An efficient RGB-UAV-based platform for field almond tree phenotyping: 3-D architecture and flowering traits

Abstract

BackgroundAlmond is an emerging crop due to the health benefits of almond consumption including nutritional, anti-inflammatory, and hypocholesterolaemia properties. Traditional almond producers were concentrated in California, Australia, and Mediterranean countries. However, almond is currently present in more than 50 countries due to breeding programs have modernized almond orchards by developing new varieties with improved traits related to late flowering (to reduce the risk of damage caused by late frosts) and tree architecture. Almond tree architecture and flowering are acquired and evaluated through intensive field labour for breeders. Flowering detection has traditionally been a very challenging objective. To our knowledge, there is no published information about monitoring of the tree flowering dynamics of a crop at the field scale by using color information from photogrammetric 3D point clouds and OBIA. As an alternative, a procedure based on the generation of colored photogrammetric point clouds using a low cost (RGB) camera on-board an unmanned aerial vehicle (UAV), and an semi-automatic object based image analysis (OBIA) algorithm was created for monitoring the flower density and flowering period of every almond tree in the framework of two almond phenotypic trials with different planting dates.ResultsOur method was useful for detecting the phenotypic variability of every almond variety by mapping and quantifying every tree height and volume as well as the flowering dynamics and flower density. There was a high level of agreement among the tree height, flower density, and blooming calendar derived from our procedure on both fields with the ones created from on-ground measured data. Some of the almond varieties showed a significant linear fit between its crown volume and their yield.ConclusionsOur findings could help breeders and researchers to reduce the gap between phenomics and genomics by generating accurate almond tree information in an efficient, non-destructive, and inexpensive way. The method described is also useful for data mining to select the most promising accessions, making it possible to assess specific multi-criteria ranking varieties, which are one of the main tools for breeders.

Status of Phenological Research Using Sentinel-2 Data: A Review

“…Several studies have used spectral mixture analyses to identify photosynthetic and non-photosynthetic fractions [76][77][78] and vegetation indices to estimate concentration of plant pigments [79][80][81]. However, similar efforts are lacking to discriminate flowers from other parts of the plant and existing techniques have been applied mostly to high-resolution aerial or hyperspectral data [82][83][84][85][86]. Notably, the EBI developed by Chen et al (2019) [73] was found to perform poorly in discriminating non-white flowers, and it may be that other band combinations are more suitable for different coloured petals.…”

Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits

“…Photogrammetry-based 3D modeling is often carried out using UAV-based platforms for remote sensing and is widely used in field-scale crop phenotyping ( de Castro et al. 2019 , López-Granados et al. 2019 ).…”

Remote Sensing Energy Balance Model for the Assessment of Crop Evapotranspiration and Water Status in an Almond Rootstock Collection

“…Such studies are rarely performed in woody crops. To the best of our knowledge, only Virlet et al (2014) ; Ampatzidis et al (2019) ; Coupel-Ledru et al (2019) ; Gutiérrez-Gordillo et al (2020) , and López-Granados et al (2019) have used RS imagery for FBP in woody crops such as apple, citrus and almond.…”