Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

Abstract

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.

How to Choose a Good Marker to Analyze the Olive Germplasm (Olea europaea L.) and Derived Products

“…High resolution imagery for analysis of olive canopy traits [38] Morphological descriptors remain the basis for a first varietal identification, but their dependence on plant development stages, cultivation techniques and other environmental factors has led to their progressive use in combination with DNA analysis techniques, which have become essential in the evaluation of distribution and extension of genetic diversity in olive crops. Recently, thanks to the advantages of modern technologies, is possible to re-evaluate morphological charactersby phenomics approaches, which allow a more reliable scoring, a reduced influence of human observation with less time and money consumed [43,44]. Today, morphological markers resolved through automatic platforms have become particularly important in mapping population analyses, association studies and functional genetics approaches.…”

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity

“…In an affordable phenotyping approach [58,59], images of fruits were acquired using a RGB flatbed scanner Canon LIDE 400 (Fig. 1A).…”

Current Status of Biodiversity Assessment and Conservation of Wild Olive (Olea europaea L. subsp. europaea var. sylvestris)

“…Recently, Khouatmiani et al [57] assessed the effect of geo-climatic conditions on pollen traits of eight Algerian oleaster populations, founding a correlation between latitude, longitude, and temperature of the site of collection and shape and size of the pollen. Although there are some limitations, morphological descriptors also remain important today for a first olive identification thanks to modern technologies, which allow us to morphologically characterize olive plants through reliable and low-cost automated platforms that significantly reduce the influence of human observation [59,60].…”