对XGBoost和神经网络模型进行比较分析,以从环境和气象数据中预测一些番茄水果的质量特征
Oussama M'hamdi1,2, Sándor Takács1, Gábor Palotás3
1Institute of Horticultural Sciences, Hungarian University of Agriculture and Life Sciences, Páter K. Str. 1, 2100 Gödöllö, Hungary.
Plants (Basel, Switzerland)
|March 13, 2024
概括
机器学习模型可以准确地预测番茄的质量. 极端梯度增强 (XGBoost) 性能优于人工神经网络 (ANN),在预测可溶性固体 (Brix),烯和精密农业的颜色方面表现出卓越的性能.
科学领域:
- 农业学是一种农业学.
- 计算生物学 计算生物学
- 食品科学 食品科学 食品科学
背景情况:
- 番茄在全球范围内对营养,经济和健康具有重要意义.
- 准确的质量预测对于饮食和农业学研究至关重要.
- 机器学习 (ML) 提供了提高番茄质量评估的潜力.
研究的目的:
- 评估机器学习模型,以预测番茄的关键品质特征.
- 为了比较极端梯度增强 (XGBoost) 和人工神经网络 (ANN) 模型的性能.
- 为了识别影响番茄质量预测的影响因素.
主要方法:
- 在5个季节中利用了来自28个匈牙利地点的48种番茄品种的数据.
- 采用XGBoost和ANN模型来预测°Brix,烯含量和颜色 (a/b比).
- 应用沙普利增量解释 (SHAP) 分析来解释模型预测和特征重要性.
主要成果:
- 在预测°Brix (R2 = 0.98) 和柳科 (R2 = 0.87) 方面,XGBoost表现出很高的准确性.
- 在色彩预测 (a/b比) 中,XGBoost表现出色,R2 = 0.93,而ANN表现不佳 (R2 = -0.35).
- SHAP分析证实了模型的效率,并强调了品种和环境因素的影响.
结论:
- 在预测番茄品质属性,特别是颜色方面,XGBoost优于ANN.
- 机器学习模型,特别是XGBoost,是用于番茄种植的精准农业的有效工具.
- 种类选择和环境条件显著影响番茄质量和模型预测.
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