Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agricult...Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agriculturalists spend a huge amount of money.However,the manual detection method of plant diseases is complicated and time-consuming.Consequently,automated systems for plant disease detection using machine learning(ML)approaches are proposed.However,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data.To address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural networks.In the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained architectures.It is capable to obtain the feature extraction of the given data with fine-tuning details.After convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural Network.The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step.The achieved average accuracy of our method is more than 96%and outperforms the recent techniques.展开更多
The effect ofelectromechanical fields, i.e., polarization fields, on the efficiency droop of GaN-based light-emitting diodes is presented using both experimental and numerical analyses. The role of incorporating such ...The effect ofelectromechanical fields, i.e., polarization fields, on the efficiency droop of GaN-based light-emitting diodes is presented using both experimental and numerical analyses. The role of incorporating such polarization charge density in device performance is numerically investigated and further compared with the experimental results of internal quantum efficiency of three different devices in consideration.展开更多
基金the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2016-0-00312)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)in part by the MSIP(Ministry of Science,ICT&Future Planning),Korea,under the National Program for Excellence in SW(2015-0-00938)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agriculturalists spend a huge amount of money.However,the manual detection method of plant diseases is complicated and time-consuming.Consequently,automated systems for plant disease detection using machine learning(ML)approaches are proposed.However,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data.To address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural networks.In the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained architectures.It is capable to obtain the feature extraction of the given data with fine-tuning details.After convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural Network.The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step.The achieved average accuracy of our method is more than 96%and outperforms the recent techniques.
文摘The effect ofelectromechanical fields, i.e., polarization fields, on the efficiency droop of GaN-based light-emitting diodes is presented using both experimental and numerical analyses. The role of incorporating such polarization charge density in device performance is numerically investigated and further compared with the experimental results of internal quantum efficiency of three different devices in consideration.
