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

加拿大油菜籽产业价值267亿美元，为加拿大人提供了25万个就业机会。油菜籽产量受到低于最佳植株种群的限制，这是由于作物出苗率低、种子分布不规律以及故意降低播种率造成的。油菜籽植株至少可以通过增加分枝和豆荚数量来部分补偿较低的植物密度。植物结构的这些变化被称为表型可塑性，增加了占用的空间，捕获了太阳辐射，并增加了每株的种子产量。然而，高度分枝的植物需要更长的时间才能达到完全闭合的树冠，这会降低对太阳辐射的捕获和作物的种子产量。与种植密度无关，改变油菜植株之间的行间和行内间距也会影响表型可塑性，这导致我们假设最佳的植物布置将降低最大化产量所需的种植密度。然而，人们对邻近油菜籽植物之间的空间关系和控制它们的机制知之甚少，目前我们缺乏空间上明确的种植建议来利用这种可塑性。拟议的研究将通过田间试验，研究不同种植安排下油菜籽树冠的发育情况，目的是优化太阳辐射捕获和作物生产力。基于卷积神经网络(CNN)的深度学习方法将被开发，以使用无人机(无人机)、地面摄像头和3D扫描仪收集的图像来量化单个油菜籽植物随着时间的推移所占据的空间。这些方法将被应用于研究相邻油菜植物之间的空间关系。此外，我们将研究不同种植安排产生的冠层光环境，并将其应用于生长室，以了解油菜表型可塑性如何随着光环境的变化而发生变化。这项研究的结果将是空间上明确的种植建议，考虑到植物的分布，而不仅仅是单位面积的播种密度。我们相信，这些建议将以更低的种子成本实现最高产量，使油菜籽生产更有利可图，并导致更大的全球粮食安全。

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.