TY - GEN
T1 - Classification of pomelo leaf diseases using convolution neural network
AU - Laosim, Sirirat
AU - Samanchuen, Taweesak
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/19
Y1 - 2021/5/19
N2 - Pomelo is an important export fruit of Thailand. However, it is a plant that is susceptible to many diseases. The objective of this work is to identify diseases using the convolution neural network, which most of the diseases of Pomelo indicate on its leaves. Three types of pomelo leaves images including healthy leaves, greening disease, and citrus leafminer are addressed in this work. Transfer Learning techniques based on GoogLeNet, AlexNet, and Squeeznet are utilized for building the proper machine learning model for classifying Pomelo diseases. Image process techniques are also applied to enhance the performance of the model such as edging, grayscale, and rotation. Experimental results show that all three models give a similar performance where GoogLeNet has a bit better performance than that of AlexNet and Squeeznet.
AB - Pomelo is an important export fruit of Thailand. However, it is a plant that is susceptible to many diseases. The objective of this work is to identify diseases using the convolution neural network, which most of the diseases of Pomelo indicate on its leaves. Three types of pomelo leaves images including healthy leaves, greening disease, and citrus leafminer are addressed in this work. Transfer Learning techniques based on GoogLeNet, AlexNet, and Squeeznet are utilized for building the proper machine learning model for classifying Pomelo diseases. Image process techniques are also applied to enhance the performance of the model such as edging, grayscale, and rotation. Experimental results show that all three models give a similar performance where GoogLeNet has a bit better performance than that of AlexNet and Squeeznet.
KW - Convolution Neural Network
KW - Deep Learning
KW - Pomelo disease
KW - Transfer Learning
UR - https://www.scopus.com/pages/publications/85112846714
U2 - 10.1109/ECTI-CON51831.2021.9454782
DO - 10.1109/ECTI-CON51831.2021.9454782
M3 - Conference contribution
AN - SCOPUS:85112846714
T3 - ECTI-CON 2021 - 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology: Smart Electrical System and Technology, Proceedings
SP - 577
EP - 580
BT - ECTI-CON 2021 - 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology
A2 - Kumsuwan, Yuttana
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021
Y2 - 19 May 2021 through 22 May 2021
ER -