Comparison of UAV Photogrammetry and 3D Modeling Techniques with Other Currently Used Methods for Estimation of the Tree Row Volume of a Super-High-Density Olive Orchard

Abstract

A comparison of three different methods to evaluate the tree row volume (TRV) of a super-high-density olive orchard is presented in this article. The purpose was to validate the suitability of unmanned aerial vehicle (UAV) photogrammetry and 3D modeling techniques with respect to manual and traditional methods of TRV detection. The use of UAV photogrammetry can reduce the amount of estimated biomass and, therefore, reduce the volume of pesticides to be used in the field by means of more accurate prescription maps. The presented comparison of methodologies was performed on an adult super-high-density olive orchard, planted with a density of 1660 trees per hectare. The first method (TRV1) was based on close-range photogrammetry from UAVs, the second (TRV2) was based on manual in situ measurements, and the third (TRV3) was based on a formula from the literature. The comparisons of TRV2-TRV1 and TRV3-TRV1 showed an average value of the difference equal to +13% (max: +65%; min: −11%) and +24% (max: +58%; min: +5%), respectively. The results show that the TRV1 method has high accuracy in predicting TRV with minor working time expenditure, and the only limitation is that professionally skilled personnel is required.

Plant phenotyping relevance

UAVフォトグラメトリと3Dモデリングによるオリーブ樹列体積（TRV）推定法を、手作業・従来法と比較検証しており、植物形態・バイオマス関連形質の取得手法が中心である。

Code and data availability

The article describes UAV photogrammetry (Pix4Dmapper), manual measurements, and Excel-based analysis for tree row volume estimation, but contains no data availability statement, no public dataset or image deposit, and no author code or model release. The only URL present is the standard CC BY license notice, which is

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