Enhanced Crop Yield Prediction with Disease Identification

Authors

  • Obula Srija Reddy  Department of Science and Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • R Naga Samyuktha  Department of Science and Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • Sirikonda Chandu  Department of Science and Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • Dr. Sunil Bhutada  Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India

Keywords:

RNN, LSTM, feedforward neural networks, Decision Tree, yield, factors(state name, district name, season, crop year, area), dataset, and disease.

Abstract

Agriculture is one of the major and most used fields in India. Around 63% of people depend on it. The yield of agriculture is not always predictable for farmers. Because, due to many factors like global warming, the effect is high on the weather which affects rainfall. So, to have the maximum yield, farmers can cultivate the crop based on predictions provided by us. Even though it’s been hundreds of years people in agriculture are still dubious about the results due to external factors other than soil. As said, the external factors like rainfall, seed type, and technical lacking, result in not the best yield. We have observed that there are many sucides happening in India over a few years , the cause behind this is climatic change vulnerabilities in crop production. This assignment proposes farmers to test the data set based on various factors to get a good crop yield .

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-06-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

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Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Obula Srija Reddy, R Naga Samyuktha, Sirikonda Chandu, Dr. Sunil Bhutada "Enhanced Crop Yield Prediction with Disease Identification" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 3, pp.360-363, May-June-2022.