Identifying Iris Species from Their Leaves Using a Decision Tree

Shweta Saraswat¹* and Monica Lamba^2
1Asst. Prof. Department of Artificial Intelligence and Data Science, Arya Institute of Engineering and Technology, Jaipur, Rajasthan, India
²Asso. Professor Department of Computer Science and Engineering, Arya Institute of Engineering and Technology, Jaipur, Rajasthan, India
*Corresponding Author: Shweta Saraswat, Asst. Prof. Department of Artificial Intelligence and Data Science, Arya Institute of Engineering and Technology, Jaipur, Rajasthan, India.

Published: June 06, 2024

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Abstract  

Botanical studies and environmental monitoring rely heavily on accurate species identification, including the Iris family. In this research, we present a unique method for employing a Decision Tree classifier to speed up the process of recognizing Iris plant species from their leaves alone. The information included measurements of sepal and petal length and breadth, among other characteristics of Iris leaves. We cleaned up the data, performed feature selection, and separated it into training and test sets as part of the preprocessing. The model was then trained using the training data and a Decision Tree method. During testing, the trained model showed impressive accuracy in distinguishing Iris species. Based on our findings, it seems that the Decision Tree technique may accurately classify Iris species by leaf characteristics. This automated method shows promise for assisting conservation efforts in many habitats and improving species identification in botanical research.Other study might entail incorporating this method into mobile apps for in-the-field, real-time species identification using other machine learning approaches.

Keywords: Decision tree classifier; iris leaf features; plant species identification

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