Results 81 to 90 of about 163,439 (186)

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Environmental Research Letters, 2020
Crop yield estimates over large areas are conventionally made using weather observations, but a comprehensive understanding of the effects of various environmental indicators, observation frequency, and the choice of prediction algorithm remains elusive.
Yanghui Kang, Mutlu Ozdogan, Xiaojin Zhu, Zhiwei Ye, Christopher Hain, Martha Anderson   +5 more
doaj   +1 more source

Airborne monitoring of crop canopy temperatures for irrigation scheduling and yield prediction [PDF]

Airborne and ground measurements were made on April 1 and 29, 1976, over a USDA test site consisting mostly of wheat in various stages of water stress, but also including alfalfa and bare soil.
Goettelman, R. C., Idso, S. B., Jackson, R. D., Lapado, R. L., Millard, J. P., Reginato, R. J.   +5 more
core   +1 more source

Using gross primary production data and deep transfer learning for crop yield prediction in the US Corn Belt

International Journal of Applied Earth Observations and Geoinformation
Estimating crop yield prior to harvest plays a crucial role in agricultural decision making. Machine learning models that use remote sensing (RS) data can provide quick and early estimates of crop yield.
Shahid Nawaz Khan, Dapeng Li, Maitiniyazi Maimaitijiang   +2 more
doaj   +1 more source

PREDICTION OF CROP YIELDS ACROSS FOUR CLIMATE ZONES IN GERMANY: AN ARTIFICIAL NEURAL NETWORK APPROACH [PDF]

This paper shows the ability of artificial neural network technology to be used for the approximation and prediction of crop yields at rural district and federal state scales in different climate zones based on reported daily weather data. The method may
Richard S.J. Tol, Thomas Heinzow
core  

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery [PDF]

, 2019
Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time.
Baenziger, P. Stephen, Belamkar, Vikas, Bhatta, Madhav, Garst, Nicholas D., Ge, Yufeng, Howard, Reka, Li, Jiating, Li, Jiating, Stoll, Hannah, Veeranampalayam-Sivakumar, Arun-Narenthiran   +9 more
core   +1 more source

Crop yield prediction using deep learning

, 2022
Multi- Model Ensemble with Deep Neural Network Based Crop Yield ...
openaire   +1 more source

Crop status evaluations and yield predictions [PDF]

A model was developed for predicting the day 50 percent of the wheat crop is planted in North Dakota. This model incorporates location as an independent variable.
Haun, J. R.
core   +1 more source

Predicting malting barley protein concentration [PDF]

, 2007
The preferred grain protein concentration (CP) of malting barley is 10.5-11.0%, but 9.5-11.5% is acceptable. It is a challenge for farmers to achieve this target with crops grown in heterogeneous fields and exposed to fluctuating weather conditions ...
Pettersson, C.G.
core  

Using machine learning for crop yield prediction in the past or the future. [PDF]

Front Plant Sci, 2023
Morales A, Villalobos FJ.
europepmc   +1 more source

County-scale crop yield prediction by integrating crop simulation with machine learning models. [PDF]

Front Plant Sci, 2022
Sajid SS, Shahhosseini M, Huber I, Hu G, Archontoulis SV.   +4 more
europepmc   +1 more source