Sweet corn yield prediction using machine learning models and field-level data
importance 4/10
partially solved
Smart Agriculture and AI agriculture stage: research
Problem
Predicting field-level sweet corn yield from crop genetics, management, weather, and soil data.
Why it matters
Accurate yield predictions help growers and the processing sweet corn industry make planning decisions.
What the AI did
Trained several machine learning models on 67 variables from a 1992-2018 US dataset; random forest performed best and identified influential yield variables.
Results
Random forest achieved the lowest RMSE (3.29 Mt/ha) and the highest Pearson's correlation among trained models.
Impact
Evidence supports improved field-level yield prediction on historic US sweet corn data.
Record
| Venue | Precision Agriculture |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Daljeet S. Dhaliwal, Martin Williams |
| Citations | 60 (OpenAlex, at import time) |
| References | 39 |
| Model or method | Random forest (best of several ML models) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s11119-023-10057-1 |
| Open access | hybrid |
| Keywords | sweet corn, yield prediction, random forest, machine learning, precision agriculture |
Importance rating
4
real but narrower advance
Abstract scopes the problem to field-level yield prediction for processing sweet corn in two US production regions; real but narrow agricultural advance.
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