Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
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Publisher
Basel: MDPI AG
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Language
English
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Publisher
Basel: MDPI AG
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Scope and Contents
Contents
Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible f...
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Full title
Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
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Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_e49f110204fb464499e1add3db67de66
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e49f110204fb464499e1add3db67de66
Other Identifiers
ISSN
2073-4395
E-ISSN
2073-4395
DOI
10.3390/agronomy10071046
