Improving the Performance of Leaves Identification by Features Selection with Genetic Algorithms

  • Laura D. Jalili
  • Alfredo Morales
  • Jair CervantesEmail author
  • José S. Ruiz-Castilla
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 657)


The development of vision systems to plant leaves identification from images is a very important current challenge. One of the main lines of research is the improvement in performance. The performance of automatic identification systems is directly linked to the extracted features. The extracted features allow to identify or classify the object. However, sometimes a high number of features introduces noise which affects the performance. In this research, a genetic algorithm is proposed to extract the most discriminative features. The proposed technique reduces the dimensionality of the data set and improves the performance to identifying plants. In the experimental results, the proposed method is compared with feature selection classic techniques. Experimental results show that the proposed technique obtains a significant improvement in the performance in comparison with other techniques.


Genetic Algorithm Feature Selection Textural Feature Classifier Performance Binary String 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was funded by the Ministry of Research of the Autonomous University of the State of Mexico with the research project 3771/2014/CIB.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Laura D. Jalili
    • 1
  • Alfredo Morales
    • 1
  • Jair Cervantes
    • 1
    Email author
  • José S. Ruiz-Castilla
    • 1
  1. 1.Posgrado e InvestigaciónUAEMEX (Autonomous University of Mexico State)TexcocoMexico

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