DeepAI AI Chat
Log In Sign Up

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

by   Sandesh Ramesh, et al.

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.


Achieving the time of 1-NN, but the accuracy of k-NN

We propose a simple approach which, given distributed computing resource...

A Grey-box Launch-profile Aware Model for C+L Band Raman Amplification

Based on the physical features of Raman amplification, we propose a thre...

The NN-Stacking: Feature weighted linear stacking through neural networks

Stacking methods improve the prediction performance of regression models...

Development of Crop Yield Estimation Model using Soil and Environmental Parameters

Crop yield is affected by various soil and environmental parameters and ...

Premium Access to Convolutional Neural Networks

Neural Networks (NNs) are today used for all our daily tasks; for instan...

Neural Network Calculator for Designing Trojan Detectors

This work presents a web-based interactive neural network (NN) calculato...

Neural Networks in Evolutionary Dynamic Constrained Optimization: Computational Cost and Benefits

Neural networks (NN) have been recently applied together with evolutiona...