用智能手机和深度学习计算地理规模的咖啡桃
Juan Camilo Rivera Palacio1,2,3, Christian Bunn2, Eric Rahn2
1Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, 15374, Germany.
Plant phenomics (Washington, D.C.)
|April 4, 2024
概括
这项研究介绍了一种人工智能驱动的公民科学方法,用于使用智能手机计算咖啡桃. 这种方法使咖啡作物的可扩展,低成本的植物表型化成为可能,即使是在树冠下.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 传统的植物监测方法,如遥感和无人机,对于树冠下的作物,如咖啡,往往是不可行的.
- 这种限制阻碍了大规模的,具有成本效益的植物监测和咖啡生产的表型.
研究的目的:
- 使用人工智能驱动的公民科学方法开发地理规模的咖啡桃计数方法.
- 为了实现咖啡作物的可扩展和低成本的表型化,克服现有技术的局限性.
主要方法:
- 利用基本的智能手机拍摄咖啡树的图像,涉及秘鲁和哥伦比亚近1000名小农.
- 训练并验证了YOLO (You Only Look Once) v8模型用于桃检测,使用来自2,968棵树的8,904张图像.
- 估计每棵树的桃总数,通过将每张图片的平均桃乘以树枝的数量.
主要成果:
- 人工智能模型在秘鲁测试时获得了0.59的R2和0.71在哥伦比亚测试时,证明了跨品种和条件的可转移性.
- 两个国家的整体表现达到了0.72.7的R2.
- 这标志着第一个人工智能驱动的咖啡桃计数方法,实现了地理规模的监控.
结论:
- 开发的AI公民科学方法是一个可扩展和可转移的解决方案,用于咖啡桃计数和表型.
- 这种方法有可能在全球低收入国家进行多年,基于照片的监测.
- 它为咖啡作物管理和研究提供了具有成本效益的替代方案.
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