Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the appli...
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Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.
This paper presents a study which examines the pedagogical-interactional dimensions in computerscience education at universities of applied sciences, focusing on the expectations and perceptions of students and profe...
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Skin tones come in a diverse range of shades and are often necessary for various computer vision tasks. While skin detection is a well-studied focus, skin tone classification is not. Most works also use the Fitzpatric...
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Immeasurable efforts have been put into strengthening energy security and reducing greenhouse gas emissions by meeting growing energy demand. With declining costs and increasing performance, the deployment of PV syste...
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Digital image quality is essential for a variety of applications in visual information processing, including medical diagnostics, defence, and many other applications. However, there are several challenges that must b...
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Quasigroups have various applications in mathematics, computerscience, and cryptography. In coding theory and cryptography they have been used in error-correcting codes, error-detection codes, to construct key exchan...
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Using dataset analysis as a research method is becoming more popular among many researchers with diverse data collection and analysis backgrounds. This paper provides the first publicly available dataset consisting of...
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Random packed beds are often employed in chemical reactors as a means to increase the contact surface between reactants or a catalyst. The present work proposes a helical flow deflector placed within the bed and numer...
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We consider the domain adaptation problem in the context of label shift, where the label distributions between source and target domain differ, but the conditional distributions of features given the label are the sam...
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