Prediction of pasture yield using machine learning-based optical sensing: a systematic review
Problem
Accurate and reliable predictions of biomass yield are needed for pasture management decisions such as fertilization, irrigation, grazing, and mowing.
Why it matters
Biomass yield predictions guide costly and environmentally consequential farm management decisions.
What the AI did
No single AI system is demonstrated; the authors systematically reviewed 43 studies on machine learning with optical sensing data for predicting grassland biomass yield.
Results
not stated in source (abstract truncated before evaluation results)
Impact
Synthesizes how ML techniques and optical sensing are being adopted for pasture yield prediction.
Record
| Venue | Precision Agriculture |
|---|---|
| Published | 2023 |
| Work type | review |
| Authors | Christoph Stumpe, Joerg Leukel, Tobias Zimpel |
| Citations | 29 (OpenAlex, at import time) |
| References | 89 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s11119-023-10079-9 |
| Open access | hybrid |
| Keywords | machine learning, optical sensing, pasture yield, systematic review, biomass prediction, grasslands |
Importance rating
real but narrower advance
Biomass yield predictions guide costly, environmentally consequential pasture decisions (fertilization, irrigation, grazing); a 43-study review supports agriculture relevance but states no scale.