Developing machine learning algorithms
We are working with the National Research Council of Canada and the University of Winnipeg to develop image collections of pea plants and their root systems in order to apply machine learning to better differentiate plants and weeds and test the associations between Rhizobium inoculation and pea growth. Rapid phenotyping pipelines will allow pea breeders to select for increased resilience and higher yields faster than previously possible.
Plant phenotyping is essential to the development of new and resilient crop varieties. Selecting resilient genetics is the first line of defence to stabilize yield through changing environments. Our crop varieties must continue to grow with the needs of growers. Constant improvement is required to continually produce tolerant and beneficial traits while improving crop yield, harvestability, quality and value. The move towards high-throughput digital phenotyping through computer vision will help modernize crop development efforts for more efficient and effective selection of traits to benefit Canadian agriculture.
Testing and validating technology solutions
We are establishing a large image collection of peas through crop development capturing shoot biomass in the field and root systems in controlled environments. This will be useful for present and future digital agriculture applications. It will be included in image databases used to train machine learning algorithms so we can move from traditional trait-based measurements to more efficient automated phenotyping pipelines.
Machine learning for plant phenotyping
Traditional methods of phenotyping rely on laborious manual measurements subject to human error. Rapid phenotyping allows breeders to stay ahead of yield-limiting factors and contribute to the success of Western Canadian farms. Our collaboration is working to develop an algorithm that utilizes machine learning to capture high throughput plant phenotyping data. We are collecting images of 12 field pea varieties on Innovation Farms at various growth stages through successive field seasons. Our aim is to use computer vision for estimating phenotypes such as biomass and yield and differentiating between pea growth and weed formation.
Automation of root and nodule assessments
Together, we are developing methods to assess rhizobium root nodules and root system traits. This includes establishing metrics applicable for optimizing pea nitrogen fixation and crop yield. Root images from rhizoboxes at the National Research Council of Canada in Saskatoon will be utilized by University of Winnipeg researchers to develop an automated machine learning pipeline for measuring root and nodule development over time. The research aims to develop tools for the selection of beneficial below-ground traits for new pea variety development and ultimately, contribute to the goal of optimizing and reducing inputs for Canadian farmers.