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Machine learning can be used to spot early-warning signs of disasters and environmental issues, possibly including natural pandemics, [343] [344] earthquakes, [345] [346] [347] landslides, [348] heavy rainfall, [349] long-term water supply vulnerability, [350] tipping-points of ecosystem collapse, [351] cyanobacterial bloom outbreaks, [352] and ...
Applications of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification.Machine learning is a subdiscipline of artificial intelligence aimed at developing programs that are able to classify, cluster, identify, and analyze vast and complex data sets without the need for explicit programming to do so. [1]
Emerging digital technologies have the potential to be game-changers for traditional agricultural practices. The Food and Agriculture Organization of the United Nations has referred to this change as a revolution: "a 'digital agricultural revolution' will be the newest shift which could help ensure agriculture meets the needs of the global population into the future."
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
The economic and environmental benefits of precision agriculture have also been confirmed in China, but China is lagging behind countries such as Europe and the United States because the Chinese agricultural system is characterized by small-scale family-run farms, which makes the adoption rate of precision agriculture lower than other countries.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
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Some useful resources for learning about e-agriculture in practice are the World Bank's e-sourcebook ICT in agriculture – connecting smallholder farmers to knowledge, networks and institutions (2011), [2] ICT uses for inclusive value chains (2013), [3] ICT uses for inclusive value chains (2013) [4] and Success stories on information and ...