The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a c...The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.展开更多
In agricultural farms in Indiawhere the staple diet formost of the households is potato,plant leaf diseases,namely Potato Early Blight(PEB)and Potato Late Blight(PLB),are quite common.The class label Plant Healthy(PH)...In agricultural farms in Indiawhere the staple diet formost of the households is potato,plant leaf diseases,namely Potato Early Blight(PEB)and Potato Late Blight(PLB),are quite common.The class label Plant Healthy(PH)is also used.If these diseases are not identified early,they can causemassive crop loss and thereby incur huge economic losses to the farmers in the agricultural domain and can impact the gross domestic product of the nation.This paper presents a hybrid approach for potato plant disease severity estimation and classification of diseased and healthy leaves,combining the strengths of classical image processing,computer vision,and deep learning.We propose a pipeline that initially employs OpenCV’s cv2 led color-based image segmentation to isolate and highlight diseased brown,yellowcolored lesions or regions and healthy green colored lesion areas associated with various potato leaf diseases.Adaptive Thresholding for illumination and texture feature extraction and U-Net Segmentation for mask refinement for severity estimation.It has a mathematical framework for quantifying the severity based on the spatial area distribution of these regions.This allows for both visual representation of the segmented regions in the form of overlay masks and quantification of distinct disease severity.We detail the implementation of the approach,including color space selection,segmentation strategies,mask creation,area calculation,and a potential mathematical model for severity calculation.Overlay masks generated are then used as input to a CBAM-EfficientNetB0 model,leveraging transfer learning for improved classification accuracy and efficiency.For the Plant Village dataset,the test accuracy achieved is 0.99,whereas the test loss is 0.02,respectively.For the Plant Doc dataset,the test accuracy achieved is 0.97,whereas the test loss is 0.06,respectively.Also,the CBAM attention mechanism model lays emphasis on relevant features within the lesions and overall image context.The results achieved with the Plant Village dataset are slightly better in comparison to the Plant Doc dataset.展开更多
The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection methods.While recent st...The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection methods.While recent studies have made progress,a common challenge is the low accuracy of existing detection models.These models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource use.The proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and EfficientNetB0.By leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial settings.This advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the field.The results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these challenges.Both CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.展开更多
As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy supply.Plant pathogens will affect its leaves at a certain time during crop cultivat...As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy supply.Plant pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling price.In the agriculture industry,the identification of fungal diseases plays a vital role.However,it requires immense labor,greater planning time,and extensive knowledge of plant pathogens.Computerized approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several times.Therefore,the proposed study presents a new framework for the recognition of fruits and vegetable diseases.This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)model.The localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model.The localized images passed as input to classify the different types of plant diseases.The classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 fully-connected.The proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.展开更多
基金support of the National Key R&D Program of China(No.2022YFC2803903)the Key R&D Program of Zhejiang Province(No.2021C03013)the Zhejiang Provincial Natural Science Foundation of China(No.LZ20F020003).
文摘The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.
基金done under Department of Biotechnology(DBT)project titled“Application of Machine Learning for Hyperspectral Imaging and Remote Sensing aimed at Early Detection of Fungal Foliar Diseases and Bacterial Wilt Diseases in Potato Crop”,DBT/Reference.No.BT/PR45388/133/58/2022.
文摘In agricultural farms in Indiawhere the staple diet formost of the households is potato,plant leaf diseases,namely Potato Early Blight(PEB)and Potato Late Blight(PLB),are quite common.The class label Plant Healthy(PH)is also used.If these diseases are not identified early,they can causemassive crop loss and thereby incur huge economic losses to the farmers in the agricultural domain and can impact the gross domestic product of the nation.This paper presents a hybrid approach for potato plant disease severity estimation and classification of diseased and healthy leaves,combining the strengths of classical image processing,computer vision,and deep learning.We propose a pipeline that initially employs OpenCV’s cv2 led color-based image segmentation to isolate and highlight diseased brown,yellowcolored lesions or regions and healthy green colored lesion areas associated with various potato leaf diseases.Adaptive Thresholding for illumination and texture feature extraction and U-Net Segmentation for mask refinement for severity estimation.It has a mathematical framework for quantifying the severity based on the spatial area distribution of these regions.This allows for both visual representation of the segmented regions in the form of overlay masks and quantification of distinct disease severity.We detail the implementation of the approach,including color space selection,segmentation strategies,mask creation,area calculation,and a potential mathematical model for severity calculation.Overlay masks generated are then used as input to a CBAM-EfficientNetB0 model,leveraging transfer learning for improved classification accuracy and efficiency.For the Plant Village dataset,the test accuracy achieved is 0.99,whereas the test loss is 0.02,respectively.For the Plant Doc dataset,the test accuracy achieved is 0.97,whereas the test loss is 0.06,respectively.Also,the CBAM attention mechanism model lays emphasis on relevant features within the lesions and overall image context.The results achieved with the Plant Village dataset are slightly better in comparison to the Plant Doc dataset.
文摘The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection methods.While recent studies have made progress,a common challenge is the low accuracy of existing detection models.These models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource use.The proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and EfficientNetB0.By leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial settings.This advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the field.The results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these challenges.Both CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.
基金This work was supported by the Soonchunhyang University Research Fund.
文摘As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy supply.Plant pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling price.In the agriculture industry,the identification of fungal diseases plays a vital role.However,it requires immense labor,greater planning time,and extensive knowledge of plant pathogens.Computerized approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several times.Therefore,the proposed study presents a new framework for the recognition of fruits and vegetable diseases.This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)model.The localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model.The localized images passed as input to classify the different types of plant diseases.The classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 fully-connected.The proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.
