Home > Published Issues > 2022 > Volume 13, No. 2, April 2022 >
JAIT 2022 Vol.13(2): 162-166
doi: 10.12720/jait.13.2.162-166

Detection and Identification with Analysis of Carica papaya Leaf Using Android

John A. Bacus 1,2 and Noel B. Linsangan 1
1. School of Electrical, Electronics and Computer Engineering, Mapua University, Manila, Philippines
2. College of Engineering Education, Computer Engineering Program, University of Mindanao, Davao City, Philippines
 

Abstract—With the increase in the usage of mobile devices such as smartphones, laptops, smartwatches, etc., access to information and communication has been effortless and convenient. Thus, making Raspberry Pi, an Android device, has been made. LineageOS is used specifically as an operating system that Konstakang developed. With CNN's MobileNet architecture and transfer learning, the classification for papaya leaf disease was a success. MobileNet Architecture was retrained using the images of the following papaya leaves such as Blackspot, Brownspot, Mealybug Infection, Powdery Mildew, Healthy, and Unknown images and employed transfer learning to create the model successfully. A total of seventy-two (72) samples were tested. The study made use of confusion matrix to compute for the accuracy of the system and got 91.667% accuracy.
 
Index Terms—convolutional neural networks, transfer learning, MobileNet, plant disease identification, TensorFlow, lineage OS

Cite: John A. Bacus and Noel B. Linsangan, "Detection and Identification with Analysis of Carica papaya Leaf Using Android," Journal of Advances in Information Technology, Vol. 13, No. 2, pp. 162-166, April 2022.
 
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

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