IT Professional · 2020 · 37 citations · 12 references
AgricultureCrop SciencesSoil SciencesArtificial IntelligenceSoil ManagementSoil ScienceAgricultural ApplicationsAgricultural Land ManagementAgricultural TechnologyDecision Support For Crop ManagementCrop MonitoringData-driven AgricultureGeospatial Big DataObservational Data CollectionPrecision Agriculture Decision MakingSite-based LearningSite-specific Pest Monitoring
Agricultural systems are enormously variable in space and time. New and developing artificial intelligence (AI)-based tools can leverage site-based science and big data to help farmers and land managers make site-specific decisions. These tools are improving information about soils and vegetation that forms the basis for investments in management actions, provides early warning of pest and disease outbreaks, and facilitates the selection of sustainable cropland management practices. Continued progress with AI will require more observational data across a wide range of agricultural settings, over long time periods.
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A statistical explanation of MaxEnt for ecologists
Jane Elith, Steven J. Phillips, Trevor Hastie et al. · Diversity and Distributions · 2010 · 6.6K citations · Full text
SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl, Jorge Mendes de Jesus, G.B.M. Heuvelink et al. · PLoS ONE · 2017 · 4.7K citations · Full text
Scaling up: the essence of effective agricultural research
J. B. Passioura · Functional Plant Biology · 2010 · 121 citations
Agriculture, Agronomy, Crop Sciences +17