Integrating UAV-RGB Spectral Indices by Deep Learning Model Enables High-Precision Olive Tree Segmentation Under Small Sample

Abstract

Accurate maps of olive plantations are very important to monitor and manage the rapid expansion of olive cultivation. Nevertheless, in situations where data samples are limited and the study area is relatively small, the low spatial resolution of satellite imagery poses challenges in accurately distinguishing olive trees from surrounding vegetation. This study presents an automated extraction model for the rapid and accurate identification of olive plantations using unmanned aerial vehicle RGB (UAV-RGB) imagery, multi-index combinations, and deep learning algorithm based on ENVI-Net5. The combined use of Lightness, Normalized Green-Blue Difference Index (NGBDI), and Modified Green-Blue Vegetation Index (MGBVI) indices effectively capture subtle spectral differences between olive trees and surrounding vegetation, enabling more precise classification. Study results indicate that the proposed model minimizes omission and misclassification errors through incorporating ENVI-Net5 and the three spectral indices, especially in differentiating olive trees from other vegetation. Compared to conventional models such as Random Forest (RF) and Support Vector Machine (SVM), the proposed method yields the highest metrics—overall Accuracy (OA) of 0.98, kappa coefficient of 0.96, producer’s accuracy (PA) of 0.95, and user’s accuracy (UA) of 0.92. These values represent an improvement of 7%–8% in OA and 15%–17% in the kappa coefficient over baseline models. Additionally, the study highlights the sensitivity of ENVI-Net5 performance to iterations, underlining the importance of selecting an optimal number of iterations for achieving peak model accuracy. This research provides a valuable technical foundation for the effective monitoring of olive plantations.