Machine Learning Yield Forecasting
Machine Learning Yield Forecasting is a computational methodology that employs machine learning algorithms to predict agricultural yield by learning patterns from historical and environmental data, distinguishing itself from traditional statistical forecasting through its capacity to model nonlinear, high-dimensional relationships.
767
Publications
53K
Citations
2.8K
Authors
1K
Institutions
Publications per year
2017–2026
655
Parents
767
Random Forests for Global and Regional Crop Yield Predictions
Jig Han Jeong, Jonathan P. Resop, Nathaniel D. Mueller et al. · PLoS ONE · 2016 · 839 citations · Full text
Agriculture, Agronomy, Crop Sciences +15
Crop Yield Prediction Using Deep Neural Networks
Frontiers in Plant Science · 2019 · 795 citations · Full text
A CNN-RNN Framework for Crop Yield Prediction
Frontiers in Plant Science · 2020 · 579 citations · Full text
Agriculture, Agronomy, Crop Sciences +15
1–5 of 767
2.8K
9
h-index
7
h-index
7
h-index
6
h-index
6
h-index
1–5 of 2.8K
1K

Chinese Academy of Sciences
Beijing, China
24 h-index
77 publications
6.6K citations
China Agricultural University
Beijing, China
18 h-index
59 publications
3.9K citations
University of Chinese Academy of Sciences
Beijing, China
17 h-index
57 publications
4.4K citations
Ministry of Agriculture and Rural Affairs
Beijing, China
17 h-index
49 publications
3K citations
Iowa State University
Ames, United States
16 h-index
44 publications
9.6K citations
Institute of Geographic Sciences and Natural Resources Research
Beijing, China
14 h-index
41 publications
3.7K citations
Vellore Institute of Technology University
Vellore, India
14 h-index
31 publications
3.5K citations
Chinese Academy of Agricultural Sciences
Beijing, China
11 h-index
30 publications
1.5K citations
1–8 of 1K
55 publications · 6.3K citations
38
h-index
19 publications · 1.8K citations
19
h-index
19 publications · 3K citations
19
h-index
17 publications · 781 citations
16
h-index
16 publications · 837 citations
15
h-index
1–5