Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/57982
Title: Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System
Authors: Shukla, Rati
Dubey, Gaurav
Malik, Pooja
Sindhwani, Nidhi
Anand, Rohit
Dahiya, Aman
Yadav, Vikash
Keywords: Feature extraction;Image segmentation;Internet of things;Unmanned aerial vehicles
Issue Date: Aug-2021
Publisher: NIScPR-CSIR, India
Abstract: 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.
Page(s): 699-706
URI: http://nopr.niscair.res.in/handle/123456789/57982
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.80(08) [August 2021

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