Optimizing Hyper parameters in CNN for Soil Classification using PSO and Whale Optimization Algorithm

• URL: http://arxiv.org/abs/2508.16660v1
• Date: Wed, 20 Aug 2025 16:30:19 GMT
• Title: Optimizing Hyper parameters in CNN for Soil Classification using PSO and Whale Optimization Algorithm
• Authors: Yasir Nooruldeen Ibrahim, Fawziya Mahmood Ramo, Mahmood Siddeeq Qadir, Muna Jaffer Al-Shamdeen,
• Abstract summary: Classifying soil images contributes to better land management, increased agricultural output, and practical solutions for environmental issues.<n>In this study, an intelligent model was constructed using Convolutional Neural Networks to classify soil kinds.<n>Swarm algorithms were employed to obtain the best performance by choosing Hyper parameters for the Convolutional Neural Networks network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Classifying soil images contributes to better land management, increased agricultural output, and practical solutions for environmental issues. The development of various disciplines, particularly agriculture, civil engineering, and natural resource management, is aided by understanding of soil quality since it helps with risk reduction, performance improvement, and sound decision-making . Artificial intelligence has recently been used in a number of different fields. In this study, an intelligent model was constructed using Convolutional Neural Networks to classify soil kinds, and machine learning algorithms were used to enhance the performance of soil classification . To achieve better implementation and performance of the Convolutional Neural Networks algorithm and obtain valuable results for the process of classifying soil type images, swarm algorithms were employed to obtain the best performance by choosing Hyper parameters for the Convolutional Neural Networks network using the Whale optimization algorithm and the Particle swarm optimization algorithm, and comparing the results of using the two algorithms in the process of multiple classification of soil types. The Accuracy and F1 measures were adopted to test the system, and the results of the proposed work were efficient result

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