How to improve measurement of major wheat diseases using artificial intelligence?

小麦真菌病害造成重大产量损失，威胁粮食安全。尤其有害的是影响小麦叶片的五种病害：小麦黑斑病（STB）、黄锈病（YR）、褐锈病（BR）、白粉病（PM）和棕斑病（TS）。在英国，杀菌剂和品种抗性是控制它们的主要方法。然而，由于病原体种群的进化适应，这两种措施往往都失败了。农药可能对环境和人类健康产生负面影响。因此，种植者需要减少杀菌剂的使用，转而采用强调文化/遗传控制的病虫害综合治理（IPM）。为了实现这一目标，种植制度需要局部调整，这需要全面的疾病监测。然而，目前由ADAS进行的国家监测[例如英国环境、食品和农村事务部（DEFRA）调查]使用视觉评分，这限制了在给定预算下采样的领域数量，并且限制了准确性/可重复性。这些因素限制了数据的效用。该学生将开发一种利用数字成像（DI）和人工智能（AI）来测量五种疾病的新方法。该方法将在精度和速度上超越现有的方法。当用于疾病监测时，该方法将增加样本量并提高数据质量。这将导致更有信息的疾病监测，使决策者能够更多地了解IPM的吸收情况及其影响，并促进其采用。目的：建立一种利用DI和AI技术检测小麦叶片各病害的新方法。目的b：建立一种利用DI和AI技术量化小麦叶片中每种病害数量的新方法。目的：研制一种高质量田间小麦叶片图像采集便携式平台。目的D.在DEFRA/ADAS调查的亚群中检验新方法。疾病存在的检测是一个图像分类问题，而疾病数量的量化是一个图像分割问题。对于这两个问题，基于卷积神经网络（CNN）的人工智能技术提供了强大的解决方案。学生将基于Python TensorFlow库构建CNN模型。模型将在参考数据集上进行训练，这些参考数据集包括标有疾病状态的叶片图像（用于图像分类），并将像素注释为与健康或患病叶片区域相对应的像素（用于图像分割）。学生将获得足够大的参考数据集，并捕获叶片表型和疾病症状的多样性。在首席导师的实验室中，有大量的STB和yr病叶图像数据集（每个实验室有大约6000张图像）。学生将使用这些数据集创建高质量的参考数据集。为了获得BR、PM和TS的参考数据集，该学生将进行重复的田间实验，以捕获对比小麦表型和抗病水平。使用这些参考数据集，学生将训练CNN模型来测量五种疾病（目标A， B）。为了提高测量速度/物流，我们将开发一种便携式现场平台（PhenoBox），用于快速和非破坏性地捕获叶片图像。PhenoBox由一个塑料盒组成，用于阻挡环境光，顶部安装有光源的照相机，底部有一个狭缝用于插入树叶。学生将测试PhenoBox的原型以实现工作配置（目标C）。我们将把PhenoBox与训练好的CNN模型结合在一起，并作为DEFRA/ADAS调查的一部分对系统进行测试。与ADAS的田间评分者并行，学生将在农民田地的子集（300个中的10个）中捕获大量叶片图像。学生将使用训练好的CNN模型来测量五种疾病，确定最佳样本量，并评估与现有方法相比的准确性增益（目标D）。

Fungal diseases of wheat cause significant yield losses and threaten food security. Especially damaging are the five diseases that affect wheat leaves: septoria tritici blotch (STB), yellow rust (YR), brown rust (BR), powdery mildew (PM), and tan spot (TS). In the UK, fungicides and cultivar resistance are the primary ways to control them. Yet, both measures often fail because of the evolutionary adaptation of pathogen populations. Pesticides may negatively affect the environment and human health. Consequently, growers need to reduce fungicide use in favour of integrated pest management (IPM) that emphasizes cultural/genetic control. To achieve this, cropping systems need local adjustments, which requires comprehensive disease monitoring. However, current national monitoring [e.g. UK Department for Environment, Food and Rural Affairs (DEFRA) surveys conducted by ADAS uses visual scoring, which limits the number of fields sampled at a given budget, and has restricted accuracy/reproducibility. These factors limit the utility of the data.The student will develop a novel method to measure the five diseases using digital imaging (DI) and artificial intelligence (AI). The method will exceed existing methods in accuracy and speed. When used in disease monitoring, the method will enable increased sample sizes and improved data quality. This will lead to a more informative disease monitoring, allowing policy-makers to learn more about IPM uptake, its effects, and boost its adoption.Objective A. Develop a new method to detect the presence of each disease on wheat leaves using DI and AI techniques.Objective B. Develop a new method to quantify the amount of each disease within wheat leaves using DI and AI techniques.Objective C. Develop a portable platform to acquire high-quality images of wheat leaves in the field. Objective D. Test the new method in subsets of DEFRA/ADAS's surveys.Detection of disease presence is an image classification problem, while quantification of the amount of disease is an image segmentation problem. For both problems, AI techniques based on convolution neural networks (CNN) offer powerful solutions. The student will build CNN models based on the Python TensorFlow library. The models will be trained on reference datasets comprising leaf images labelled with their disease status (for image classification) and having pixels annotated as those corresponding to healthy or diseased leaf areas (for image segmentation). The student will acquire reference datasets that are sufficiently large, and capture the diversity of leaf phenotypes and disease symptoms.Extensive datasets of STB- and YR-diseased leaf images are available in lead supervisor's lab (>6,000 images for each). The student will use these datasets to create high-quality reference datasets. To acquire reference datasets for BR, PM and TS, the student will conduct replicated field experiments that capture contrasting wheat phenotypes and disease resistance levels. Using these reference datasets, the student will train CNN models to measure the five diseases (Objectives A, B).To improve measurement speed/logistics, we will develop a portable field platform (PhenoBox) that captures leaf images rapidly and non-destructively. PhenoBox consists of a plastic box to block ambient light, a photocamera with a light source will be mounted at the top and a slit at the bottom to insert the leaves. The student will test PhenoBox's prototypes to achieve a working configuration (Objective C).We will bring together the PhenoBox with the trained CNN models, and test the system as part of DEFRA/ADAS's surveys. In parallel with ADAS's field scorers, the student will capture large numbers of leaf images in a subset of farmers' fields (10 out of 300). The student will use the trained CNN models to measure the five diseases, determine the optimal sample size and evaluate the accuracy gain versus existing methods (Objective D).