Assessment of Olive Tree Canopy Characteristics and Yield Forecast Model Using High Resolution UAV Imagery

Abstract

Greek agriculture is mainly based on olive tree cultivation. Farmers have always been concerned about annual olive orchard production. The necessity for the improvement of farming practices initiated the development of new technological tools that are useful in agriculture. The main goal of this study is the utilization of new technologies in order to define the geometry of olive tree configuration, while the development of a forecasting model of annual production in a non-linear olive grove, planted on a hilly uneven terrain is the secondary goal. The field’s orthomosaic, its Digital Terrain Model (DTM) and Digital Surface Model (DSM) were created by employing high resolution multispectral imagery. The Normalized Difference Vegetation Index (NDVI) thematic map has also been developed. The trees’ crowns were isolated employing the field’s orthomosaic, rendering individual polygons for each tree through Object Based Image Analysis (OBIA). The measurements were conducted in a Geographic Information System (GIS) environment and were also verified by ground ones. Tree crown height, surface, and volume were calculated, and thematic maps for each variable were created, allowing for the observation of the spatial distribution for each parameter. The compiled data were statistically analyzed revealing important correlations among different variables. These were employed to produce a model, which would enable production forecasting in kilograms per tree. The spatial distribution of the variables gave noteworthy results due to the similar pattern they followed. Future crop yield optimization, even at a tree level, can be based on the results of the present study. Its conclusions may lead to the development and implementation of precision olive tree cultivation practices.

High-Resolution UAV Imagery for Field Olive (Olea europaea L.) Phenotyping

“…Sola-Guirado et al [51], in an experiment carried out in Greece, developed a multiple linear regression based on NDVI, canopy volume and the average slope of the tree location, yielding an R 2 of 0.6. Although good performance of vegetative indices in discriminating olive cultivars and in yield forecasting was found in our study and in previous ones [49][50][51][52], it is important to pay attention to possible limitations in practical transferring of these techniques. For instance, the ability of vegetative indices in cultivar identification has been tested in experimental fields characterized by homogeneous pedo-climatic conditions and agronomic practices within the orchard [49, this experiment].…”

“…This is a crucial information in phenotyping trials which are mainly aimed at finding low vigor but highly productive olive varieties. Recent studies focused on olive yield forecasting based on geometrical and spectral canopy characteristics [51,52]. Sola-Guirado et al [51], in an experiment carried out in Greece, developed a multiple linear regression based on NDVI, canopy volume and the average slope of the tree location, yielding an R 2 of 0.6.…”

Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard

“…In previous studies aerial images and spectral indices were used to determine the canopy projection in the olive orchard and to derive relationships with soil and tree structures to be applied in site-specific management [30,31]. Recent studies also focused on predicting yield based on geometrical and spectral canopy characteristics [32,33].…”

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

“…Performing automatic monitoring of olive tree growth would be essential in these regions to effectively address these threats. Nowadays, the application of machine learning methods on very high spatial resolution satellite and aerial images opens the possibility of detecting isolated shrubs and trees at regional scale [ 6 , 7 , 8 , 9 , 10 ].…”