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Optimal transfer learning based nutrient deficiency classification model in ridge gourd (Luffa acutangula).

Finney Daniel Shadrach1, Gunavathi Kandasamy2, S Neelakandan3

  • 1Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, India.

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|August 29, 2023
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Detecting nutrient deficiencies in ridge gourd (Luffa acutangula) is crucial for farmers. A new method using Ring Toss Game Optimization with Deep Transfer Learning (RTGODTL-NDC) accurately identifies these deficiencies, improving crop management.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Nutrient deficiency in crops like ridge gourd (Luffa acutangula) poses significant challenges to farmers, impacting yield and quality.
  • Accurate and early detection of these deficiencies is vital for effective crop management and sustainable agriculture.

Purpose of the Study:

  • To develop and evaluate a novel method for classifying nutrient deficiencies in ridge gourd plants.
  • To enhance agricultural practices through improved crop health monitoring and targeted interventions.

Main Methods:

  • Implementation of the Ring Toss Game Optimization with Deep Transfer Learning-based Nutrient Deficiency Classification (RTGODTL-NDC) framework.
  • Utilizing Gabor filters for image pre-processing, SqueezeNet model with Adam optimizer for feature extraction, and a deep hybrid learning model for classification.

Main Results:

  • The RTGODTL-NDC method achieved high performance in classifying nutrient deficiencies.
  • Demonstrated accuracy of 97.16% and specificity of 98.29% in identifying nutrient deficits in ridge gourd plants.

Conclusions:

  • The RTGODTL-NDC framework offers a robust solution for accurate nutrient deficiency detection in ridge gourd.
  • This approach has the potential to significantly improve crop management, leading to better agricultural health and resource utilization.