Exploring crop health and its associations with fungal soil microbiome composition using machine learning applied to remote sensing data

''' <span style="color:#006400"> Article title: "Exploring crop health and its associations with fungal soil microbiome composition using machine learning applied to remote sensing data"''' <br/>
'''Authors: Mathies Brinks Sørensen, David Faurdal, Giovanni Schiesaro, Emil Damgaard Jensen, Michael Krogh Jensen, Line Katrine Harder Clemmensen
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'''Source:''' https://doi.org/10.1038/s43247-025-02330-0  <br/>
'''Date: 7 May 2025''' <br/>

<u> '''Summary and Introduction''' </u> <br/>

<p style="text-indent: 2em;">The goal of this study was to examine how remote sensing combined with machine learning could assist in understanding crop health. One of the largest growing concerns around the globe has been an increase in food insecurity as populations continue to rise. For this reason, it is important to apply sustainable agricultural practices in order to increase crop yields and productivity. <br/> 
:Smart farming methods integrate remote sensing technologies such as satellite imagery and/or drones with data analytics tools to monitor fields. The common satellites in such applications are Sentinel-2 and Landsat 8, which have medium resolution but only sample every several days. The MODIS satellite is another option since it provides daily samples; however, it is low resolution and provides less accurate analyses. Multispectral images from these satellites are used to calculate vegetation indices that are used to visualize vegetation characteristics. The most common vegetation indices are the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the leaf area index (LAI). <br/>
:Of all the vegetation indices, NDVI is the most popular because of the large number of factors that can affect it. Some of these include soil moisture, climate, nutrients, crop type, and temperatures. This paper conducted a literature review on how different microbial species may affect the NDVI. Prior research has proven a link between certain bacteria and NDVI values. As of now, there has been limited research linking the existence of certain fungi and NDVI values. Though the research that does exist has shown that soil decomposers play a role in higher NDVI values and that fungal richness is connected to environmental factors such as pH and landscape types. The lack of research on fungal biomes motivated this study to better examine the connection between fungal soil composition and NDVI values. <br/>