Application of Machine Learning for Disease Detection Tasks in Olive Trees Using Hyperspectral Data

Abstract

Timely and accurate detection of diseases plays a significant role in attaining optimal growing conditions of olive crops. This study evaluated the use of two machine learning algorithms, Random Forest (RF) and XGBoost (XGB), in conjunction with the feature selection methods Recursive Feature Elimination (RFE) and Mutual Information (MI), for detecting stress in olive trees using hyperspectral data. The research was conducted in Halkidiki, Northern Greece, and focused on identifying stress caused by biotic and abiotic factors through the analysis of hyperspectral images. Both the RF and XGB algorithms demonstrated high efficacy in stress classification, achieving roc-auc scores of 0.977 and 0.955, respectively. The study also highlighted the effectiveness of RFE and MI in optimizing the classification process, with RF and XGB requiring a reduced number of hyperspectral features for an optimal performance of 1.00 on both occasions. Key wavelengths indicative of stress were identified in the visible to near-infrared spectrum, suggesting their strong correlation with olive tree stress. These findings contribute to precision agriculture by demonstrating the viability of using machine learning for stress detection in olive trees, and underscores the importance of feature selection in improving classifier performance.