Bueckert, R.

Duddu, H.S.

Guo, X.

Ryu, S.

Shirtliffe, S.

Vail, S.

Wang, M.

Zhang, T.

English

eCommons@USASK

Phenotyping canola flowering using UAV-based phenomics

University of Saskatchewan Research Archive

Phenotyping Canola Flowering Using UAV-based PhenomicsTi Zhang, Hema S. Duddu, Menglu Wang, Seungbum Ryu, Sally Vail, Rosalind Bueckert, Xulin Guo, Steve Shirtliffe*Department of Plant SciencesUniversity of Saskatchewan2017 Soils and Crops❖Canada: largest exporter (74% of the export trade)❖Billions to Canadian economy ❖Product of seeds: oil with low saturated fatsCanola (Brassica napus L.)Source: Canola Council of Canada, 2016Introduction❖Canola seed yield- Stand density, pod number/plant, seed weight/pod- Twice flowers than pods- Good indication of potential pod development and seed yield❖Phenotyping flowering manually- Labor-intensive - Destructive- Subjective❖UAV-based Phenotyping methods- Small UAV platforms- Image sensors- Hundreds of high-resolution images for phenotyping cropIntroduction (cont.)Fig. 1a) UAV platform Fig. 1b) Sony RX100 iii Fig. 1c) Multispectral camera❖Novel vs. traditional - Cost effective- Quickly and accurately- Non-invasiveIntroduction (cont.)Fig. 2 Reflectance spectral features of healthy and unhealthy leaves.  Principle of Using UAV-based ImagesPhoto of figure 2: http://agribotix.com/blog/2014/06/10/misconceptions-about-uav-collected-ndvi-imagery-and-the-agribotix-experience-in-ground-truthing-these-images-for-agriculture/Photo of sun: https://clipartfox.comObjective❖To investigate the application of vegetation indices in detecting canola flower intensity➢ Experiment design- Rectangular lattice    design with three rep.➢ Study site-year- AAFC, 2016➢ Factor- 56 genotypesFig. 3 Plot layout at the Agriculture and Agri-Food Canada Research Farm (52.181196ºN, -106.501494ºE) near Saskatoon, SK in 2016Rep. 3Rep. 2Rep. 1Methods and MaterialsAerial imagery data collection❖Platform and sensorsFig.4a) Rotary-wing UAVFig. 4b) Multispectral camera with five bandsFig. 4c) Modified commercial camera with three bandsImage processing pipelineStep 1. Pre-process (Pix4D) (b) Calibration(a) Stitching/mosaicFig. 5a) Stitching individual images for experimental trial; b) radiometric and geometric calibration Step 2. Data extraction (ArcGIS)- Plot segmentation- Vegetation indices- Pixel classification Image processing pipelineFig. 6 Calibrated orthomosaic images Vegetation Indices❖NDYI = Digital yellowness estimationSulik and Long, 2015Ground Reference CollectionFig. 7 Manually count flower number in the field.❖ Collection dates:- July 8, 15, 22, 29- August 5, 12, 19Regression: flower intensity vs. NDYI Flowering intensity vs. NDYIY = 1.95*10-6x + 0.5662P < 0.0001; Adjusted R2 = 0.1670Y = 5.02*10-6x + 0.4615P < 0.0001; Adjusted R2 = 0.3634Y =3.3*10-6x+ 0.5209P < 0.0001; Adjusted R2 = 0.4851Y = 1.98*10-6x + 0.5533P < 0.0001; Adjusted R2 = 0.2993Pixel Classification: threshold methods(b) Calculate NDYI(c) Extract pixels usingthreshold value(a) Calibrated orthomosaic image Y = 31214.1*(1- exp (-0.00003x)P < 0.0001; Adjusted R2 = 0.5047Y = 0.4806x+15756.3P < 0.0001; Adjusted R2 = 0.1881Y = 32124.4*(1- exp (-0.0001x)P < 0.0001; Adjusted R2 = 0.4653Y = 0.7464x – 355.6P < 0.0001; Adjusted R2 = 0.4653Conclusion❖Achievements based on objectives- Vegetation indices/pixel classification in detecting flower❖Take-home message - NDYI works better in peak flowering timing- Threshold methods improved ability of detecting flowerAcknowledgements❖ SupervisorDr. Steve Shirtliffe❖ Committee MembersDr. Sally VailDr. Rosalind  BueckertDr. Xulin GuoDr. Yuguang Bai❖ Agronomy Weed GroupTHANKS

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Phenotyping canola flowering using UAV-based phenomics

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