Hyperspectral imaging combats yield loses

HYPERSPECTRAL IMAGING COMBATS YIELD LOSSES

The leading canola disease, sclerotinia stem rot, threatens production and causes yield losses to Canadian producers. To combat this fungal disease, and others, Lakeland College and industry partners have enlisted the use of drones and camera technology. The Application of Hyperspectral Imaging for Crop Staging and Yield Prediction in Canola project uses drones and hyperspectral imaging methods to determine the bloom stage of canola fields as well as predictive grain yield quickly and accurately. “We began two experimental trials in spring of 2020 to examine the success of correlating hyperspectral image analysis to canola bloom stage at various degrees and grain yield. The collected data is currently being correlated with the images to develop yield algorithms,” says JP Pettyjohn, Lakeland’s crop research scientist. Trial 1 is testing four seeding dates and targeting four, eight and 12 plants/ft2 in order to ensure a range of plant staging and degree of branching on the date of hyperspectral image capture. The date of hyperspectral imaging will be determined largely by growing season conditions affecting advancement of canola growth stage. Trial 2 determines the success of hyperspectral imaging during the flowering stage to predict canola yield using a yield gradient created by applying six nitrogen rates. Lakeland College, Alberta Innovates, LandView Drones, Thompson Rivers University and Stream Technologies, hope to support the agriculture industry through improved disease management, input use efficiency, and increased canola production, with environmental benefits including reduction of ineffective pesticide applications. LandView is responsible for acquiring hyperspectral images, analyzing the imaging, and correlating groundtruth data to the hyperspectral imaging data. These correlations will support information surrounding the accuracy of hyperspectral imaging assessment of canola bloom stage at varying stages and plant densities, and of canola grain yield. Stream Technologies Inc. is providing analytics on all the various datasets to create AI-powered algorithms for canola staging and yield. Through alignment of the research with regional and provincial industry needs and dissemination of results to agricultural producers and industry, Lakeland will ensure that net research outcomes reflect the primary goal of sustainably enhancing commercial agriculture in the region, in Alberta and in Canada.