The remote sensing (RS) technique is less cost- and labour- intensive than ground-based surveys for diverse applications in agriculture. Machine learning (ML), a branch of artificial intelligence (AI), provides an effective approach to construct a model for regression and classification of a multivariate and non-linear system. Without being explicitly programmed, machine learning models learn from training data, i.e., past experience. Machine learning, when applied to remotely sensed data, has the potential to evolve a real-time farm-specific management system to reinforce farmers' ability to make appropriate decisions. Recently, the use of machine learning techniques combined with RS data has reshaped precision agriculture in many ways, such as crop identification, yield prediction and crop water stress assessment, with better accuracy than conventional RS methods. As agriculture accounts for approximately 70% of the worldwide water withdrawals, it must be used in the most efficient way to obtain maximum yields and food production. The use of water management and irrigation based on plant water stress have been demonstrated to not only save water but also increase yield. To date, RS and ML-based results have encouraged farmers and decision-makers to adopt this technology to meet global food demands. This phenomenon has led to the much-needed interest of researchers in using ML to improve agriculture outcomes. However, the use of ML for the potential evaluation of water stress continues to be unexplored and the existing methods can still be greatly improved. This study aims to present an overall review of the widely used methods for crop water stress monitoring using remote sensing and machine learning and focuses on future directions for researchers.

中文翻译：

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各种作物作物水分胁迫测定的遥感和机器学习：批判性审查

对于农业中的各种应用，遥感 (RS) 技术的成本和劳动力密集程度低于地面调查。机器学习 (ML) 是人工智能 (AI) 的一个分支，它为构建多元非线性系统的回归和分类模型提供了一种有效的方法。在没有明确编程的情况下，机器学习模型从训练数据（即过去的经验）中学习。机器学习应用于遥感数据时，有可能发展出针对农场的实时管理系统，以增强农民做出适当决策的能力。最近，机器学习技术结合 RS 数据的使用在许多方面重塑了精准农业，例如作物识别、产量预测和作物水分胁迫评估，具有比传统 RS 方法更好的精度。由于农业约占全球取水量的 70%，因此必须以最有效的方式使用农业，以获得最大产量和粮食产量。已经证明，基于植物水分胁迫的水资源管理和灌溉不仅可以节约用水，还可以提高产量。迄今为止，基于 RS 和 ML 的结果已鼓励农民和决策者采用这项技术来满足全球粮食需求。这种现象导致研究人员对使用 ML 来改善农业成果产生了急需的兴趣。然而，使用 ML 对水资源压力的潜在评估仍有待探索，现有方法仍然可以大大改进。