Journal of Scientific and Industrial Research

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Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System


Affiliations
1 GIS Cell, MNNIT Prayagraj, Allahabad, Uttar Pardesh, India
2 ABES Engineering College, Ghaziabad, Uttar Pradesh, India
3 Shiv Nadar University, Greater Noida, India, 4Amity University, Noi, India
4 Amity University, Noida, India
5 G B Pant Engineering College, New Delhi, India
6 Maharaja Surajmal Institute of Technology, New Delhi, India

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.
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  • Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

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Authors

Rati Shukla
GIS Cell, MNNIT Prayagraj, Allahabad, Uttar Pardesh, India
Gaurav Dubey
ABES Engineering College, Ghaziabad, Uttar Pradesh, India
Pooja Malik
Shiv Nadar University, Greater Noida, India, 4Amity University, Noi, India
Nidhi Sindhwani
Amity University, Noida, India
Rohit Anand
G B Pant Engineering College, New Delhi, India
Aman Dahiya
Maharaja Surajmal Institute of Technology, New Delhi, India
Vikash Yadav
ABES Engineering College, Ghaziabad, Uttar Pradesh, 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.