smAvo: Packhouse optimization using smart avocadoes in South Africa

Broekman, André; Steyn, Wynand Jacobus Van der Merwe

doi:10.1016/j.compag.2021.106507

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…Innovative synthetic sensors, designed to emulate the fruit's morphology, have been developed, and are capable of documenting both environmental and physical stresses that avocados endure during post-harvest handling. It is envisaged that as these undergo the same treatment as the fruits themselves, they can provide real-time insights into their ripening state [24]. Research into hyperspectral imaging and other technologies as non-invasive means to determine flesh firmness and dry matter content has been conducted, providing potential insights into the avocado's internal state.…”

Section: Introductionmentioning

confidence: 99%

Shelf-Life Management and Ripening Assessment of ‘Hass’ Avocado (Persea americana) Using Deep Learning Approaches

Xavier,

Rodrigues,

Silva

2024

Foods

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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.

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Section: Introductionmentioning

confidence: 99%