How does in-row seed spacing and spatial pattern affect canola yield?

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The Canadian canola industry is worth $26.7 billion and sustains 250,000 jobs for Canadians. Canola yield is limited by lower than optimum plant populations, which result from poor crop emergence, irregular seed distribution, and intentionally low seeding rates. Canola plants may at least partially compensate for lower plant densities through increased branching and number of pods. These changes to plant architecture, termed phenotypic plasticity, increase the space occupied, solar radiation captured and seed production per plant. However, highly branched plants take longer to reach full canopy closure, which reduces solar radiation capture and seed yield for the crop overall. Independent of plant density, modifying the inter- and intra-row spacing between canola plants also affects phenotypic plasticity, leading us to hypothesize that arranging plants optimally will lower the plant density required to maximize yield. However the spatial relationships between neighbouring canola plants and the mechanisms that govern them are poorly understood, and we currently lack spatially explicit planting recommendations to exploit this plasticity. The proposed study will examine how the canola canopy develops with varied planting arrangements through field experimentation, with the goal of optimizing solar radiation capture and crop productivity. Deep learning approaches based on convolutional neural networks (CNN) will be developed to quantify the space occupied by individual canola plants over time using imagery collected by a UAV (drone), ground cameras, and 3D scanner. These methods will be applied to study spatial relationships between neighbouring canola plants. Furthermore we will study the canopy light environments that result from different planting arrangements, and apply them in a growth chamber to understand how canola phenotypic plasticity arises with the changing light environment. The outcome of this study will be spatially explicit planting recommendations that take plant distribution into account rather than only seeding density per area. We believe that these recommendations will result in maximum yield with lower seed costs, making canola production more profitable and resulting in greater global food security.