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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

A comparative analysis of optimized CNN models using bio-inspired algorithms

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pp. 531–540Vol. 46Issue 2March 2025DOI: 10.47974/JIOS-1957XML
Received:
13 Nov 2024
Published Online:
17 Mar 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1957
Pages:
531–540

Abstract

Nitrogen is a macronutrient that is responsible for crop development. Accurate nitrogen prediction improves crop productivity. This study aims to enhance nitrogen prediction in wheat crops using convolutional neural networks (CNN) with bio-inspired algorithms. CNN, which is widely used for image classification, relies on carefully chosen parameters such as the number of layers, learning rate and kernel sizes to perform effectively. These parameters were optimized using five bio-inspired algorithms. Optimization algorithms aid in selecting the parameters of CNNs to make them more accurate and efficient. The algorithms are genetic algorithms grey wolf optimizer, particle swarm optimization, ant bee colony, and evolutionary algorithms. These findings indicate that employing bio-inspired optimization visibly increases the performance of CNN. The genetic algorithm achieved the best accuracy, making the model more accurate. Overall, these methods helped CNN model predict the nitrogen levels better. This methodology provides essential insights into precision farming, enabling the creation of more efficient nutrient management plans.

Keywords

Subject Classifications

65k10

References

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