Odilbekov, Firuz
- Department of Plant Breeding, Swedish University of Agricultural Sciences
Abstract
Phenotyping with proximal sensors allow high-precision measurements of plant traits both in the controlled conditions and in the field. In this work, using machine learning, an integrated analysis was done from the data obtained from spectroradiometer, infrared thermometer, and chlorophyll fluorescence measurements to identify most predictive proxy measurements for studying Septoria tritici blotch (STB) disease of wheat. The random forest (RF) models for chlorosis and necrosis identified photosystem II quantum yield (QY) and vegetative indices (Ms) associated with the biochemical composition of leaves as the top predictive variables for identifying disease symptoms. The RF model for chlorosis was validated with a validation set (R-2: 0.80) and in an independent test set (R-2: 0.55). Based on the results, it can be concluded that the proxy measurements for photosystem II, chlorophyll content, carotenoid, and anthocyanin levels and leaf surface temperature can be successfully used to detect STB. Further validation of these results in the field will enable application of these predictive variables for detection of STB in the field.
Keywords
Septoria tritici blotch; wheat; proximal phenotyping; disease detection; machine learning; random forest; machine learning
Published in
Frontiers in Plant Science
2018, volume: 9, article number: 685
SLU Authors
Armoniené, Rita
- Department of Plant Breeding, Swedish University of Agricultural Sciences
- Department of Plant Breeding, Swedish University of Agricultural Sciences
Chawade, Aakash
- Department of Plant Breeding, Swedish University of Agricultural Sciences
- Department of Plant Breeding, Swedish University of Agricultural Sciences
Associated SLU-program
SLU Plant Protection Network
UKÄ Subject classification
Agricultural Science
Publication identifier
- DOI: https://doi.org/10.3389/fpls.2018.00685
Permanent link to this page (URI)
https://res.slu.se/id/publ/95400