DOI: https://doi.org/10.14710/jtsiskom.2022.14250

Automatic detection of crop diseases using gamma transformation for feature learning with a deep convolutional autoencoder

1Research Center for Data and Information Sciences, National Research and Innovation Agency (BRIN), Indonesia

2Research Center for Artificial Intelligence and Cyber Security, National Research and Innovation Agency (BRIN), Indonesia

Received: 23 Jun 2021; Published: 24 Sep 2024.
Open Access Copyright (c) 2024 Vicky Zilvan, Ade Ramdan, Ahmad Afif Supianto, Ana Heryana, Andria Arisal, Asri Rizki Yuliani, Dikdik Krisnandi, Endang Suryawati, Raden Budiarianto Suryo Kusumo, Raden Sandra Yuawana, Hilman F. Pardede
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Precision agriculture is a management strategy for sustaining and increasing the production of agricultural commodities. One of its implementations is for crop disease detection. Currently, deep learning methods have become widespread methods for the automatic detection of crop diseases. Most deep learning methods showed better performance when using an original image in raw form as inputs. However, the original image of crop diseases may appear similar between one disease to another.  Therefore, the deep learning methods may misclassify the data. To deal with these, we propose the gamma transformation with a deep convolutional autoencoder to extract good features from the original image data. We use the output of the gamma transformation with a deep convolutional autoencoder as inputs to a classifier for the automatic detection of crop diseases. Our experiments show that the average accuracies of our method improve the performance of crop disease detection compared to only using raw data as inputs.
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Keywords: deep learning; gamma transformation; unsupervised feature learning; deep convolutional autoencoder; automatic detection of crop diseases
Funding: Insinas Grant 2021 from the Indonesian Ministry of Research, Technology, and Higher Education

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Section: Original Research Articles
Language : EN
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