Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.Howeve...Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.However,deep learning entails extensive data for training,and it may be challenging to collect plant datasets.Even though plant datasets can be collected,they may be uneven in quantity.As a result,the problem of classification model overfitting arises.This study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available data.First,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image quality.Then,a gradient penalty mechanism was added to the loss function to improve the training stability of the model.Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset.On AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the experiment.Moreover,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original dataset.Furthermore,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this approach.The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.展开更多
In terms of the requirement of automatically sorting pearls, the pearl contour feature extraction and shape recognition algorithm are studied in this paper to reckon with the rapid identification of pearls shape onlin...In terms of the requirement of automatically sorting pearls, the pearl contour feature extraction and shape recognition algorithm are studied in this paper to reckon with the rapid identification of pearls shape online,and a monocular dynamic machine vision-based pearl shape detection device is designed. Through blowing, the pearl is suspended in a funnel shaped container and flipped rapidly in the device. The entire surface image of the pearl to be measured can be promptly grasped by the camera placed right above the funnel. The results of illumination experiments conducted from different angles indicate that the image contour acquired by the medium angle illumination is better extracted. The pearl shape test indicates that the method is incorporated with the inflatable suspension device to classify the pearls into seven types according to the national standard,and additionally the average error rate is confined under 5.38%. The shape characteristic of the pearl can be detected promptly and reliably, and accordingly the high-speed automatic sorting can be satisfied.展开更多
文摘Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.However,deep learning entails extensive data for training,and it may be challenging to collect plant datasets.Even though plant datasets can be collected,they may be uneven in quantity.As a result,the problem of classification model overfitting arises.This study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available data.First,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image quality.Then,a gradient penalty mechanism was added to the loss function to improve the training stability of the model.Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset.On AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the experiment.Moreover,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original dataset.Furthermore,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this approach.The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.
基金the Foundation of Zhejiang Key Level1 Discipline of Forestry Engineering within the Research Project(No.2014lygcz018)the Public Welfare Project of Zhejiang Science and Technology Department(No.2012C32021)+1 种基金the Preresearch Project of the Research Center for Smart Agriculture and Forestry,Zhejiang Agricultural and Forestry University(No.2013ZHNL02)the Scientific Research Foundation of Zhejiang Agricultural and Forestry University(No.2012FR070)
文摘In terms of the requirement of automatically sorting pearls, the pearl contour feature extraction and shape recognition algorithm are studied in this paper to reckon with the rapid identification of pearls shape online,and a monocular dynamic machine vision-based pearl shape detection device is designed. Through blowing, the pearl is suspended in a funnel shaped container and flipped rapidly in the device. The entire surface image of the pearl to be measured can be promptly grasped by the camera placed right above the funnel. The results of illumination experiments conducted from different angles indicate that the image contour acquired by the medium angle illumination is better extracted. The pearl shape test indicates that the method is incorporated with the inflatable suspension device to classify the pearls into seven types according to the national standard,and additionally the average error rate is confined under 5.38%. The shape characteristic of the pearl can be detected promptly and reliably, and accordingly the high-speed automatic sorting can be satisfied.