Mobile QR Code QR CODE

2025

Reject Ratio

81.5%

References

1 
Trang K. , TonThat L. , Thao N. G. , Thi N. T. , 2019, Mango diseases identification by a deep residual network with contrast enhancement and transfer learning, Proc. of IEEE Conference on Sustainable Utilization and Development in Engineering and Technologies, pp. 138-142DOI
2 
Pham T. N. , Tran L. V. , Dao S. V. , 2020, Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection, IEEE Access, Vol. 8, pp. 189960-189973DOI
3 
Uguz S. , Uysal N. , 2021, Classification of olive leaf diseases using deep convolutional neural networks, Neural Computing and Applications, Vol. 33, No. 9, pp. 4133-4149DOI
4 
Yadav S. , Sengar N. , Singh A. , Singh A. , Dutta M. K. , 2021, Identification of disease using deep learning and evaluation of bacteriosis in peach leaf, Ecological Informatics, Vol. 61, pp. 101247DOI
5 
Rao U. S. , Swathi R. , Sanjana V. , Arpitha L. , Chandrasekhar K. , Naik P. K. , 2021, Deep learning precision farming: grapes and mango leaf disease detection by transfer learning, Global Transitions Proceedings, Vol. 2, No. 2, pp. 535-544DOI
6 
Gulavnai S. , Patil R. , 2019, Deep learning for image-based mango leaf disease detection, International Journal of Recent Technology and Engineering, Vol. 8, No. 3S3, pp. 54-56DOI
7 
Turkoglu M. , Hanbay D. , 2019, Plant disease and pest detection using deep learning-based features, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 27, pp. 1636-1651DOI
8 
Bagga M. , Goyal S. , 2024, Image-based detection and classification of plant diseases using deep learning: State-of-the-art review, Urban Agriculture & Regional Food Systems, Vol. 9, No. 1, pp. e20053DOI
9 
Rajbongshi A. , Khan T. , Pramanik M. M. , Tanvir S. M. , Siddiquee N. R. , 2021, Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach, Indonesian Journal of Electrical Engineering and Computer Science, Vol. 23, No. 3, pp. 1681-1688DOI
10 
Agarap A. F. , 2018, Deep learning using rectified linear units (ReLU), arXiv preprint arXiv:1803.08375DOI
11 
Balderas L. , Lastra M. , Benítez J. M. , 2023, Optimizing convolutional neural network architecture, arXiv preprint arXiv:2401.01361DOI
12 
Sinha D. , El-Sharkawy M. , 2019, Thin MobileNet: An enhanced MobileNet architecture, Proc. of IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, pp. 280-285DOI
13 
Jaiswal A. , Gianchandani N. , Singh D. , Kumar V. , Kaur M. , 2021, Classification of the COVID-19 infected patients using DenseNet201 based on deep transfer learning, Journal of Biomolecular Structure and Dynamics, Vol. 39, No. 15, pp. 5682-5689DOI
14 
Szegedy C. , Vanhoucke V. , Ioffe S. , Shlens J. , Wojna Z. , 2016, Rethinking the inception architecture for computer vision, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826DOI
15 
He K. , Zhang X. , Ren S. , Sun J. , 2016, Deep residual learning for image recognition, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778DOI
16 
Narayanan K. L. , Naresh R. , 2024, Detection and prevention of black hole attack using tree hierarchical deep convolutional neural network and enhanced identity-based encryption in vehicular ad hoc network, IEIE Transactions on Smart Processing & Computing, Vol. 13, No. 1, pp. 41-50DOI
17 
Kim M. , 2023, Light-weight deep neural network for small vehicle detection using model-scale YOLOv4, IEIE Transactions on Smart Processing & Computing, Vol. 12, No. 5, pp. 369-378DOI
18 
Wang S. , Cao H. , Liu Y. , 2023, Application of SIFT algorithm based on the Gabor features in multi-source information image monitoring, IEIE Transactions on Smart Processing & Computing, Vol. 12, No. 2, pp. 112-121DOI
19 
Allmamun M. , 2024, Drone detection and tracking using deep convolutional neural networks from real-time CCTV footage, IEIE Transactions on Smart Processing & Computing, Vol. 13, No. 4, pp. 313-321DOI
20 
Arivazhagan S. , Ligi S. V. , 2018, Mango leaf diseases identification using convolutional neural network, International Journal of Pure and Applied Mathematics, Vol. 120, No. 6, pp. 11067-11079Google Search
21 
Ksibi A. , Ayadi M. , O. S. B. , Jamjoom M. M. , Ullah Z. , 2022, MobiRes-net: a hybrid deep learning model for detecting and classifying olive leaf diseases, Applied Sciences, Vol. 12, No. 20, pp. 10278DOI
22 
Jung J.-Y. , Lee S.-H. , Kim J.-O. , 2023, Knowledge transfer based spatial embedding network for plant leaf instance segmentation, IEIE Transactions on Smart Processing & Computing, Vol. 12, No. 2, pp. 162-170DOI
23 
Lodhi B. A. , Ullah R. , Imran S. , Imran M. , 2024, SenseNet: Densely connected, fully convolutional network with bottleneck skip connection for image segmentation, IEIE Transactions on Smart Processing & Computing, Vol. 13, No. 4, pp. 328-336DOI
24 
Sankupellay M. , Konovalov D. , 2018, Bird call recognition using deep convolutional neural network, ResNet-50, Proc. of the Australian Acoustical Society Conference, pp. 1-8DOI
25 
Haque I. , Alim M. , Alam M. , Nawshin S. , Noori S. R. H. , Habib M. T. , 2022, Analysis of recognition performance of plant leaf diseases based on machine vision techniques, Journal of Human, Earth, and Future, Vol. 3, No. 1, pp. 129-137DOI