A round 2010,academic circles witnessed a surge in Al research fueled by break-throughs such as the ImageNet project,a publicly available large-scale image database.The field reached a tipping point in 2016 when Googl...A round 2010,academic circles witnessed a surge in Al research fueled by break-throughs such as the ImageNet project,a publicly available large-scale image database.The field reached a tipping point in 2016 when Google’s AlphaGo defeated Go world champion Lee Se-dol and gained widespread public attention with the release of OpenAI's ChatGPT in November 2022.Just one year after ChatGPT’s debut,Chinese Al firm DeepSeek launched its open-source general large model,a milestone in the evolution of Al technology.展开更多
Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method f...Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method for vegetable images classification based on deep learning framework.The AlexNet network model in Caffe was used to train the vegetable image data set.The vegetable image data set was obtained from ImageNet and divided into training data set and test data set.The output function of the AlexNet network adopted the Rectified Linear Units(ReLU)instead of the traditional sigmoid function and the tanh function,which can speed up the training of the deep learning network.The dropout technology was used to improve the generalization of the model.The image data extension method was used to reduce overfitting in the learning process.With AlexNet network model used for training different number of vegetable image data set,the experimental results showed that the classification accuracy decreases as the number of data set decreases.The experimental verification indicated that the accuracy rate of the deep learning method in the test data set reached as high as 92.1%,which was greatly improved compared with BP neural network(78%)and SVM classifier(80.5%)methods.展开更多
The agriculture sector is no exception to the widespread usage of deep learning tools and techniques.In this paper,an automated detection method on the basis of pre-trained Convolutional Neural Network(CNN)models is p...The agriculture sector is no exception to the widespread usage of deep learning tools and techniques.In this paper,an automated detection method on the basis of pre-trained Convolutional Neural Network(CNN)models is proposed to identify and classify paddy crop biotic stresses from the field images.The proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely,Inception-V3,VGG-16,ResNet-50,DenseNet-121 and MobileNet-28.Brown spot,hispa,and leaf blast,three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the experimentation.The experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61%outperforming the other considered CNN models.The study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts.展开更多
文摘A round 2010,academic circles witnessed a surge in Al research fueled by break-throughs such as the ImageNet project,a publicly available large-scale image database.The field reached a tipping point in 2016 when Google’s AlphaGo defeated Go world champion Lee Se-dol and gained widespread public attention with the release of OpenAI's ChatGPT in November 2022.Just one year after ChatGPT’s debut,Chinese Al firm DeepSeek launched its open-source general large model,a milestone in the evolution of Al technology.
基金This research was financially supported by the International Science&Technology Cooperation Program of China(2015DFA00530)Key Research and Development Plan Project of Shandong Province(2016CYJS03A02).
文摘Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method for vegetable images classification based on deep learning framework.The AlexNet network model in Caffe was used to train the vegetable image data set.The vegetable image data set was obtained from ImageNet and divided into training data set and test data set.The output function of the AlexNet network adopted the Rectified Linear Units(ReLU)instead of the traditional sigmoid function and the tanh function,which can speed up the training of the deep learning network.The dropout technology was used to improve the generalization of the model.The image data extension method was used to reduce overfitting in the learning process.With AlexNet network model used for training different number of vegetable image data set,the experimental results showed that the classification accuracy decreases as the number of data set decreases.The experimental verification indicated that the accuracy rate of the deep learning method in the test data set reached as high as 92.1%,which was greatly improved compared with BP neural network(78%)and SVM classifier(80.5%)methods.
文摘The agriculture sector is no exception to the widespread usage of deep learning tools and techniques.In this paper,an automated detection method on the basis of pre-trained Convolutional Neural Network(CNN)models is proposed to identify and classify paddy crop biotic stresses from the field images.The proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely,Inception-V3,VGG-16,ResNet-50,DenseNet-121 and MobileNet-28.Brown spot,hispa,and leaf blast,three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the experimentation.The experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61%outperforming the other considered CNN models.The study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts.
