Detecting Sugarcane Crop Yield using Decision Tree Classifier in the District of Muzaffarnagar

International Journal of Engineering and Management Research

View Publication Info
 
 
Field Value
 
Title Detecting Sugarcane Crop Yield using Decision Tree Classifier in the District of Muzaffarnagar
 
Creator Ankit Kumar
Anil Kumar Kapil
 
Subject Crop
Decision Tree
Sugarcane
Muzaffarnagar
 
Description The district of Muzaffarnagar is the highest sugarcane producing district in Uttar Pradesh and therefore is an important industrial district as well. The district is part of Western UP and it shares the problems of the sugar industry elsewhere in the state: unpredictable demands and crop failures. In this context, predicting sugarcane demand and informing its production can turn to be just the key to solve some of the problems the industry faces.
The existing crop forecasting method for the cultivation of sugarcane used in UP relies, to a large degree, on subjective details, centred on the expertise of engineers in the sugar and alcohol field and on information on input demand in the supply chain. The measurement of the utility of the sample detection using NDVI images from the SPOT sensor used in the sensor's determination over the ECMWF model was possible to infer the official productivity data reported in the previously selected municipalities and harvest. Significant features of the municipal productivity of a given village is listed in a decision tree, and out of the combinations of attributes the corresponding municipal productivity is rated as "Normal" on the average urban productivity scale. Using data from the NDVI time-series between 2013 to 2020, we can discern the three classes of productivity in the meanwhile. Findings indicate that productivity in January ranked as less than mean, mean, and more than mean. The findings were more successful for the class Vegetation, the participants of which were permitted to conclude about the pattern of the average federal productivity prior to.
 
Publisher Vandana Publications
 
Date 2021-04-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://www.ijemr.net/ojs/index.php/ojs/article/view/754
10.31033/ijemr.11.2.10
 
Source International Journal of Engineering and Management Research; Vol. 11 No. 2 (2021): April Issue; 75-82
2250-0758
2394-6962
 
Language eng
 
Relation https://www.ijemr.net/ojs/index.php/ojs/article/view/754/836
 
Rights Copyright (c) 2021 International Journal of Engineering and Management Research
https://creativecommons.org/licenses/by-nc-nd/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library