Salinity Determination of Soil Through Machine Learning and Remote Sensing Techniques
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https://doi.org/10.5281/zenodo.19608245Anahtar Kelimeler:
Soil salinity, remote sensing, sustainable agricultureÖzet
Global soil salinity is a problem that jeopardizes ecosystem health and agricultural productivity. Applying traditional soil salinity analysis techniques over large areas can be challenging, expensive, and time-consuming because they rely on laboratory-based measurements. Thus, machine learning techniques and remote sensing are being utilized more and more to determine soil salinity quickly and accurately. By using satellites and aerial sensors to record the spectral characteristics of the soil surface, remote sensing technologies can identify indirect markers of soil salt buildup. Specifically, the salinity of soil is often mapped using spectral data from the visible, near-infrared (VNIR), and thermal bands. In order to forecast soil salinity, machine learning algorithms analyze these spectral data to model intricate and non-linear relationships. Methods such as Support Vector Machines (SVM), Random Forest, Artificial Neural Networks (ANN), and Deep Learning provide high accuracy rates in predicting soil salinity. These methods have a lot of promise for tracking changes in soil salinity over time and space, improving farming methods, and creating potent anti-salinity plans. Additionally, by assisting with decisions about soil management, particularly in large-scale farming areas, these techniques support the growth of sustainable agricultural practices. Consequently, it is recognized that a promising approach to controlling and tracking soil salinity is the combination of machine learning and remote sensing.
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