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Disease Prediction of Mango Crop Using Machine Learning and IoT

  • P. B. JawadeEmail author
  • Dattatray Chaugule
  • Devashri Patil
  • Hemendra Shinde
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

India is leading producer and an exporter country of Mango with annual production of approximately 19 tons, accounting for 40% of the world’s total production. The production has been declined in recent years due to damage from pests and other disease-causing agents, in response to find a better solution to this problem the Prediction of Disease of Mango Fruit Crop using Machine Learning and IoT’ is an advanced alerting system. The main objective is to develop a system that can forecast the attack of diseases on Mango fruit crop using past weather data and crop production. The field sensors collected live weather data to calculate disease prediction in real time. The Random Forest Regression model was trained on past weather data and used to calculate disease outbreak probability. The model showed pretty accurate results in relation to the forecasting of the disease.

Keywords

Internet of things (IoT) Random Forest Regression Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • P. B. Jawade
    • 1
    Email author
  • Dattatray Chaugule
    • 1
  • Devashri Patil
    • 1
  • Hemendra Shinde
    • 1
  1. 1.Information Technology DepartmentGovernment College of Engineering KaradKaradIndia

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