A combined remote sensing and machine learning approach to monitoring crop stress and predicting crop yield

遥感和机器学习相结合的方法来监测作物胁迫和预测作物产量

• 批准号: 2896437
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2896437
• 关键词: combined; remote; sensing; machine; learning

Optimising crop yield under current and future growth conditions is a key priority for ensuring future food security. The challenges of a changing climate, through increasing temperatures, more variable precipitation, and drought will lead to increased abiotic crop stress, potentially limiting yields and compromising the maintenance of a reliable food supply. The ability of crops to physiologically respond to multiple, simultaneous stresses underpins the resilience of agricultural systems. Technological advances in remote sensing (RS) hold the potential for monitoring crop stress in real-time, however detecting the specific type of abiotic stress from RS data is challenging and compromises both the ability for dynamic intervention and also for understanding the impact on yields. Machine learning (ML) shows great promise for developing new analytical tools to extract key biological information from a range of remotely sensed data. The overall aim of the PhD project is to combine RS measurements with in-depth analyses of plant physiology in order to develop artificial intelligence (AI)/ML algorithms to make effective predictions about different signals of abiotic crop stress and the impacts on crop yield.

在当前和未来的增长条件下优化农作物产量是确保未来粮食安全的关键重点。气候变化的挑战，通过升高，降水量变化更大和干旱会导致非生物作物压力增加，可能限制产量并损害可靠的食物供应的维持。农作物在生理上对多种，同时的压力做出反应的能力是农业系统的弹性。遥感（RS）的技术进步具有实时监测作物应力的潜力，但是发现RS数据中的特定类型的非生物应力类型具有挑战性，并损害了动态干预的能力，也损害了对产量的影响。机器学习（ML）显示了开发新的分析工具，以从一系列远程感知的数据中提取关键的生物学信息。博士项目的总体目的是将RS测量结果与植物生理学的深入分析相结合，以开发人工智能（AI）/ML算法，以对非生物作物压力的不同信号和对作物产量的影响做出有效的预测。