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Related Experiment Video

Updated: Jun 5, 2025

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Performance enhancement in hydroponic and soil compound prediction by deep learning techniques.

Mustufa Haider Abidi1, Sanjay Chintakindi2, Ateekh Ur Rehman2

  • 1Advanced Manufacturing Institute, King Saud University, Riyadh, Saudi Arabia.

Peerj. Computer Science
|December 13, 2024
PubMed
Summary

This study introduces a deep learning model to predict soil and hydroponic compound dynamics during plant growth, enhancing agricultural decision-making for sustainable crop production. The innovative approach improves understanding of plant-environment interactions and aids in managing soil pollution risks.

Keywords:
Hydroponic and soil compound predictionIteration-assisted enhanced mother optimization algorithmMulti-scale feature fusion-based convolution autoencoder with gated recurrent unitPlant growthWeighted features

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Computer Science

Background:

  • Soil quality is vital for crop nutrition and yield, with composition influencing crop selection and weed management.
  • Soil pollution by emerging contaminants poses significant risks to water resources and food production.
  • Accurate modeling of chemical transport and reactions in soil and plants is crucial for effective mitigation strategies.

Purpose of the Study:

  • To develop an innovative deep learning approach for predicting hydroponic and soil compound dynamics during plant growth.
  • To overcome limitations of traditional numerical models in describing complex plant-soil interactions.
  • To enhance agricultural decision-making for sustainable and efficient crop production.

Main Methods:

  • Data acquisition from online resources followed by a feature extraction phase.
  • Optimal weight determination for features using the Iteration-assisted Enhanced Mother Optimization Algorithm (IEMOA).
  • Hydroponic and soil compound prediction using a Multi-Scale feature fusion-based Convolution Autoencoder with a Gated Recurrent Unit (MS-CAGRU) network.

Main Results:

  • Successful prediction of hydroponic and soil compound dynamics.
  • Extraction of weighted features, deep belief network (DBN) features, and original features.
  • Demonstrated efficacy of the proposed model through performance evaluation against conventional methods.

Conclusions:

  • The developed deep learning model effectively predicts soil and hydroponic compound dynamics.
  • This approach enhances the understanding of plant-environment interactions and aids sustainable agriculture.
  • The model offers a promising tool for managing soil pollution and improving crop production efficiency.