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Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

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Title Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System
 
Creator Shukla, Rati
Dubey, Gaurav
Malik, Pooja
Sindhwani, Nidhi
Anand, Rohit
Dahiya, Aman
Yadav, Vikash
 
Subject Feature extraction
Image segmentation
Internet of things
Unmanned aerial vehicles
 
Description 699-706
The crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making
comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and
machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the
same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its
data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned
aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore,
IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is
capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The
various machine learning is also applied to test the performance of our system and compared with the existing disease
detection methods.
 
Date 2021-09-01T11:03:17Z
2021-09-01T11:03:17Z
2021-08
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/57982
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(08) [August 2021]