In thefield of agriculture,the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants.This research focuses on identifying the plant diseas...In thefield of agriculture,the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants.This research focuses on identifying the plant diseases and detecting them promptly through the advancements in thefield of computer vision.The images obtained from in-field farms are typically with less visual information.However,there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images.We propose a novel Reconstructed Disease Aware–Convolutional Neural Network(RDA-CNN),inspired by recent CNN architectures,that integrates image super resolution and classification into a single model for rice plant disease classification.This network takes low-resolution images of rice crops as input and employs the super resolution layers to transform low-resolution images to super-resolution images to recover appearance such as spots,rot,and lesion on different parts of the rice plants.Extensive experimental results indicated that the proposed RDA-CNN method performs well under diverse aspects generating visually pleasing images and outperforms better than other con-ventional Super Resolution(SR)methods.Furthermore,these super-resolution images are subsequently passed through deep classification layers for disease classi-fication.The results demonstrate that the RDA-CNN significantly boosts the clas-sification performance by nearly 4–6%compared with the baseline architectures.展开更多
Amongst several biometric traits,Vein pattern biometric has drawn much attention among researchers and diverse users.It gains its importance due to its difficulty in reproduction and inherent security advantages.Many ...Amongst several biometric traits,Vein pattern biometric has drawn much attention among researchers and diverse users.It gains its importance due to its difficulty in reproduction and inherent security advantages.Many research papers have dealt with the topic of new generation biometric solutions such as iris and vein biometrics.However,most implementations have been based on small datasets due to the difficulties in obtaining samples.In this paper,a deeper study has been conducted on previously suggested methods based on Convolutional Neural Networks(CNN)using a larger dataset.Also,modifications are suggested for implementation using ensemble methods.Ensembles were used to reduce training time and cost by training multiple weak classifiers instead of a single,strong classifier.Classifiers used were CNN,Random Forest and Logistic Regression.An inexpensive and robust data acquisition system was also developed for obtaining the dataset.The obtained result shows an improved accuracy of 96.77%using ensemble method instead of dealing with a single classifier.展开更多
文摘In thefield of agriculture,the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants.This research focuses on identifying the plant diseases and detecting them promptly through the advancements in thefield of computer vision.The images obtained from in-field farms are typically with less visual information.However,there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images.We propose a novel Reconstructed Disease Aware–Convolutional Neural Network(RDA-CNN),inspired by recent CNN architectures,that integrates image super resolution and classification into a single model for rice plant disease classification.This network takes low-resolution images of rice crops as input and employs the super resolution layers to transform low-resolution images to super-resolution images to recover appearance such as spots,rot,and lesion on different parts of the rice plants.Extensive experimental results indicated that the proposed RDA-CNN method performs well under diverse aspects generating visually pleasing images and outperforms better than other con-ventional Super Resolution(SR)methods.Furthermore,these super-resolution images are subsequently passed through deep classification layers for disease classi-fication.The results demonstrate that the RDA-CNN significantly boosts the clas-sification performance by nearly 4–6%compared with the baseline architectures.
文摘Amongst several biometric traits,Vein pattern biometric has drawn much attention among researchers and diverse users.It gains its importance due to its difficulty in reproduction and inherent security advantages.Many research papers have dealt with the topic of new generation biometric solutions such as iris and vein biometrics.However,most implementations have been based on small datasets due to the difficulties in obtaining samples.In this paper,a deeper study has been conducted on previously suggested methods based on Convolutional Neural Networks(CNN)using a larger dataset.Also,modifications are suggested for implementation using ensemble methods.Ensembles were used to reduce training time and cost by training multiple weak classifiers instead of a single,strong classifier.Classifiers used were CNN,Random Forest and Logistic Regression.An inexpensive and robust data acquisition system was also developed for obtaining the dataset.The obtained result shows an improved accuracy of 96.77%using ensemble method instead of dealing with a single classifier.
