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Machine learning in agriculture: A comprehensive updated review
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …
artificial intelligent systems for the sake of making value from the ever-increasing data …
Foundation models in smart agriculture: Basics, opportunities, and challenges
J Li, M Xu, L ** and yield gap analysis from an extensive ground dataset in the US Corn Belt
Crop yield maps estimated from satellite data increasingly are used to understand drivers of
yield trends and variability, yet satellite-derived regional maps are rarely compared with …
yield trends and variability, yet satellite-derived regional maps are rarely compared with …
[HTML][HTML] Using linear regression, random forests, and support vector machine with unmanned aerial vehicle multispectral images to predict canopy nitrogen weight in …
The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N)
supply to the crop N demand is the subject of intense research due to the environmental and …
supply to the crop N demand is the subject of intense research due to the environmental and …
Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in Nepal
Crop yield estimation is a major issue of crop monitoring which remains particularly
challenging in develo** countries due to the problem of timely and adequate data …
challenging in develo** countries due to the problem of timely and adequate data …