Optimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning Modeling Using USDA Datasets
Problem
Predicting crop yields and identifying the underlying factors that cause differences in agricultural output, using USDA data from 2003 to 2013.
Why it matters
Accurate yield prediction supports food security planning and farm-level economic decisions.
What the AI did
Exploratory data analysis of weather and soil data was followed by training and comparing multiple machine learning regression models on USDA data.
Results
not stated in source (abstract truncated before metrics)
Impact
The evidence only supports that multiple ML models were built and compared, with noticeably varying accuracy.
Record
| Venue | Current Agriculture Research Journal |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Ravindra Kumar Yadav, Anita Seth, Naresh Dembla |
| Citations | 10 (OpenAlex, at import time) |
| References | 8 |
| Model or method | Random Forest, Decision Trees, Bagging Regressor, KNN, Gradient Boost, Linear Regression |
| Organization | not stated in source |
| Primary source | https://doi.org/10.12944/carj.12.1.22 |
| Open access | diamond |
| Keywords | crop yield, machine learning, USDA, precision agriculture, regression |
Importance rating
real but narrower advance
Abstract frames yield prediction as precision agriculture supporting output forecasting; food-relevant but abstract gives no scale or deployment evidence, and results are model comparisons only.
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