PDF(Pubmed)
Abstract：
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.
摘要：
鳄梨生产主要局限于热带和亚热带地区,导致冗长的分销渠道，再加上他们不可预测的收获后行为，使鳄梨容易遭受重大损失和浪费。为了加强对\'Hass\'鳄梨成熟的监测，使用深度学习方法开发了一种数据驱动工具。这项研究涉及监测储存在三个不同储存环境中的478个鳄梨，使用5阶段成熟指数根据每个水果的共同特征对它们的成熟期进行分类。这些类别与鳄梨的每日摄影记录配对，导致标记图像的数据库。两种卷积神经网络模型,AlexNet和ResNet-18使用迁移学习技术进行了训练，以识别不同的成熟指标，能够预测成熟阶段和新的未知数据的保质期估计。该方法对成熟评估的最终预测精度为88.8%，当考虑样本的最佳方面时，96.7%的预测偏离其实际分类不超过半个阶段。根据归属分类的平均保质期估计在实际保质期的0.92天内，而模型的预测与实际保质期的平均偏差为0.96天。
