Summary
Soil health is essential to global sustainable food production. Beyond its role in food production, soil also plays a crucial role in maintaining ecosystem health and mitigating climate change. Monitoring and improving the health of agricultural soils requires insight into spatial variation in soil properties and associated ecosystem functions. Measuring this variation via classic sampling and analysis on field, regional or global scale is challenging due to high spatial variability inherent to soils and to the lack of affordable and reliable measurement methods. We present here a novel and worldwide applicable approach combining NIR spectroscopy using proximal sensors, remote sensing data and deep learning models to predict the main soil properties controlling soil health in the field. Th
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