review · Big Data and Cognitive Computing
Healthy soil is critical for sustainable food production, yet intensive cultivation can degrade soil quality and deplete essential nutrients, resulting in reduced crop yields. Precision agriculture relies on smart soil prediction and digital soil mapping to provide accurate insights into nutrient distribution across farmland. Machine learning methods are increasingly underpinning these intelligent soil prediction systems. This systematic review synthesises the application of machine learning approaches to the estimation of soil properties and nutrient levels. It investigates fundamental soil components, the prediction of soil parameters, available soil datasets, digital soil mapping, and soil information systems. In addition, the inquiry examines how soil nutrients directly influence crop growth, highlighting how data-driven agricultural methods can support improved crop productivity and food quality.
Declining soil nutrients threaten agricultural output and food security. By reviewing machine learning approaches for digital soil mapping and parameter prediction, this work outlines how data-driven tools can help agricultural stakeholders monitor soil health. Understanding these predictive technologies supports the adoption of precision farming strategies aimed at boosting crop yields and sustaining soil quality.
The work addresses precision agriculture applications, particularly digital soil mapping and soil information systems that could be used by farm managers and agritech developers to guide nutrient management. Because this is a review analysing existing models, datasets, and systems rather than testing a specific deployed tool, the insights reflect early-stage synthesis to inform future software development and smart farming implementations.
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Agriculture is essential to a flourishing economy. Although soil is essential for sustainable food production, its quality can decline as cultivation becomes more intensive and demand increases. The importance of healthy soil cannot be overstated, as a lack of nutrients can significantly lower crop yield. Smart soil prediction and digital soil mapping offer accurate data on soil nutrient distribution needed for precision agriculture. Machine learning techniques are now driving intelligent soil prediction systems. This article provides a comprehensive analysis of the use of machine learning in predicting soil qualities. The components and qualities of soil, the prediction of soil parameters, the existing soil dataset, the soil map, the effect of soil nutrients on crop growth, as well as the soil information system, are the key subjects under inquiry. Smart agriculture, as exemplified by this study, can improve food quality and productivity.
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DOI: 10.3390/bdcc7020113
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