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Related Concept Videos

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A critical systematic review on spectral-based soil nutrient prediction using machine learning.

Shagun Jain1, Divyashikha Sethia2, Kailash Chandra Tiwari3

  • 1Department of Software Engineering, Delhi Technological University, Delhi, India. shagunjain191172@gmail.com.

Environmental Monitoring and Assessment
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Summary

Artificial Intelligence (AI) enhances soil nutrient prediction for sustainable agriculture. Machine learning and deep learning models, using spectral data, improve farm productivity and environmental health, aiding the goal of zero hunger.

Keywords:
Deep learningHyperspectralMachine learningSoil nutrientsSustainable agriculture

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Computer Science

Background:

  • Intensive agriculture degrades soil quality, impacting crop yields and environmental sustainability.
  • Accurate soil nutrient analysis is crucial for optimizing agricultural practices and achieving global food security goals.
  • Artificial Intelligence (AI) offers advanced solutions for crop yield estimation and soil nutrition management.

Purpose of the Study:

  • To review the application of machine learning (ML) and deep learning (DL) in predicting soil nutrients.
  • To assess the effectiveness of hyperspectral and multispectral sensors in soil nutrient identification.
  • To provide insights into AI techniques for optimizing soil nutrition management and supporting sustainable agriculture.

Main Methods:

  • Systematic literature review of 155 papers (2014-2024) on AI for soil nutrient prediction.
  • Analysis of hyperspectral and multispectral sensor data for spectral analysis and nutrient identification.
  • Evaluation of feature selection techniques and spectral indices for improved prediction accuracy.

Main Results:

  • Machine learning and deep learning models show significant potential in predicting soil nutrients using spectral data.
  • Hyperspectral and multispectral sensors enable precise nutrient identification through multi-band spectral analysis.
  • Feature selection and spectral indices enhance the accuracy of AI-driven soil nutrient prediction models.

Conclusions:

  • AI techniques, particularly ML and DL, are highly effective for optimizing soil nutrition management.
  • The integration of advanced sensors and data analysis methods can significantly improve agricultural productivity and sustainability.
  • This review provides a foundation for future research and policy to advance sustainable agriculture and achieve zero hunger goals.