应用多层感知器人工神经网络,根据物理特征识别桃子品种
Adel M Al-Saif1, Mahmoud Abdel-Sattar1,2, Abdulwahed M Aboukarima3,4
1College of Food and Agriculture Sciences, Department of Plant Production, King Saud University, Riyadh, Saudi Arabia.
PeerJ
|November 15, 2024
概括
这项研究使用了多层感知器人工神经网络来根据皮肤颜色,坚硬度和尺寸识别桃子品种. 该模型实现了100%的准确性,为水果分类提供了一种可行的方法.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 食品科学 食品科学 食品科学
背景情况:
- 准确识别水果品种和质量对于新鲜水果行业至关重要.
- 需要对桃子特性进行客观评估,以提高加工效率.
研究的目的:
- 为了评估九种桃子品种的皮肤颜色,坚硬度和大小的差异.
- 应用多层感知人工神经网络用于使用这些属性识别桃子品种.
主要方法:
- 分析了9种桃子品种的皮肤颜色 (L*,a*,b*),坚硬度和尺寸 (极和十字直径).
- 一个多层感知人造神经网络被训练了特定的输入,隐藏和输出层,利用过度触角和软max激活函数.
- 评估了变量的重要性,以确定关键的识别属性.
主要成果:
- 北极直径,L*,b*,a*,坚固度和横直径显著影响了桃子的识别.
- 多层感知子模型实现了0.165.5的交叉误差.
- 人工神经网络模型在测试阶段显示了100%的分类准确性.
结论:
- 多层感知人造神经网络是一种可行且高效的桃子品种识别和分类方法.
- 关于桃子属性的定量数据可以提高加工桃子的加工效率和质量.
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