Crop Yield Prediction by Modified Convolutional Neural Network and Geographical Indexes

Authors

  • Tiwari P UIT, RGPV, Bhopal, India
  • Shukla P UIT, RGPV, Bhopal, India

DOI:

https://doi.org/10.26438/ijcse/v6i8.503513

Keywords:

Crop yield prediction, Data mining, machine learning, Vegetation Index

Abstract

Agriculture is the main sector of employment in India. One of the major causes for the continuing downfall in agricultural trends is cultivation of crops that are not suitable with the environmental factors like soil and weather conditions. A recommendation system can provide suggestions for a crop that can be cultivated based on spatial conditions. The research focus on to build a recommendation system that can collect raw data for environmental factors like NDVI, SPI parameters from satellite images. The collected data then will be forwarded where this data is processed. In this paper modified convolutional neural network was proposed which takes spatial features as input and trained by back propogation, this reduce error of prediction as well. Experiment was done real dataset from authentic geo-spatial resources. Results are compared with some previous existing methods and it was obtained that proposed modified CNN model was better on various evaluation parameters

References

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Published

2025-11-15
CITATION
DOI: 10.26438/ijcse/v6i8.503513
Published: 2025-11-15

How to Cite

[1]
P. Tiwari and P. Shukla, “Crop Yield Prediction by Modified Convolutional Neural Network and Geographical Indexes”, Int. J. Comp. Sci. Eng., vol. 6, no. 8, pp. 503–515, Nov. 2025.

Issue

Vol. 6 No. 8 (2018): IJCSE August Edition

Section

Research Article

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