Using machine learning for crop yield prediction in the past or the future
The use of ML in agronomy has been increasing exponentially since the start of the century, including data-driven predictions of crop yields from farm-level information on soil, climate and management. However, little is known about the effect of data partitioning schemes on the actual performance o...
Main Authors: | Alejandro Morales, Francisco J. Villalobos |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-03-01
|
Series: | Frontiers in Plant Science |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1128388/full |
Similar Items
-
Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms
by: Murali Krishna Gumma, et al.
Published: (2024-03-01) -
Simulation of Crop Yields Grown under Agro-Photovoltaic Panels: A Case Study in Chonnam Province, South Korea
by: Jonghan Ko, et al.
Published: (2021-12-01) -
High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia
by: Kindie Tesfaye, et al.
Published: (2023-12-01) -
Simulation of Maize Lethal Necrosis (MLN) Damage Using the CERES-Maize Model
by: William D. Batchelor, et al.
Published: (2020-05-01) -
Modeling the growth, yield and N dynamics of wheat for decoding the tillage and nitrogen nexus in 8-years long-term conservation agriculture based maize-wheat system
by: Kamlesh Kumar, et al.
Published: (2024-01-01)