TY - GEN
T1 - Identification and classification of Green Leafy Vegetables using CNN models
AU - Vilanculos, Eneia Filipe
AU - Shongwe, Thokozani
AU - Hasan, Ali N.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Identifying and classifying vegetables in big farms is a challenge, especially when the vegetables are similar in colour and shape. Manual identification of vegetables takes time and is prone to errors. Therefore, the automatic classification process of the precision farming, increasingly using image processing and pattern recognition to identify fruits and vegetable, is becoming essential to identify and classify vegetables in big farms. In this paper, an automatic system for the identification and classification of green leafy vegetables, similar in colour and shape was evaluataed using five different deep learning models such as CNN, MobileNet, VGG-16, Inception V3 and ResNet 50. The accuracies of these models achieved in this paper vary from 67% to 99%. The model with the highest accuracy is the MobileNet.
AB - Identifying and classifying vegetables in big farms is a challenge, especially when the vegetables are similar in colour and shape. Manual identification of vegetables takes time and is prone to errors. Therefore, the automatic classification process of the precision farming, increasingly using image processing and pattern recognition to identify fruits and vegetable, is becoming essential to identify and classify vegetables in big farms. In this paper, an automatic system for the identification and classification of green leafy vegetables, similar in colour and shape was evaluataed using five different deep learning models such as CNN, MobileNet, VGG-16, Inception V3 and ResNet 50. The accuracies of these models achieved in this paper vary from 67% to 99%. The model with the highest accuracy is the MobileNet.
KW - Agriculture
KW - Deep Learning
KW - MobileNet
KW - VGG-16
KW - Vegetables Classification
UR - http://www.scopus.com/inward/record.url?scp=85172009487&partnerID=8YFLogxK
U2 - 10.1109/icABCD59051.2023.10220482
DO - 10.1109/icABCD59051.2023.10220482
M3 - Conference contribution
AN - SCOPUS:85172009487
T3 - 6th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2023 - Proceedings
BT - 6th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2023 - Proceedings
A2 - Pudaruth, Sameerchand
A2 - Singh, Upasana
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2023
Y2 - 3 August 2023 through 4 August 2023
ER -
