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Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points
Year:
2013
Source of publication :
precision agriculture (source )
Authors :
Eizenberg, Hanan
;
.
Volume :
14
Co-Authors:
Lati, R.N., Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Technion City, 32000 Haifa, Israel
Filin, S., Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Technion City, 32000 Haifa, Israel
Eizenberg, H., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Ramat Yishay, Israel
Facilitators :
From page:
586
To page:
605
(
Total pages:
20
)
Abstract:
Crop canopy spatial parameters are indicative of plant phenological growth stage and physiological condition, and their estimation is therefore of great interest for modeling and precision agriculture practices. Rapid increases in computing power have made stereovision models an attractive alternative to common single-image-based 2D methods, by allowing detailed estimation of the plant's growth parameters regardless of imaging conditions. Models that have been proposed thus far are still limited in their application because of sensitivity to outdoor illumination conditions and the inherent difficulty in modeling complex plant shapes using only radiometric information. Assuming that not all of the plant-related pixels are essential for growth estimation, this study proposes a 3D reconstruction model that focuses on selected salient features on the plant surface, which are sufficient for obtaining growth characteristics. In addition, by introducing a hue-invariant model, the proposed algorithm shows robustness to diverse outdoor illumination conditions. The algorithm was tested under greenhouse and field conditions on corn, cotton, sunflower, tomato and black nightshade plants, from young seedlings to fully developed plant growth stages, and accurately estimated height (error ~4.5 %) and leaf cover area (error ~5 %). Furthermore, a strong correlation (r2 ~0.92) was found between the plant's estimated volume and measured biomass, yielding an accurate biomass estimator in the validation tests (error ~4.5 %). This estimation ability remained stable while applying the model on plants with varying densities (overlapping leaves) and imaging setups where the standard 2D based analyses failed, thus showing the 3D modeling contribution to robust growth estimation models. © 2013 Springer Science+Business Media New York.
Note:
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More details
DOI :
10.1007/s11119-013-9317-6
Article number:
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
27749
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:33
Scientific Publication
Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points
14
Lati, R.N., Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Technion City, 32000 Haifa, Israel
Filin, S., Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Technion City, 32000 Haifa, Israel
Eizenberg, H., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Ramat Yishay, Israel
Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points
Crop canopy spatial parameters are indicative of plant phenological growth stage and physiological condition, and their estimation is therefore of great interest for modeling and precision agriculture practices. Rapid increases in computing power have made stereovision models an attractive alternative to common single-image-based 2D methods, by allowing detailed estimation of the plant's growth parameters regardless of imaging conditions. Models that have been proposed thus far are still limited in their application because of sensitivity to outdoor illumination conditions and the inherent difficulty in modeling complex plant shapes using only radiometric information. Assuming that not all of the plant-related pixels are essential for growth estimation, this study proposes a 3D reconstruction model that focuses on selected salient features on the plant surface, which are sufficient for obtaining growth characteristics. In addition, by introducing a hue-invariant model, the proposed algorithm shows robustness to diverse outdoor illumination conditions. The algorithm was tested under greenhouse and field conditions on corn, cotton, sunflower, tomato and black nightshade plants, from young seedlings to fully developed plant growth stages, and accurately estimated height (error ~4.5 %) and leaf cover area (error ~5 %). Furthermore, a strong correlation (r2 ~0.92) was found between the plant's estimated volume and measured biomass, yielding an accurate biomass estimator in the validation tests (error ~4.5 %). This estimation ability remained stable while applying the model on plants with varying densities (overlapping leaves) and imaging setups where the standard 2D based analyses failed, thus showing the 3D modeling contribution to robust growth estimation models. © 2013 Springer Science+Business Media New York.
Scientific Publication
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