There has been a growing use of remote sensing, climate
Research gap analysis derived from 3 agriculture papers in our local library.
The gap
There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear.
Evidence profile
Sourced from the abstract and future work and future-work section of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 2 journals. Those papers have been cited 36 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Improving Wheat Yield Prediction with Multi-Source Remote Sensing Data and Machine Learning in Arid Regions (2025) · Remote Sensing · cited 36× · doi
There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear.
generalabstractKeywords: there growing remote sensing climate combination estimate yields optimal indices time window wheat yield prediction - Maize yield prediction using machine learning: a systematic literature review (2026) · Frontiers in Artificial Intelligence · doi
LSTM. J. (2022). Prediction of corn yield in the USA Corn Belt using satellite data and machine learning: From an evapotranspiration perspective. Agriculture, 12(8), 1263. https://doi. org/10.3390/agriculture12081263 MDPI M. F. d. Oliveira et al.
generalfuture workevidence 5/5Keywords: corn agriculture lstm prediction yield belt using satellite machine learning evapotranspiration perspective https mdpi oliveira - Rice Yield Estimation Based on Machine Learning Applied to UAV Remote Sensing Data (2026) · Remote Sensing · doi
Further research is needed to explore the application of ML models in different regions and soil types, - Investigating the use of other UAV spectral data and machine learning algorithms for rice yield prediction, - Examining the impact of climate change on rice yield and the potential of ML models to predict these changes
generalfuture-work sectionevidence 5/5Keywords: further research needed explore application models different regions
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