TY - JOUR
T1 - Shelf-life management and ripening assessment of ‘hass’ avocado (persea americana) using deep learning approaches
AU - Xavier, Pedro
AU - Rodrigues, Pedro Miguel
AU - Silva, Cristina L. M.
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/4/10
Y1 - 2024/4/10
N2 - Avocado production is mostly confined to tropical and subtropical regions, leading to lengthy distribution channels that, coupled with their unpredictable post-harvest behavior, render avocados susceptible to significant loss and waste. To enhance the monitoring of ‘Hass’ avocado ripening, a data-driven tool was developed using a deep learning approach. This study involved monitoring 478 avocados stored in three distinct storage environments, using a 5-stage Ripening Index to classify each fruit’s ripening phase based on their shared characteristics. These categories were paired with daily photographic records of the avocados, resulting in a database of labeled images. Two convolutional neural network models, AlexNet and ResNet-18, were trained using transfer learning techniques to identify distinct ripening indicators, enabling the prediction of ripening stages and shelf-life estimations for new unseen data. The approach achieved a final prediction accuracy of 88.8% for the ripening assessment, with 96.7% of predictions deviating by no more than half a stage from their actual classifications when considering the best side of the samples. The average shelf-life estimates based on the attributed classifications were within 0.92 days of the actual shelf-life, whereas the predictions made by the models had an average deviation of 0.96 days from the actual shelf-life.
AB - Avocado production is mostly confined to tropical and subtropical regions, leading to lengthy distribution channels that, coupled with their unpredictable post-harvest behavior, render avocados susceptible to significant loss and waste. To enhance the monitoring of ‘Hass’ avocado ripening, a data-driven tool was developed using a deep learning approach. This study involved monitoring 478 avocados stored in three distinct storage environments, using a 5-stage Ripening Index to classify each fruit’s ripening phase based on their shared characteristics. These categories were paired with daily photographic records of the avocados, resulting in a database of labeled images. Two convolutional neural network models, AlexNet and ResNet-18, were trained using transfer learning techniques to identify distinct ripening indicators, enabling the prediction of ripening stages and shelf-life estimations for new unseen data. The approach achieved a final prediction accuracy of 88.8% for the ripening assessment, with 96.7% of predictions deviating by no more than half a stage from their actual classifications when considering the best side of the samples. The average shelf-life estimates based on the attributed classifications were within 0.92 days of the actual shelf-life, whereas the predictions made by the models had an average deviation of 0.96 days from the actual shelf-life.
KW - Convolutional neural network
KW - Fruit ripening
KW - Shelf-life tracking
KW - Post-harvest handling
KW - Supply chain management
UR - http://www.scopus.com/inward/record.url?scp=85191354840&partnerID=8YFLogxK
U2 - 10.3390/foods13081150
DO - 10.3390/foods13081150
M3 - Article
C2 - 38672823
SN - 2304-8158
VL - 13
JO - Foods
JF - Foods
IS - 8
M1 - 1150
ER -