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July 20, 2026Discover Applied SciencesOpen Access

Leveraging nonlinear deep learning models for intelligent crop recommendation in precision agriculture

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Authors

STSwagatika TripathyPRPremansu Sekhara RathDADibya Ranjan Das Adhikary

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Overview

Randomized trial proposes a method for crop selection using deep learning, indicating improved agricultural decision-making.

Key Points

  • This research aims to develop an intelligent crop recommendation system using deep learning to enhance agricultural decision-making.
  • Utilized various deep learning techniques, including ANNs, CNNs, RNNs, LSTMs, and CRNNs for crop categorization.
  • Integrated individual classifier predictions into an ensemble classifier for improved accuracy.
  • Performed testing to compare the accuracy of the ensemble model against individual models.
  • The ensemble model achieved an overall accuracy of 96.95%.
  • Significant improvement in crop recommendation accuracy was observed compared to individual deep learning models.
  • Deep learning systems effectively support crop selection under variable soil and climatic conditions.

Cite This Study

Tripathy et al. (2026) studied this question.

synapsesocial.com/papers/6a5dbab88bd453d3397abc28https://doi.org/10.1007/s42452-026-09248-y
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