Data Analytics in Agriculture

Food security is a crucial global need, threatened by population growth, climate change, and decreasing arable land. Data-driven agriculture is the most promising approach to solving these current and future problems by improving crop yields, reducing costs, and ensuring sustainability. As the number of smart sensors and machines on farms increases and a greater variety of data is used, farms will become increasingly data-driven, enabling the development of smart farming. This is possible, thanks to new technologies that enable massive data storage, such as cloud computing and Hadoop, in addition to processing and analysis through Big Data and machine learning. In this chapter, we explain some practical examples of their use.

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Authors and Affiliations

1. Departamento de Ciencias de la Computación e Informática, Facultad de Ingeniería y Ciencias, Universidad de La Frontera, Temuco, Chile Ania Cravero Leal

1. Ania Cravero Leal

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Editors and Affiliations

1. Rubber Research Institute of India (formerly), Kottayam, Kerala, India P. M. Priyadarshan
2. Department Agricultural Sciences, University of Helsinki, Helsinki, Finland Shri Mohan Jain
3. Amity Centre for Nuclear Biotechnology, Amity University Maharashtra, Mumbai, India Suprasanna Penna
4. Department of Agricultural Biotechnology, King Faisal University, Al-Ahsa, Saudi Arabia Jameel M. Al-Khayri

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Leal, A.C. (2024). Data Analytics in Agriculture. In: Priyadarshan, P.M., Jain, S.M., Penna, S., Al-Khayri, J.M. (eds) Digital Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-031-43548-5_17

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• DOI : https://doi.org/10.1007/978-3-031-43548-5_17
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