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)


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.


Internet of things (IoT) Random Forest Regression Machine learning 


  1. 1.
    Gundappa AT, Shukla PK (2016) Prediction of mango thrips using thermal indices. GERF Bull Biosc 7(1):17–20Google Scholar
  2. 2.
    Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016:1–11Google Scholar
  3. 3.
    Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Singh BK, Singh S, Yadav SM (2014) Current scenario of production, area and some important post harvest disease of mango and their management in India: an overview. Asian J Plant Sci 13:46–50MathSciNetCrossRefGoogle Scholar
  6. 6.
    Kodali RK, Sahu A (2016) An IoT based weather information prototype using WeMos. In: 2016 2nd International conference on contemporary computing and informatics (IC3I). Noida, pp 612–616Google Scholar
  7. 7.
    Flores KO, Butaslac IM, Gonzales JEM, Dumlao SMG, Reyes RS (2016) Precision agriculture monitoring system using wireless sensor network and Raspberry Pi local server. In: 2016 IEEE region 10 conference (TENCON), pp 3018–3021. IEEE, November 2016Google Scholar
  8. 8.
  9. 9.
    Jenkins GM, Alavi AS (1981) Some aspects of modelling and forecasting multivariate time series. J Time Ser Anal 2(1):1–47MathSciNetCrossRefGoogle Scholar
  10. 10.
    Mercadier M, Lardy JP (2019) Credit spread approximation and improvement using random forest regression. Eur J Oper Res 277:351–365Google Scholar
  11. 11.
    Li Y, Zou C, Berecibar M, Nanini-Maury E, Chan JCW, van den Bossche P, Van Mierlo J, Omar N (2018) Random forest regression for online capacity estimation of lithium-ion batteries. Appl Energy 232:197–210CrossRefGoogle Scholar
  12. 12.
    Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181MathSciNetzbMATHGoogle Scholar

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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