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Yuawana, Raden Sandra
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Computer Science & IT Electrical & Electronics Engineering

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Automatic detection of crop diseases using gamma transformation for feature learning with a deep convolutional autoencoder Zilvan, Vicky; Ramdan, Ade; Supianto, Ahmad Afif; Heryana, Ana; Arisal, Andria; Yuliani, Asri Rizki; Krisnandi, Dikdik; Suryawati, Endang; Suryo Kusumo, Raden Budiarianto; Yuawana, Raden Sandra; Kadar, Jimmy Abdel; Pardede, Hilman F.
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14250

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.
Co-Authors Ade Ramdan Ahmad Afif Supianto Andria Arisal, Andria Heryana, Ana Kadar, Jimmy Abdel Krisnandi, Dikdik Pardede, Hilman F. Suryawati, Endang Suryo Kusumo, Raden Budiarianto Vicky Zilvan, Vicky Yuliani, Asri Rizki