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新型转移学习方法用于检测果果类型和质量评估

Muhammad Usama Tanveer¹, Kashif Munir², Amine Bermark³

¹Institute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan.

Scientific reports

|November 18, 2025

概括

本研究引入了一种使用转移学习和机器学习来分类果品种并评估其质量的新方法. 随机森林模型实现了99%的准确性,改善了农业供应链.

科学领域:

农业科学农业科学
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

果品种在味道,质地和外观上有很大的不同,需要精确的分类.
有效的供应链管理和满足消费者需求依赖于准确的果品种识别和质量评估.

研究的目的:

开发一种创新的方法来对果品种进行分类,并使用转移学习和机器学习评估其质量.
引入IncepForestNet方法,用于果分类和质量评估中的特征工程.

主要方法:

通过转移学习利用预训练的Inception V3模型,通过果图像数据集进行微调.
采用了新的IncepForestNet用于空间特征提取,其次是随机森林用于概率特征识别.
整合并比较各种机器学习算法用于分类和质量评估.

主要成果:

拟议的方法准确地分类了果品种,并评估了关键的质量属性.
随机森林模型以99%的准确性和强大的k倍验证证明了出色的性能.
通过对比分析,确定了用于果分类和质量评估的最佳机器学习技术.

结论:

该研究提出了一种结合转移学习和机器学习的强大方法,用于果分类和质量评估.
开发的方法具有优化农业供应链和提高消费者满意度的巨大潜力.
随机森林模型在果品种分类和质量评估任务中都非常有效.

关键词:

人工智能的人工智能是人工智能.在IncepForestNet中,我们发现了森林.机器学习机器学习果水果类型果水果类型质量评估质量评估的质量评估.

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Fruit Development, Structure, and Function

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Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.