3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism

Abstract

Effective control of the parasitic weed sunflower broomrape (Orobanche cumana Wallr.) can be achieved by herbicides application in early parasitism stages. However, the growing environmental concerns associated with herbicide treatments have motivated the adoption of precise chemical control approaches that detect and treat infested areas exclusively. The main challenge in developing such control practices for O. cumana lies in the fact that most of its life-cycle occurs in the soil sub-surface and by the time shoots emerge and become observable, the damage to the crop is irreversible. This paper approaches early O. cumana detection by hypothesizing that its parasitism already impacts the host plant morphology at the sub-soil surface developmental stage. To validate this hypothesis, O. cumana- infested sunflower and non-infested control plants were grown in pots and imaged weekly over 45-day period. Three-dimensional plant models were reconstructed using image-based multi-view stereo followed by derivation of their morphological parameters, down to the organ-level. Among the parameters estimated, height and first internode length were the earliest definitive indicators of infection. Furthermore, the detection timing of both parameters was early enough for herbicide post-emergence application. Considering the fact that 3-D morphological modeling is nondestructive, is based on commercially available RGB sensors and can be used under natural illumination; this approach holds potential contribution for site specific pre-emergence managements of parasitic weeds and as a phenotyping tool in O. cumana resistant sunflower breeding projects.

Are root parasitic broomrapes still a good target for bioherbicide control?

“…Combining field history data between growing seasons and infestation patch‐mapping using GIS technology could increase the precision of parasitic weed control under site‐specific weed management. In addition, hyperspectral cameras, and specifically near‐infrared reflectance spectroscopy, 37 may aid the early detection of broomrape‐infested patches in the field, based on the assumption that transpiration rates, which affect leaf temperature, may vary between infected and non‐infected hosts. Additionally, the rapid development of internet applications for farmers and virtual communities allows data transfer between farmers, as well as between regions and countries.…”

Image analysis for the automatic phenotyping of Orobanche cumana tubercles on sunflower roots

“…Therefore, phenotyping with RhizOSun and phenotyping in pots/fields are complementary approaches. Other phenotyping tools mentioned previously rely on indirect measurements of O. cumana development through hyperspectral measures (red/far-red measures [ 21 ], blue green fluorescence and thermal imaging [ 22 ], far-near infrared and shortwave infrared reflectance values [ 23 ]) as well as plant height and first internode length measured by 3D imaging [ 24 ]. Although these tools have the potential to be used for the detection of Orobanche infestation in fields cultivated with homogeneous hybrid lines, they are not optimized for large screenings of resistant genotypes, as they require twice as many pots for each genotype to compare inoculated plants from control noninoculated plants.…”

“…Other approaches for phenotyping the interaction have been proposed, relying on indirect measures of broomrape attack, through leaf chlorophyll or fluorescence measures. Hyperspectral measurements such as red/far-red measures [ 21 ], blue green fluorescence and thermal imaging [ 22 ], far-near infrared and shortwave infrared reflectance values [ 23 ], and plant height and first internode length measured by 3D imaging [ 24 ] have been shown to discriminate between broomrape-infected plants and healthy plants. This discrimination appeared from two weeks [ 21 , 22 ] to 31 days after inoculation [ 23 ] and 750 growing degree days [ 24 ].…”

“…Hyperspectral measurements such as red/far-red measures [ 21 ], blue green fluorescence and thermal imaging [ 22 ], far-near infrared and shortwave infrared reflectance values [ 23 ], and plant height and first internode length measured by 3D imaging [ 24 ] have been shown to discriminate between broomrape-infected plants and healthy plants. This discrimination appeared from two weeks [ 21 , 22 ] to 31 days after inoculation [ 23 ] and 750 growing degree days [ 24 ]. However, these approaches relying on indirect measurements of infestation do not provide information on the stage of interaction affected in the case of resistance and could be affected by the vegetative architecture and growth of sunflowers depending on the genotype.…”

Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation

“…In ornamental plants, digital image technology has been used for gerbera flower classification ( Lino et al, 2011 ), rose shape analysis ( Miao et al, 2006 ), bedding plant species quality assessment ( Parsons et al, 2009 ), flower color pattern determination in Primula sieboldii E. Morren ( Yoshioka et al, 2004 ), as well as evaluation of Anthurium ‘Tropical’ postharvest quality ( Guerra Mattos et al, 2020 ). In sunflower, image analysis so far has been used for early detection of broomrape infection ( Cochavi et al, 2017 ; Ortiz-Bustos et al, 2017 ; Lati et al, 2019 ), weed mapping ( López-Granados et al, 2016 ), architecture-based organ segmentation ( Gélard et al, 2016 ) and floral dimension determination ( Sunoj et al, 2018 ). Common for all image analysis studies is that they can either replace the currently used parameters or provide additional characteristics with good discriminating power, determined in an objective and standardized way ( Lootens et al, 2013 ).…”