TY - JOUR
T1 - Few shot learning for avocado maturity determination from microwave images
AU - Ahmed, Muhammad
AU - Mustafa, Hamza
AU - Wu, Muzhi
AU - Babaei, Mahdi
AU - Kong, Lingyan
AU - Jeong, Nathan
AU - Gan, Yu
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/3
Y1 - 2024/3
N2 - Artificial intelligence (AI) has played a critical role in the enhancement and automation of global food production and delivery. The assessment of ripeness through microwave images and AI presents a unique opportunity to enhance the current process and minimize food wastage. Particularly in avocado maturity prediction, microwave imaging provides a non-invasive solution to see through the shell and predict the maturity intelligently. However, conventional deep learning models are generally data-hungry, requiring huge amounts of training data to maintain high performance. In this paper, we propose a few-shot learning (FSL) model to address the need for a large training dataset in maturity estimation. We classify avocado microwave images for maturity assessment through a prototypical FSL model. The FSL model achieves an accuracy range of 80%–96 % across the different experimental groups and all outperforms the conventional deep learning in scenarios where limited data is available for training. This experiment demonstrates the feasibility and accuracy of utilizing microwave scanning and FSL to determine avocado maturity.
AB - Artificial intelligence (AI) has played a critical role in the enhancement and automation of global food production and delivery. The assessment of ripeness through microwave images and AI presents a unique opportunity to enhance the current process and minimize food wastage. Particularly in avocado maturity prediction, microwave imaging provides a non-invasive solution to see through the shell and predict the maturity intelligently. However, conventional deep learning models are generally data-hungry, requiring huge amounts of training data to maintain high performance. In this paper, we propose a few-shot learning (FSL) model to address the need for a large training dataset in maturity estimation. We classify avocado microwave images for maturity assessment through a prototypical FSL model. The FSL model achieves an accuracy range of 80%–96 % across the different experimental groups and all outperforms the conventional deep learning in scenarios where limited data is available for training. This experiment demonstrates the feasibility and accuracy of utilizing microwave scanning and FSL to determine avocado maturity.
KW - Artificial intelligence
KW - Avocado
KW - Computer vision
KW - Few-shot learning
KW - Fruit maturity assessment
KW - Microwave imaging
UR - http://www.scopus.com/inward/record.url?scp=85183540122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85183540122&partnerID=8YFLogxK
U2 - 10.1016/j.jafr.2024.100977
DO - 10.1016/j.jafr.2024.100977
M3 - Article
AN - SCOPUS:85183540122
VL - 15
JO - Journal of Agriculture and Food Research
JF - Journal of Agriculture and Food Research
M1 - 100977
ER -