在有机和传统种植实践下种植的蔬菜作物的超谱歧视:一种机器学习方法
Manoj Kaushik1, Rama Rao Nidamanuri2, B Aparna3
1Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Thiruvananthapuram, Kerala, 695547, India.
Scientific reports
|March 6, 2025
概括
超光谱遥感可以以85-95%的准确度区分有机作物和传统作物. 机器学习模型和同时出现的作物对歧视产生影响,需要进行多站点研究以进行可靠的绘制.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 有机作物认证依赖于人工检查和记录,这是劳动密集型的.
- 超光谱遥感为作物信息提取提供了一个可扩展的方法.
- 区分有机农作物与传统农作物对于市场完整至关重要.
研究的目的:
- 为了调查有机与传统种植的和红的光谱歧视.
- 评估同时出现的作物物种对光谱歧视准确性的影响.
- 用超光谱数据评估各种用于多作物分类的机器学习算法.
主要方法:
- 采用高分辨率的现场高光谱测量,对选定的蔬菜作物进行测量.
- 应用了12个机器学习算法来解决一个多作物分类问题.
- 鉴于作物类型和种植实践,量化光谱区分的准确性.
主要成果:
- 在有机和传统蔬菜作物中,实现了高光谱区分精度 (85-95%).
- 机器学习模型的选择和同时出现的物种的存在将歧视精度降低了高达10%.
- 由于生理差异,有机种植的作物与传统种植的作物相比,具有明显的光谱特征.
结论:
- 超光谱遥感,再加上适当的机器学习方法,可以在很大面积上绘制有机作物的地图.
- 建议进行多地点,多种现象学研究,以确保在不同条件下进行强有力的作物歧视.
- 光谱差别显示出可验证和区域级有机作物绘制的前景,增强认证流程.
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