TY - GEN
T1 - Mango Pests Identification Based-on Convolutional Neural Network
AU - Asy'ari, Misbachul Falach
AU - Azizah, Anisa Nur
AU - Hidayati, Shintami Chusnul
AU - Herumurti, Darlis
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Mango pests consist of various types and it requires classification for prevention and treatment after infection. But for large-scale agriculture or industry, it takes a long time and is more expensive to identify the type of mango pest. Fortunately, Computer Vision can automatically identify the type of mango pest by analyzing previously recognized mango pests. Therefore, this paper proposes various Convolutional Neural Network (CNN) models to identify 15 types of mango pests automatically. This paper also proposes data augmentation to overcome the low number of images in several classes (mango pests). CNN models such as AlexNet, GoogleNet, InceptionV3, ResNet18, ResNet50 can identify mango pests well. By combining the CNN model and data augmentation, this study achieves 99.72% highest accuracy, highest 99.67% sensitivity, and 99.98% highest specificity. These results are better than previous studies.
AB - Mango pests consist of various types and it requires classification for prevention and treatment after infection. But for large-scale agriculture or industry, it takes a long time and is more expensive to identify the type of mango pest. Fortunately, Computer Vision can automatically identify the type of mango pest by analyzing previously recognized mango pests. Therefore, this paper proposes various Convolutional Neural Network (CNN) models to identify 15 types of mango pests automatically. This paper also proposes data augmentation to overcome the low number of images in several classes (mango pests). CNN models such as AlexNet, GoogleNet, InceptionV3, ResNet18, ResNet50 can identify mango pests well. By combining the CNN model and data augmentation, this study achieves 99.72% highest accuracy, highest 99.67% sensitivity, and 99.98% highest specificity. These results are better than previous studies.
KW - Classification
KW - Convolutional Neural Network
KW - Data augmentation
KW - Mango pests
UR - http://www.scopus.com/inward/record.url?scp=85141569964&partnerID=8YFLogxK
U2 - 10.1109/ICoICT55009.2022.9914871
DO - 10.1109/ICoICT55009.2022.9914871
M3 - Conference contribution
AN - SCOPUS:85141569964
T3 - 2022 10th International Conference on Information and Communication Technology, ICoICT 2022
SP - 88
EP - 92
BT - 2022 10th International Conference on Information and Communication Technology, ICoICT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information and Communication Technology, ICoICT 2022
Y2 - 2 August 2022 through 3 August 2022
ER -