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使用卷积神经网络模型方法预测木瓜 (Carica papaya L.) 的物理化学特性

Sujin An¹, Geuncheol Oh², Dongyoung Lee³

¹Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, USA.

Journal of food science

|October 16, 2024

概括

这项研究开发了一种新型的卷积神经网络 (CNN) 模型,用于非破坏性的瓜质量评估. 该模型使用图像和重量准确预测物理化学性质,改善农业质量管理.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
食品科学食品科学食品科学

背景情况:

手动的水果质量评估是主观的,耗时的,容易出现错误.
需要非破坏性方法来准确和实用地评估水果的质量.
番茄的质量预测需要超越传统方法的先进技术.

研究的目的:

开发一种新的方法来预测的物理化学性质.
使用一个卷积神经网络 (CNN) 模型,集成图像分析和重量评估.
建立一种非破坏性的方法,用于准确评估的质量.

主要方法:

捕获了532张不同成熟阶段的番茄的图像;增强了数据集到1064张图像.
利用CNN的模型训练了的图像和重量作为输入.
使用平均平方误差 (MSE) 和确定系数 (R2) 评估模型性能.

主要成果:

美国有线电视新闻网 (CNN) 的模型准确地预测了木瓜的各种物理化学性质.
在测试数据集上实现了高R2值,从0.71到0.94不等.
在培训 (0.0284) 和验证 (0.1729) 集中显示低MSE值.

结论:

基于CNN的模型提供了对水果质量的详细,定量洞察.
这种方法提高了农业中预测建模的准确性.
促进了在生产和运输过程中改善番茄的质量管理.

关键词:

卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.评价水果的评价水果的评价机器学习是机器学习.帕帕亚 (Papaya) 是一个葡萄牙菜.

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