Algorithms and Inference of Grammars and Natural Computing Models

当植物育种者创造新品种时，他们检查成千上万的植物，寻找理想或不理想的生长，以及压力或疾病的证据。传统上，这是通过人工检查来完成的，这形成了一个瓶颈，可以通过自动图像分析来缓解。机器学习可用于检测各种组件及其形式;通常从已经识别这些属性的大量图像开始，并使用它们来训练将输入映射到输出的模型。人工神经网络通常用于此，但众所周知，从它们中提取科学知识是困难的。形式文法是一种数学形式主义，它具有重写字符串的规则，这些规则从初始符号开始重复应用以创建新字符串。Lindenmayer系统（L系统）是一种语法系统，被创建用于模拟存在于许多具有内在自相似性的生物有机体（如植物）中的多细胞结构。L系统已被广泛用于创建发育中的植物的逼真的视觉模拟。第一个目标是使用L系统（而不是例如神经网络）来从发展中的植物图像的序列预测植物成分和几何形状。将使用为捕获一系列表型（例如物种）而创建的现有L系统作为起点。图像处理将被用作每个图像上的植物成分的初始预测。然后，将创建软件以找到最接近于初始预测的L系统模拟。该模拟将形成新的预测，该预测必须与植物的发育程序一致（与独立分析的图像的预测相反）。这将检测到一些组件，即使它们完全隐藏在图像中。准确性将与其他预测模型进行比较。虽然这种预测不需要注释数据，但它确实需要L个系统，这也是目前难以创建的，特别是对于每个感兴趣的品种。因此，第二个目标是构建算法和软件，从数据（图像或它们的描述）中自动学习L系统。L系统可以可选地具有与重写规则相关联的概率，重写规则用于计算模拟发生的概率。我们将使用第一个物镜的L系统顶部的图像数据创建用于计算的软件。接下来，将构建软件来从数据中学习规则本身。 预期的成果是创造新的方法和软件来使用和学习L系统，以加强表型分析和改善作物的管道。习得的语法和概率直接描述了发展，因此，科学原理可以被提取和更好地理解。这些都是在加拿大和世界范围内人口不断增长和环境急剧变化的情况下增加粮食安全的关键。

When plant breeders create new varieties, they inspect thousands of plants looking for desirable or undesirable growth, and evidence of stress or disease. This has traditionally been done by manual inspection which forms a bottleneck that can be alleviated with automatic image analysis. Machine learning can be used to detect various components and their form; it is common to start with a large set of images with these properties already identified, and use them to train a model that maps inputs to outputs. Artificial neural networks are often used for this, but it is notoriously difficult to extract scientific knowledge from them. Formal grammars are a mathematical formalism with rules for rewriting strings that are repeatedly applied, starting from an initial symbol to create new strings. Lindenmayer systems (L systems) are a type of grammar system that were created to model multicellular structures present in many biological organisms with inherent self-similarity, such as plants. L systems have been widely used to create realistic visual simulations of developing plants. The first objective is to use L systems (and not e.g. neural networks) for predicting plant components and geometry from sequences of developing plant images. An existing L system created to capture a range of phenotypes (e.g. a species) will be used as a starting point. Image processing will be used as an initial prediction of plant components on each image. Then, software will be created to find the L system simulation that most closely matches the initial prediction. This simulation will form a new prediction that must be consistent with the developmental program of the plant (in contrast to predictions from images analyzed independently). This will detect some components even if they are completely hidden in an image. The accuracy will be compared to other predictive models. While this prediction does not require annotated data, it does require L systems which are also currently difficult to create, especially for each variety of interest. Hence, the second objective is to build algorithms and software to learn L systems automatically from data (images or descriptions of them). L systems can optionally have probabilities associated with rewriting rules which are used to calculate the probability of a simulation occurring. We will create software for their calculation using image data on top of the L system from the first objective. Next, software will be built to learn the rules themselves from data. The anticipated outcomes are the creation of new methods and software to use and learn L systems towards strengthening the pipeline for phenotyping and improving crops. Learned grammars and probabilities directly describe development and hence, scientific principles can be extracted and better understood. These are both critical towards increasing food security amidst a growing population and dramatic environmental changes within Canada and worldwide.