Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

The forecast of frost occurrence requires complex decision analysis that uses conditional probabilities. Due to frost events, the production of crops and flowers gets reduced, and we must predict this event to minimize the damages. If the frost prediction results are accurate, then the damage caused...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.6890-6900
Main Authors: Latif, Rana M. Amir, Brahim, Samir Brahim, Saeed, Saqib, Imran, Laiqa Binte, Sadiq, Mazhar, Farhan, Muhammad
Format: Article
Language:English
Subjects:
Agriculture
analysis
Artificial neural networks
CNN
Crop production
Decision analysis
ensemble
Flowers
Frost damage
frost event
Google
Google Play store
measurement
Meteorology
prediction
Production
scraping
Temperature distribution
Temperature sensors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The forecast of frost occurrence requires complex decision analysis that uses conditional probabilities. Due to frost events, the production of crops and flowers gets reduced, and we must predict this event to minimize the damages. If the frost prediction results are accurate, then the damage caused by frost can be reduced. In this paper, an ensemble learning approach is used to detect frost events with Convolutional Neural Network (CNN). We have used this to get more efficient and accurate results. Frost events need to be predicted earlier so that the farmer can take on-time precautionary measures. So, for measurement and analysis of Google Play, we have scrapped a dataset of the Agricultural category from different genres and collected the top 550 application of each category of Agricultural applications with 70 attributes for each category. The prediction of frost events prior few days of an actual frost event with an accuracy of 98.86%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2963590