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Cassava Disease Prediction Using Data Mining

  • Amal AnandEmail author
  • Merin Joseph
  • S. K. Sreelakshmi
  • G. Sreenu
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)

Abstract

Pesticides are used abusively mainly used to control plant diseases and pests, which lead to reduced quality of vegetables and endangering the life of the living beings. So we propose a model that can predict the presence of diseases with the fulfillment of speed and accuracy. And here for the successful predictions it mainly depends on the selected parameters which we use for the prediction. The parameters we use are predictable. Predictable computer applications that predicts diseases under favourable conditions will be of great help to all farmers. Such applications would reduce problems related to plant protection. In order to create this prediction model we need to consider different prediction variables. Prediction is done using the weather variables such as humidity, temperature and soil conditions such as soil type and data that represents specific disease characteristics. Through this model we check the presence of diseases present in Cassava plants. The proposed solution will take current weather details and soil conditions as input and would predict the diseases, if present any, along with some suggestions to overcome or suppress these diseases.

Keywords

RFT Disease prediction WEKA Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Amal Anand
    • 1
    Email author
  • Merin Joseph
    • 1
  • S. K. Sreelakshmi
    • 1
  • G. Sreenu
    • 1
  1. 1.Department of Computer Science and EngineeringMuthoot Institute of Technology and ScienceKochiIndia

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