Denoising Convolutional Variational Autoencoders-Based Feature Learning for Automatic Detection of Plant Diseases

Early detection is critical for maintaining quantity and quality of farming commodity. Currently, detection of plant diseases still requires human expertise and/or need microscopic identification such as spectroscopic technique and molecular biological. So, it would be very costly and time consuming...

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Veröffentlicht in:	2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) S. 1 - 6
Hauptverfasser:	Zilvan, Vicky, Ramdan, Ade, Suryawati, Endang, Kusumo, R. Budiarianto S., Krisnandi, Dikdik, Pardede, Hilman F.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2019
Schlagworte:	autoen-coders Autoencoders Computer architecture Data mining deep learning Denoising convolutional variational autoencoders Feature extraction feature learning Microscopy Noise Noise reduction Plant diseases plant diseases detection Representation learning Training
Online-Zugang:	Volltext
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