使用无人机系统和多光谱图像,基于传感器对花生病脱叶的量化
Rebecca L Barocco1, James W Clohessy1, G Kelly O'Brien1
1North Florida Research and Education Center, Department of Plant Pathology, University of Florida Institute of Food and Agricultural Sciences, Quincy, FL 32351.
Plant disease
|August 1, 2023
概括
这项研究开发了一种使用无人机图像的算法,以准确测量花生叶斑点疾病的严重程度. 这项技术有助于农民做出更好的收获决策,并支持疾病管理研究.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 遥感 遥感 遥感 遥感
背景情况:
- 早期和晚期的叶子斑是主要的花生疾病,导致大量的产量损失.
- 杀菌剂的应用是昂贵的,并且可能不会在最佳时间进行.
- 对植物病的客观监测对于综合管理至关重要.
研究的目的:
- 开发一种算法,用无人机系统 (UAS) 多光谱图像量化花生病脱叶.
- 为研究和种植者决策支持创造一种可靠的方法来评估疾病严重程度.
主要方法:
- 开发了一种脱叶量化算法,将绿色规范差异植被指数 (GNDVI) 和修改土壤调整植被指数 (MSAVI) 结合起来.
- 将算法校准为特定网站的峰值树冠生长.
- 使用β回归来训练模型,将图像数据与视觉脱叶估计相关联.
主要成果:
- 经过训练的模型实现了高精度 (伪R2 = 0.71) 和良好的验证 (R2 = 0.84,RMSE = 4.0%).
- 该模型在独立的实地试验数据上表现出强的性能 (R2 = 0.79和R2 = 0.87).
- 该算法提供了一个客观的测量中到后季花生病的严重程度.
结论:
- 基于UAS的多谱图像提供了一个客观和可重复的方法来评估花生病脱叶.
- 这项技术可以帮助种植者做出明智的收获决策.
- 未来与地面传感器的整合将使病原体识别和早期疾病检测成为可能.
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