Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases

Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of...

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Vydáno v:2018 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) s. 158 - 162
Hlavní autoři: Pardede, Hilman F., Suryawati, Endang, Sustika, Rika, Zilvan, Vicky
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2018
Témata:
convolutional autoen-coder
Deep learning
Diseases
Feature extraction
feature learning
Kernel
Plant diseases detection
Support vector machines
SVM
Training
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Shrnutí:Developing an automatic detector of plant diseases is one of application fields in machine learning. Ground-truth diagnoses of plant diseases which are conducted by experts in laboratory tests are often inapplicable for fast and cheap implementations. Using machine learning approaches, the images of leaves or fruits are used as input data. From the data, we design discriminative features that are good for diseases classification. However, finding suitable features from the images are often challenging due to high intra-variability and inter-variability of the data. In this paper, we present an unsupervised feature learning algorithm using the convolutional autoencoder for detection of plant diseases. The use of convolutional autoencoder has two main advantages. First, the use of handcrafted features is not necessary as the network itself may learn to produce discriminative features. Secondly, the procedure is conducted in an unsupervised manner and hence, no labeling of the data are required. Here, we use the output of the autoencoder as inputs to SVM-based classifiers for automatic detection of plant diseases. The method indicates to be better than conventional autoencoder with more hidden layers.
DOI:10.1109/IC3INA.2018.8629518