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All Journal International Journal of Engineering, Technology and Natural Sciences (IJETS)
Ruri Suko Basuki
Dian Nuswantoro University, Semarang, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Deep Convolution Neural Network to solve Problems for Appel Leaf Disease Detection Sutriawan; Ahmad Zaniul Fanani; Farrikh Alzami; Ruri Suko Basuki
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 5 No 2 (2023): International Journal of Engineering, Technology and Natural Sciences
Publisher : University of Technology Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v5i2.232

Abstract

Orchardists are now concerned about apple leaf infections since they could result in crop failure. It is challenging for growers to identify the type of illness on apple leaves due to the large variety of diseases that can affect apple leaves. In this study, we cover four different illness categories: healthy, numerous diseased, rusty, and scabby. a deep convolutional neural network method of processing. using a number of suggested methods, including data preprocessing and the pre-configured VGG-16 deep convolutional neural network (CNN) architecture. The Adam optimization model's beta 2 = 0.999 parameter value at Ephoch to 85/100 with an accuracy of 0.7582 and epsilon = 1e-07 parameter value at Ephoch to 32/100 with an accuracy of 0.7582 both produced the best accuracy outcomes.
Co-Authors Ahmad Zaniul Fanani Farrikh Alzami Sutriawan Sutriawan