Impact of large kernel size on yield prediction: a case study of corn yield prediction with SEDLA in the U.S. Corn Belt

Predicting agricultural yields is imperative for effective planning to sustain the growing global population. Traditionally, regression-based, simulation-based, and hybrid methods were employed for yield prediction. In recent times, there has been a notable shift towards the adoption of Machine Lear...

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Opis bibliograficzny

Główni autorzy:	Anil Suat Terliksiz, Deniz Turgay Altilar
Format:	Artigo
Język:	Inglês
Wydane:	IOP Publishing 2024-01-01
Seria:	Environmental Research Communications
Hasła przedmiotowe:	yield prediction deep learning low-cost DL architecture U.S. Corn Belt CNN MODIS
Dostęp online:	https://doi.org/10.1088/2515-7620/ad27fa
Etykiety:	Dodaj etykietę Nie ma etykietki, Dołącz pierwszą etykiete!