Classification of Endophyte Types Based on Plant Morphological Characteristics Using Feature Selection and Machine Learning

Classification of Endophyte Types Based on Plant Morphological Characteristics Using Feature Selection and Machine Learning

• By Muhammad Iqbal, Riski Annisa, Lisnawanty, Siti Nurdiani, Muhammad Rezki, Muhammad Ifan Rifani Ihsan;
• 55
• 146-151

Endophytic microorganisms play a crucial role in plant health and agricultural productivity, making accurate identification essential for crop management. This study develops a machine learning-based classification system to identify endophyte types based on plant morphological characteristics using feature selection and machine learning algorithms. The controlled experimental dataset consists of 100 plant samples with morphological parameters including plant height, stem circumference, number of leaves, and pathological indicators. Four machine learning algorithms were evaluated: Random Forest, Support Vector Machine (SVM), XGBoost, and Neural Network, combined with Principal Component Analysis (PCA) for feature selection optimization. Results show that Random Forest achieved the highest performance with an accuracy of 0.80, followed by XGBoost with an accuracy of 0.70, while SVM and Neural Network achieved an accuracy of (0.60). Feature importance analysis revealed that plant height (0.2066) and leaf yellowing (0.2059) were the most discriminative characteristics for endophyte detection, with the top four features contributing 0.7362 of the total predictive power. This study demonstrates the effectiveness of machine learning algorithms in biological classification and proves that plant morphological characteristics can distinguish endophyte colonization patterns. The findings contribute to precision agriculture by enabling early detection of endophytes based on observable plant phenotypes.

• Katakunci Endophyte classification, machine learning, fea- ture selection, plant morphology, Random Forest
• URL https://ieeexplore.ieee.org/document/11385431
• Penerbit IEEE (Institute Of Electrical and Electronical Engineering,

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