rodríguez and X.Chang has been supported in part by the University of Zaragoza and the Fundación Ibercaja under Grant JIUZ-2020-TIC-08The research of R.J.Rodríguez has also been supported in part by the University,Industry and Innovation Department of the Aragonese Government under Programa de Proyectos Estratégicos de Grupos de Investigación(DisCo research group,ref.T21-20R).
文摘Adversarial Malware Example(AME)-based adversarial training can effectively enhance the robustness of Machine Learning(ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement.Generative Adversarial Network(GAN)is a kind of AME generation method,but the existing GAN-based AME generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose a novel approach(denote as LSGAN-AT)to enhance ML-based malware detector robustness against Adversarial Examples,which includes LSGAN module and AT module.LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square(LS)loss to optimize boundary samples.AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector(RMD).Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack.The results also verify the performance of the generated RMD in the recognition rate of AME.
基金The research of J.Wang,X.Chang,Y.Wang and J.Zhang was supported in part by Project supported by Chinese National Key Laboratory of Science and Technology on Information System Security and National Natural Science Foundation of China under Grant No.U1836105The research of R.J.Rodriguez and X.Chang has been supported in part by the University of Zaragoza and the Fundacion Ibercaja under Grant JIUZ-2020-TIC-08The research of R.J.Rodriguez has also been supported in part by the University,Industry and Innovation Department of the Aragonese Government under Programa de Proyectos Estrategicos de Grupos de Investigacidn(DisCo research group,ref.T21-20R).
文摘Adversarial Malware Example(AME)-based adversarial training can effectively enhance the robustness of Machine Learning(ML)-based malware detectors against AME.AME quality is a key factor to the robustness enhancement.Generative Adversarial Network(GAN)is a kind of AME generation method,but the existing GAN-based AME generation methods have the issues of inadequate optimization,mode collapse and training instability.In this paper,we propose a novel approach(denote as LSGAN-AT)to enhance ML-based malware detector robustness against Adversarial Examples,which includes LSGAN module and AT module.LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square(LS)loss to optimize boundary samples.AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector(RMD).Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack.The results also verify the performance of the generated RMD in the recognition rate of AME.
