使用种子形态学和机器学习技术识别和分类零食类型西瓜 (Citrullus lanatus) 基因型
Uğur Ercan1, Sıtkı Ermiş2, Onder Kabas3
1Department of Informatics, Akdeniz University, 07070 Antalya, Türkiye.
Foods (Basel, Switzerland)
|December 11, 2025
概括
机器学习使用形态和颜色数据准确地从种子中识别西瓜基因型. 随机森林模型达到92.22%的准确性,为育种和种子生产提供可靠的基因型分类.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 遗传学 是一个遗传学.
背景情况:
- 精确的西瓜 (Citrullus lanatus) 种子的基因型鉴定对于育种计划和商业生产至关重要.
- 传统的种子识别方法可能耗时,可能缺乏精度.
- 开发自动化,非破坏性的基因型分类方法是非常可取的.
研究的目的:
- 评估机器学习 (ML) 算法的有效性,用于根据种子特征自动识别西瓜基因型.
- 为了比较人工神经网络 (ANN),随机森林 (RF) 和额外树 (ET) 模型在分类西瓜种子中的性能.
- 确定种子的形态,物理和色度属性是否可以可靠地预测基因型.
主要方法:
- 收集了九种西瓜基因型的数据,分析了每种基因型的200种种子.
- 使用高分辨率成像和数字测量提取形态 (大小,形状),物理 (重量) 和色度 (L,a,b) 属性.
- 经过训练和验证的ANN,RF和ET模型使用十倍交叉验证方法.
主要成果:
- 随机森林 (RF) 模型表现出最高的性能,达到92.22%的准确性,91.87%的F1得分和0.9118科恩的卡帕.
- 额外树 (ET) 和人工神经网络 (ANN) 模型的准确性分别较低,分别为90.00%和86.11%.
- 统计分析证实RF和ET显著超过ANN,RF提供优越的稳定性.
结论:
- 机器学习框架提供了一种快速,可靠和非破坏性的方法,通过基因型对零食类型西瓜种子进行分类.
- 这些ML方法具有强大的潜力,可以提高品种在育种中的可追溯性,在种子生产中的质量控制,并满足工业需求.
- 该研究验证了种子形态,物理和色度数据的使用,以准确地进行基因型分类.
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