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References from Evaluating Machine Learning and Deep Learning Models for Predicting Rice Vegetation Condition in Acadia, Louisiana. Local targets link to admitted publications; unresolved targets remain external evidence.
The Global Lifeline: A Staple Crop Sustaining Two-Thirds of the World’s Population
10.51470/agri.2023.2.3.15 · 2023 · External reference
Optimizing integration techniques for UAS and satellite image data in precision agriculture—A review
10.3389/frsen.2025.1622884 · 2025 · External reference
Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
10.3389/fpls.2022.716506 · 2022 · External reference
Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems
10.1371/journal.pone.0088741 · 2014 · External reference
10.1117/12.3096178
10.1117/12.3096178 · 2026 · External reference
Unresolved reference
2010 · External reference
The Shuttle Radar Topography Mission
10.1029/2005rg000183 · 2007 · External reference
Machine learning approaches for crop yield prediction: A systematic literature review
10.1016/j.rsase.2026.102151 · 2026 · External reference
NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space
10.1016/s0034-4257(96)00067-3 · 1996 · External reference
Google Earth Engine: Planetary-scale geospatial analysis for everyone
10.1016/j.rse.2017.06.031 · 2017 · External reference
10.5194/isprs-archives-xlviii-m-5-2024-45-2025
10.5194/isprs-archives-xlviii-m-5-2024-45-2025 · 2025 · External reference
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
10.1016/s0034-4257(02)00096-2 · 2002 · External reference
Adapting machine learning for environmental spatial data—A review
10.1016/j.ecoinf.2024.102634 · 2024 · External reference
10.3390/rs15082014
10.3390/rs15082014 · 2023 · External reference
Deep learning in agriculture: A survey
10.1016/j.compag.2018.02.016 · 2018 · External reference
10.1109/iccmc65190.2025.11140702
10.1109/iccmc65190.2025.11140702 · 2025 · External reference
10.1109/accai61061.2024.10601854
10.1109/accai61061.2024.10601854 · 2024 · External reference
The impact of cultivar development and cultural practices on Louisiana rice yield
10.1002/agg2.20231 · 2022 · External reference
A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
10.3390/rs14122843 · 2022 · External reference
Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer
10.3390/rs13050968 · 2021 · External reference
Unresolved reference
2025 · External reference
10.5525/gla.thesis.84306
10.5525/gla.thesis.84306 · 2024 · External reference
Unresolved reference
External reference
Developing a general post-classification framework for land-cover mapping improvement using high-spatial-resolution remote sensing imagery
10.1080/2150704x.2017.1306137 · 2017 · External reference
The power of on-farm data for improved agronomy
10.1016/j.gfs.2024.100752 · 2024 · External reference
10.1109/mysurucon55714.2022.9972523
10.1109/mysurucon55714.2022.9972523 · 2022 · External reference
ERA5-Land: A state-of-the-art global reanalysis dataset for land applications
10.5194/essd-13-4349-2021 · 2021 · External reference
Machine Learning of Spatial Data
10.3390/ijgi10090600 · 2021 · External reference
10.1109/icccnt61001.2024.10724901
10.1109/icccnt61001.2024.10724901 · 2024 · External reference
Advancing global food security via multidimensional strategies for sustainable paddy productivity
10.31893/multirev.2026097 · 2025 · External reference
Climate variation explains a third of global crop yield variability
10.1038/ncomms6989 · 2015 · External reference
A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning
10.1016/j.srs.2025.100227 · 2025 · External reference
Comprehensive Study and Review of Thirty Key Research Contributions on Artificial Intelligence in the Field of Agricultural
10.22214/ijraset.2025.74167 · 2025 · External reference
Bias in random forest variable importance measures: Illustrations, sources, and a solution
10.1186/1471-2105-8-25 · 2007 · External reference
Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod
10.1016/j.isprsjprs.2014.12.006 · 2015 · External reference
Unresolved reference
External reference
Crop yield prediction using machine learning: A systematic literature review
10.1016/j.compag.2020.105709 · 2020 · External reference
10.3389/frsen.2026.1840405
10.3389/frsen.2026.1840405 · 2026 · External reference
Systems-based rice improvement approaches for sustainable food and nutritional security
10.1007/s00299-021-02790-6 · 2021 · External reference
A Temporal–Geospatial Deep Learning Framework for Crop Yield Prediction
10.3390/electronics13214273 · 2024 · External reference
Unresolved reference
2010 · External reference
Current status of Landsat program, science, and applications
10.1016/j.rse.2019.02.015 · 2019 · External reference
Progress and Perspectives of Crop Yield Forecasting With Remote Sensing: A review
10.1109/mgrs.2025.3571906 · 2025 · External reference
10.1609/aaai.v31i1.11172
10.1609/aaai.v31i1.11172 · 2017 · External reference
Spatial Panel Models of Crop Yield Response to Weather: Econometric Specification Strategies and Prediction Performance
10.1017/aae.2021.29 · 2022 · External reference
10.1201/9781003308423-16
10.1201/9781003308423-16 · 2023 · External reference
Benefits of the free and open Landsat data policy
10.1016/j.rse.2019.02.016 · 2019 · External reference
