Monocular Depth Estimation Driven Canopy Segmentation for Enhanced Determination of Vegetation Indices in Olive Grove Monitoring

Abstract

Highlights: What are the main findings? Monocular depth estimation based on the Depth Anything v2 method applied for segmentation and calculation of vegetation indices for trees in an olive grove proved to be superior to other methods. The method used, the time of year when trees are photographed, and the resolution used significantly affect the final results of multispectral measurements. What is the implication of the main finding? When determining vegetation indices in an olive grove, high-resolution sampling (drone) and the application of segmentation methods based on depth estimation are recommended. The seasonality and dependence of the calculated vegetation indices must be taken into account when drawing conclusions about the condition of trees. It is recommended to propose a new index that would take into account all these parameters and be simpler for interpretation by the user. This study investigates the application of unmanned aerial vehicles with multispectral cameras for monitoring the condition of olive groves with high accuracy. Multispectral images of olive groves provided detailed insight into the spectral data required for the analysis of vegetation indices. Using deep learning-based object detection, individual olive trees were identified within the images, which allowed the extraction of parts corresponding to each tree. To separate the background from the canopy, segmentation based on the monocular depth estimation algorithm, Depth Anything, was applied. In this way, elements that are not part of the tree’s crown were removed for more accurate analysis and calculation of the NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) indices. The obtained results were compared with the results obtained for unsegmented patches, threshold-based patches, and manually segmented patches. The comparison and analysis carried out shows that the proposed segmentation approach improved the accuracy of NDVI and NDRE by focusing exclusively on the crowns of the observed trees, excluding the noise of the surrounding vegetation and soil. In addition, measurements were carried out on three observed olive groves at different parts of the vegetation cycle, and the values of the vegetation indices were compared. This integrated method combining drone-based multispectral imaging, deep learning object detection, and advanced segmentation techniques highlights a robust approach to olive tree health monitoring and provides insight into seasonal vegetation dynamics, for winter and spring, to capture differences in vegetative activity.