用鲸鱼和粒子群算法优化CNN超参数，提升土壤图像分类精度
Optimizing Hyper parameters in CNN for Soil Classification using PSO and Whale Optimization Algorithm
- 用鲸鱼优化和粒子群算法搜索CNN最佳超参数
- 模型在土壤分类任务中达到高准确率与F1值
- 适合农业、环境监测领域研究者参考
土壤图像分类有助于改善土地管理、提高农业产量并解决环境问题。理解土壤质量可降低风险、提升性能并支持科学决策，对农业、土木工程和自然资源管理等领域至关重要。本文构建基于卷积神经网络（CNN）的智能分类模型，并利用机器学习算法优化其性能。为提升CNN在土壤类型多分类中的表现，采用鲸鱼优化算法（Whale Optimization Algorithm, WOA）和粒子群优化算法（Particle Swarm Optimization, PSO）自动搜索最优超参数，并对比两种算法在分类任务中的效果。通过准确率（Accuracy）和F1分数评估系统性能，实验结果表明该方法能有效提升土壤图像分类效率与准确性。
原文摘要 · Abstract (English)
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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