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A method for durian precise fertilization based on improved radial basis neural network algorithm.

Ruipeng Tang1, Sun Wei1, Tang Jianxun2

  • 1Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

Frontiers in Plant Science
|June 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) for precision durian fertilization. The IM-RBNNA accurately predicts soil nutrient content and yield, optimizing fertilizer plans for increased harvests and reduced costs.

Keywords:
durian plantingdurian precise fertilizationdurian soil nutrient managementdurian yield predictionprecise nutrient supply

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Area of Science:

  • Agricultural Science
  • Artificial Intelligence
  • Soil Science

Background:

  • Durian cultivation requires precise soil nutrient management for optimal yield.
  • Understanding the relationship between soil nutrients (N, P, K) and durian yield is crucial for effective fertilization strategies.

Purpose of the Study:

  • To develop and evaluate an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) for precision durian fertilization.
  • To enhance the prediction accuracy of soil nutrient content and its correlation with durian yield.

Main Methods:

  • Proposed an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) incorporating the gray wolf algorithm for optimizing weights and thresholds.
  • Collected soil nutrient and historical yield data to train and validate the IM-RBNNA model.
  • Compared the performance of IM-RBNNA against other relevant algorithms.

Main Results:

  • IM-RBNNA demonstrated superior performance over three other algorithms in predicting soil N, K, and P content, evidenced by lower average relative error and average absolute error, and a higher coefficient of determination.
  • The algorithm accurately predicted the complex relationship between soil nutrients and durian yield.

Conclusions:

  • The IM-RBNNA algorithm provides accurate predictions for durian soil nutrient content and yield, aiding farmers in developing effective agronomic plans.
  • Efficient nutrient resource utilization through IM-RBNNA minimizes environmental impact, maximizes durian growth potential, reduces costs, and increases overall yield.