Seeds of chickpea cv.Ghab3 were treated with three fungicides;Oxycure,Topsin and Vitaflo,and infected by Fusarium Oxysporum f.sp.Ciceri at culturing under a glasshouse condition.The results showed that Oxycure(Oxychlo...Seeds of chickpea cv.Ghab3 were treated with three fungicides;Oxycure,Topsin and Vitaflo,and infected by Fusarium Oxysporum f.sp.Ciceri at culturing under a glasshouse condition.The results showed that Oxycure(Oxychloride)caused a significant increase in seed germination compared with other treatments,but it could not provide this protection at the post-germination phase,resulting in a significant increase in seedling mortality to 42.1%at 15 days after germination,compared with the other fungicides.In addition,Topspin(Thiophanate-methyl)was an effective protectant at germination stage,and in the seedling one too,since the percent of seedling mortality did not exceed 11.11%,whereas,Vitaflo had the least effect in protecting seeds from fungal infection,but with a significant difference with the positive control,as the percentage of non-germinated seeds was 41.66%and 62.5%,respectively.展开更多
COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread.This spread of COVID-19 requires a fast technique for diagnosis to make the ap...COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread.This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be classified.Convolutional Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy.Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results.In this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and pneumonia.In this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia infection.In this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the dataset.The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the datasets.It achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.展开更多
文摘Seeds of chickpea cv.Ghab3 were treated with three fungicides;Oxycure,Topsin and Vitaflo,and infected by Fusarium Oxysporum f.sp.Ciceri at culturing under a glasshouse condition.The results showed that Oxycure(Oxychloride)caused a significant increase in seed germination compared with other treatments,but it could not provide this protection at the post-germination phase,resulting in a significant increase in seedling mortality to 42.1%at 15 days after germination,compared with the other fungicides.In addition,Topspin(Thiophanate-methyl)was an effective protectant at germination stage,and in the seedling one too,since the percent of seedling mortality did not exceed 11.11%,whereas,Vitaflo had the least effect in protecting seeds from fungal infection,but with a significant difference with the positive control,as the percentage of non-germinated seeds was 41.66%and 62.5%,respectively.
文摘COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread.This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be classified.Convolutional Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy.Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results.In this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and pneumonia.In this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia infection.In this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the dataset.The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the datasets.It achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.
