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Article

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

1
Department of Plant Pathology and Weed Research, Newe Ya’ar Research Center, Agricultural Research Organization, Ramat Yishay 30095, Israel
2
Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1569; https://doi.org/10.3390/s19071569
Received: 12 February 2019 / Revised: 18 March 2019 / Accepted: 28 March 2019 / Published: 1 April 2019
(This article belongs to the Special Issue Agricultural Sensing and Image Analysis)
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. View Full-Text
Keywords: multi-view stereo; plant phenotyping; segmentation multi-view stereo; plant phenotyping; segmentation
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MDPI and ACS Style

Lati, R.N.; Filin, S.; Elnashef, B.; Eizenberg, H. 3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism. Sensors 2019, 19, 1569. https://doi.org/10.3390/s19071569

AMA Style

Lati RN, Filin S, Elnashef B, Eizenberg H. 3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism. Sensors. 2019; 19(7):1569. https://doi.org/10.3390/s19071569

Chicago/Turabian Style

Lati, Ran N.; Filin, Sagi; Elnashef, Bashar; Eizenberg, Hanan. 2019. "3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism" Sensors 19, no. 7: 1569. https://doi.org/10.3390/s19071569

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