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Transforming Soil Paradigms with Machine Learning

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Data Science in Agriculture and Natural Resource Management

Part of the book series: Studies in Big Data ((SBD,volume 96))

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Abstract

Numerous technological advancements have assisted to secure the vital key for scientific predictions in various fields, and soil science is no exception. Soil has always been chosen as an indispensable component by scientists, environmentalists, and policy makers for shaping a sustainable present and a secure future. Evidently, a huge amount of soil spatial data is required in the process to attain the desired outcomes. However, the challenges posed by the paucity of time and resources pose the hurdle in the collection and analysis of soil information. State-of-art technologies like Machine Learning (ML) and Big Data come as saviors to address those challenges. ML is helping to quantify, predict, identify, and classify the soil resources. The advanced algorithms, and models helped to gain better insights into soil mapping along with widening the perspective for its better management. Digital Soil Mapping (DSM), ML integrated with spectroscopic soil studies is gaining momentum among the scientific communities. These innovative approaches have the capabilities to solve global issues like desertification, ecological stability, carbon pool management and climate mitigation in a holistic and integrated way by keeping the soil as one of the key parameters. In this chapter, an attempt has been made to present a comprehensive overview of ML algorithms, which have been adopted by many researchers across the globe in prediction of various soil properties and presented a case study on digital soil mapping by using ML algorithms.

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Authors and Affiliations

  1. Department of Soil Science and Agricultural Chemistry, University of Agricultural Sciences, Bangalore,, 560065, India

    Kumari Sweta & A. Sathish

  2. ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Bangalore, 560024, India

    S. Dharumarajan, Amar Suputhra, M. Lalitha, R. Vasundhara, B. Kalaiselvi, B. N. Shashi Kumar & Rajendra Hegde

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  1. Kumari Sweta
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  2. S. Dharumarajan
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  3. Amar Suputhra
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  4. M. Lalitha
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  5. R. Vasundhara
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Editors and Affiliations

  1. Division of Remote Sensing Applications, ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur, India

    G. P. Obi Reddy

  2. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India

    Mehul S. Raval

  3. Centre of Studies in Resources Engineer, Indian Institute of Technology Bombay (IITB), Mumbai, India

    J. Adinarayana

  4. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India

    Sanjay Chaudhary

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Sweta, K. et al. (2022). Transforming Soil Paradigms with Machine Learning. In: Reddy, G.P.O., Raval, M.S., Adinarayana, J., Chaudhary, S. (eds) Data Science in Agriculture and Natural Resource Management. Studies in Big Data, vol 96. Springer, Singapore. https://doi.org/10.1007/978-981-16-5847-1_12

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