CYPUR-NN: Crop Yield Prediction Using Regression and Neural Networks

TL;DR

CYPUR-NN is a system that combines regression and neural networks to accurately predict paddy crop yields from images or data inputs, also detecting unseen diseases, aiding farmers and agriculturists.

Contribution

The paper introduces CYPUR-NN, a novel system integrating regression and neural networks for crop yield prediction and disease detection from images and data inputs.

Findings

High accuracy in paddy yield prediction

Effective disease detection from images

Promising results on stock images

Abstract

Our recent study using historic data of paddy yield and associated conditions include humidity, luminescence, and temperature. By incorporating regression models and neural networks (NN), one can produce highly satisfactory forecasting of paddy yield. Simulations indicate that our model can predict paddy yield with high accuracy while concurrently detecting diseases that may exist and are oblivious to the human eye. Crop Yield Prediction Using Regression and Neural Networks (CYPUR-NN) is developed here as a system that will facilitate agriculturists and farmers to predict yield from a picture or by entering values via a web interface. CYPUR-NN has been tested on stock images and the experimental results are promising.