参与式人工智能用于包容性的作物改进
Violet Lasdun1, Davíd Güereña2, Berta Ortiz-Crespo2
1London School of Economics, Houghton St., London WC2A 2AE, United Kingdom.
Agricultural systems
|October 30, 2024
概括
移动表型工具克服了作物育种的数据限制,允许更快,更准确的品种选择. 这项技术赋予农民和育种者权力,加速作物改进以适应气候变化.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 计算生物学 计算生物学
背景情况:
- 全球南方在作物育种方面面临着"表型瓶",由于人工,容易出错的方法,阻碍了气候适应性品种的发展.
- 传统的育种计划往往排除了农民,导致改进作物品种的采用率较低.
研究的目的:
- 通过使用移动表型化技术来实现分散的,高质量的数据收集,使作物育种民主化.
- 通过使用多式联运数据,将农民的偏好纳入品种开发中,确保与当地需求保持一致.
主要方法:
- 在哥伦比亚和坦桑尼亚开发和实地测试新的移动表型工具.
- 对数据质量和准确性的分析与传统方法和基本事实相比.
- 以人为中心的设计 (HCD) 的应用,以确保技术的可用性.
主要成果:
- 计算机视觉 (CV) 支持的移动表型显著降低了得分变化.
- 通过基准真实数据实现了高特征准确度 (皮尔森相关性0.88-0.95).
- 通过在不到一分钟的时间内实现情节表型,减少了劳动力.
结论:
- 移动表型化允许任何拥有智能手机的人收集高质量的数据,扩大农场表型化能力.
- 移动表型和人工智能的包容性创新加速了特征评估和农民参与品种选择.
- 这些进步促进了品种的快速转换,增强了粮食安全和在气候变化下作物品种的文化相关性.
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