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

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

Summary and Introduction

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.
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).
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.

Methods and Data
The first step of the methodology was to download the satellite images and apply the NDVI. Low NDVI depicts bad crop health, while high NDVI depicts good crop health. Then, the NDVI values were adjusted for abiotic (non-living) factors by removing their influence through a random forest model. Next, the NDVI values were analyzed based on different fungal soil microbiomes. The abiotic data, which were used to adjust the NDVI, came from various sources. Topsoil composition data were obtained from the LUCAS 2018 topsoil dataset, but only the three most prevalent crop types were used: wheat, barley, and maize. The data was further filtered by removing instances during winter months since most of the images were covered in snow. Climate data came from the ERA5 Copernicus dataset, and information related to soil properties was obtained, such as soil temperature, soil moisture, soil type, and air temperature. These variables were then linked to each NDVI observation. This step was vital to make sure that any recorded changes in the NDVI values resulted exclusively from fungi and not any other non-living factors. If too many parameters were changing simultaneously without adjustments, no reasonable correlation could be examined. The last dataset used was the LUCAS biodiversity dataset. This dataset contains a total of 885 biosamples, of which 115 were for wheat, barley, and maize crops, during the specific time period.