Soil nutrient detection using genetic algorithm

• Main Authors: Puno, John Carlo V., Bedruz, Rhen Anjerome R., Brillantes, Allysa Kate M., Vicerra, Ryan Rhay P., Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2019
• Subjects: Soils and animal nutrition; Soils and nutrition; Genetic algorithms; Mechanical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3361; https://animorepository.dlsu.edu.ph/context/faculty_research/article/4363/type/native/viewcontent/HNICEM48295.2019.9072689
• Institution: De La Salle University

© 2019 IEEE. 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.