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Improving crop production using an agro-deep learning framework in precision agriculture.

J Logeshwaran1, Durgesh Srivastava2, K Sree Kumar3

  • 1Department of Computer Science, Christ University, Bengaluru, Karnataka, 560029, India.

BMC Bioinformatics
|November 2, 2024
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Summary

The Agro Deep Learning Framework (ADLF) uses artificial intelligence to improve crop management in precision agriculture. This AI framework enhances decision-making, leading to better crop yields and reduced losses.

Keywords:
Agricultural technologyAgro deep learning frameworkArtificial intelligence in agricultureCrop monitoringCrop yield predictionData analysisDeep learningNeural networksPrecision agricultureSmart farming

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Area of Science:

  • Agricultural Science
  • Computer Science

Background:

  • Precision agriculture optimizes farming by monitoring and adjusting crop growth factors.
  • Artificial intelligence (AI), specifically deep learning, offers significant benefits to precision agriculture.
  • The Agro Deep Learning Framework (ADLF) was developed to address critical crop cultivation challenges using large datasets.

Purpose of the Study:

  • To enhance precision agriculture effectiveness through deep learning technologies.
  • To process vast datasets including soil moisture, temperature, and humidity for crop behavior prediction.
  • To improve decision-making, enable early detection of crop issues, and boost agricultural productivity.

Main Methods:

  • Development and application of the Agro Deep Learning Framework (ADLF).
  • Utilizing deep learning models to analyze agricultural datasets.
  • Processing variables such as soil moisture, temperature, and humidity.

Main Results:

  • The ADLF achieved 85.41% accuracy, 84.87% precision, 84.24% recall, and an 88.91% F1-Score.
  • Demonstrated strong predictive capabilities for crop management with low error rates.
  • Indicated significant enhancement of decision-making, crop yield, and reduction of agricultural losses.

Conclusions:

  • The ADLF significantly improves precision agriculture by providing insights for crop management.
  • Enables early issue detection, optimized resource use, and improved crop yields.
  • AI-driven agriculture shows potential for revolutionizing farming towards greater efficiency and sustainability.