Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu
Problem
Predicting paddy yield in the Cauvery Delta Zone, Tamil Nadu, is difficult because yields depend on interacting genotype, environmental and management factors; statistical methods were inaccurate.
Why it matters
Accurate yield forecasts help farmers and planners make water, pricing and food-supply decisions.
What the AI did
Demonstrated support vector regression, GRNN, RBFNN and BPNN machine learning models to predict paddy yield for the region.
Results
ML methods 'demonstrated to predict the paddy yield accurately'; no numeric accuracy stated in source.
Impact
Abstract claims accurate regional yield prediction, but provides no figures to verify the claim.
Record
| Venue | Agronomy |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | S. Vinson Joshua, A. Selwin Mich Priyadharson, Raju Kannadasan |
| Citations | 57 (OpenAlex, at import time) |
| References | 42 |
| Model or method | SVR, GRNN, RBFNN, BPNN |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/agronomy11102068 |
| Open access | gold |
| Keywords | paddy yield prediction, machine learning, neural networks, Cauvery Delta Zone, agriculture |
Importance rating
real but narrower advance
Regional paddy yield prediction for one zone; could aid farmer and planner decisions, but abstract gives no scale of affected farmers or quantified benefit.