Soil Nutrient Detection using Genetic Algorithm

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI:10.1109/HNICEM48295.2019.9072689

J. C. Puno, R. Bedruz, Allysa Kate M. Brillantes, R. R. Vicerra, A. Bandala, E. Dadios

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引用次数: 2

Abstract

Genetic Algorithm is the method used in this study in classifying the qualitative level of the soil nutrients. The data set includes images coming from the result of the soil testing. The extracted features were the HSV values and the LAB values color space. Out of the six extracted features from the data set, the B from LAB color space is the most linear so with that, it is the input of genetic algorithm in identifying the qualitative level of the soil nutrients. For the run of the program using python programming language and pyCharm CE as IDE, the values of each parameters follow: the population size is 10, mutation rate is 0.01, the number of cross over points is 2 and the maximum number of generations is 1000. The population’s final best fitness has 98.2609% that proves that Genetic Algorithm is an effective method in classifying the qualitative level of the soil nutrients.

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基于遗传算法的土壤养分检测

遗传算法是本研究中对土壤养分质量水平进行分类的方法。数据集包括来自土壤测试结果的图像。提取的特征为HSV值和LAB值颜色空间。在从数据集中提取的六个特征中，LAB颜色空间中的B是最线性的，因此，它是遗传算法在识别土壤养分定性水平方面的输入。对于使用python编程语言和pyCharm CE作为IDE的程序运行，每个参数的值如下:种群大小为10，突变率为0.01，交叉点数为2，最大代数为1000。种群的最终最佳适应度为98.2609%，证明遗传算法是一种有效的土壤养分质量水平分类方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )

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