This project's main aim is to determine the level of edge-based processing required to reliably detect the presence and germination of fungal spores a

该项目的主要目的是确定可靠地检测真菌孢子的存在和发芽所需的基于边缘的处理水平，并确定可以在云上完成的这种处理的份额，以便系统总体上是健壮和可靠的。为了做到这一点，首先将使用连接到嵌入式计算机（树莓派4）的低成本相机对孢子进行成像，相机的像素大小为1.12微米，理论上应该足以清晰地成像孢子（30微米）及其胚芽管（5-6微米）。这里提出的另一个问题是，相机系统是否足以清晰地成像所需的物体，从而允许进一步可靠的处理，以及需要什么水平的图像处理才能产生足够质量的图像。要确定这个问题的答案，首先要获取包含所需检测对象的图像数据集（例如，在这种情况下，它是孢子本身以及带有可见胚管的发芽孢子）。这将需要在实验室建造一个摄像机，并在受控条件下进行孢子萌发试验，以使我们能够确定摄像机下孢子萌发所需的条件。一旦发芽率是可靠的，那么数据集可以建立在许多发芽试验和技术，如数据增强实施，以确保数据集中有高水平的变化。如果将机器学习技术用于最终的检测/分类任务，这将防止过度拟合等问题。在此之后的下一个任务是使用数据集来训练一个模型来检测孢子和它们的胚管，有多种方法可以做到这一点，但是最有可能评估性能的方法有两种，这些方法是传统的机器学习/深度学习和支持向量机等技术与偏最小二乘回归等聚类技术相结合。这些方法有很大的不同，因此在边缘上应该有明显的性能差异，这提供了非常有用的比较，并深入了解每种方法的性能和处理成本。这将允许选择最合适的选项部署到低功耗设备上进行测试。在此之后的阶段将主要是关于优化和解决边缘设备和云之间处理分裂的问题。

This project's main aim is to determine the level of edge-based processing required to reliably detect the presence and germination of fungal spores and determine the share of this processing that can be done on the cloud so that the system is robust and reliable overall. To do this, first the spores will be imaged using a low-cost camera connected to an embedded computer (Raspberry Pi 4), the pixel size of the camera is 1.12 micrometres which should be theoretically sufficient to clearly image both the spores (30 micrometres) and their germ tubes (5-6 micrometres). Another question posed here is, is the camera system adequate to image the desired objects clearly enough to allow for reliable processing further down the pipeline and what level of image processing is required to produce an image of sufficient quality. The approach that will be taken to determine an answer for this will be to first acquire a dataset of images containing the desired objects to be detected (e.g., in this case it is the spores themselves as well as germinated spores with visible germ tubes). This will require a camera rig to be constructed in the lab and spore germination trials to be run in controlled conditions to allow us to ascertain the required conditions for spore germination under the camera. Once the germination rates are reliable then the dataset can be built over many germination trials and techniques such as data augmentation implemented to ensure that there is a high level of variation in the dataset. This will prevent issues such as overfitting if machine learning techniques are used for the final detection/classification task.The next task after this is to use the dataset to train a model to detect the spores and their germ tubes, there are multiple ways this could be done, however two approaches will most likely be evaluated for performance, these being traditional machine learning/deep learning and a technique such as a support vector machine coupled with a clustering technique such as partial least squares regression. These approaches are vastly different and as a result there should be a noticeable difference in performance at the edge, providing a very useful comparison and an insight into the performance and processing cost of each approach. This will allow for the most suitable option to be selected to be deployed onto a low power device for testing. The stage after this will mostly be about optimization and addressing the question of the split in processing between the edge device and the cloud.