Crop Prediction Models—A Review

  • Supreeth S. AvadhaniEmail author
  • Aashrith B. Arun
  • Varun Govinda
  • Juyin Shafaq Imtiaz Inamdar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)


Crop yield is directly dependent on climatic and weather conditions. A lot of research has been done studying the dependency of weather on crop yield. Crop prediction models have proven to be successful in increasing the crop yield. Soil parameters and atmospheric parameters are used by the models to predict the suitable crop. Parameters such as type of soil, pH, phosphate, potassium, organic carbon, sulphur, manganese, copper, iron, depth, temperature, rainfall, humidity have shown to influence the yield of crop. In this paper, we review the research conducted by several researchers in this direction with a logical conclusion.


Artificial neural network (ANN) Weather Crop prediction models 



The authors are grateful to Dr. Anitha C, Assistant Professor, Department of Computer Science & Engineering, The National Institute of Engineering, Mysuru for her guidance and helpful comments. The authors also would like to thank the HOD, Department of CSE and the Principal for their continuous support and encouragement.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Supreeth S. Avadhani
    • 1
    Email author
  • Aashrith B. Arun
    • 1
  • Varun Govinda
    • 1
  • Juyin Shafaq Imtiaz Inamdar
    • 1
  1. 1.The National Institute of EngineeringMysuruIndia

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