基于人工智能的分类器用于预测最近种植的咖啡品种,使用远程驾驶飞机系统
Nicole L Bento1, Gabriel Araújo E S Ferraz1, Rafael Alexandre P Barata1
1Universidade Federal de Lavras/UFLA, Departamento de Engenharia, Avenida Aquenta Sol, Caixa Postal 3037, 37200-000 Lavras, MG, Brazil.
Anais da Academia Brasileira de Ciencias
|November 8, 2023
概括
人工智能模型使用远程驾驶飞机系统 (RPAS) 数据准确预测咖啡品种. 神经网络实现了最高的准确性,在预测咖啡植物特征方面超过了其他算法.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
背景情况:
- 在巴西,咖啡种植在经济上至关重要.
- 远程驾驶飞机系统 (RPAS) 为农业提供先进的数据收集能力.
- 人工智能 (AI) 的分类和预测方法增强了各部门的决策.
研究的目的:
- 为了比较和选择AI模型来预测不同的咖啡品种 (Coffea arabica L.).
- 用RPAS衍生数据评估各种数据分类技术和算法.
- 评估AI模型在识别最近种植的咖啡品种方面的表现.
主要方法:
- 使用Python编程来准备和分析数据.
- 应用分类算法,包括决策树,随机森林,支持矢量机器和神经网络.
- 使用交叉验证,命中率,灵敏度,特异性,F1得分和ROC曲线分析评估模型性能.
主要成果:
- 所有测试的AI算法都显示了咖啡品种的良好预测率.
- 神经网络实现了最高的预测准确度 (0.899%),超过了其他模型.
- 支持矢量机也表现出强的表现,成功率为0.886%.
结论:
- 人工智能,特别是神经网络,显示出对准确咖啡品种预测的巨大潜力.
- 与人工智能分类技术相结合的RPAS数据可以支持咖啡农业的智能决策.
- 该研究为选择最佳AI模型用于精密农业应用提供了一个框架.
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