Machine learning in agriculture: A comprehensive updated review

L Benos, AC Tagarakis, G Dolias, R Berruto, D Kateris… - Sensors, 2021 - mdpi.com
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 …

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
JM Deines, R Patel, SZ Liang, W Dado… - Remote sensing of …, 2021 - Elsevier
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 …

[HTML][HTML] Using linear regression, random forests, and support vector machine with unmanned aerial vehicle multispectral images to predict canopy nitrogen weight in …

H Lee, J Wang, B Leblon - Remote Sensing, 2020 - mdpi.com
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 …

Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in Nepal

R Fernandez-Beltran, T Baidar, J Kang, F Pla - Remote sensing, 2021 - mdpi.com
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 …