Enhancing agricultural methods through the utilization of Earth observation and artificial intelligence (AI) has emerged as a significant concern. The ability to quantify soil parameters on a large scale can play a pivotal role in optimizing the fertilization process. While techniques for noninvasive estimation of soil parameters from hyperspectral images (HSIs) exist, their validation typically occurs across different datasets and employs varying validation protocols. This diversity renders them inherently challenging (or even impossible) to compare objectively.

中文翻译：

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从高光谱图像估计土壤参数：基准数据集和 HYPERVIEW 挑战的结果


通过利用地球观测和人工智能（AI）加强农业方法已成为一个重要问题。大规模量化土壤参数的能力可以在优化施肥过程中发挥关键作用。虽然存在从高光谱图像 (HSI) 非侵入性估计土壤参数的技术，但它们的验证通常发生在不同的数据集上，并采用不同的验证协议。这种多样性使得它们本质上具有挑战性（甚至不可能）进行客观比较。