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

小麦真菌性病害造成巨大的产量损失，威胁粮食安全。危害最大的是影响小麦叶片的五种病害：黑斑病(STB)、黄锈病(YR)、褐锈病(BR)、白粉病(PM)和褐斑病(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，这两种方法经常失败。农药可能会对环境和人类健康造成负面影响。因此，种植者需要减少杀菌剂的使用，以支持强调文化/遗传控制的病虫害综合管理(IPM)。为了实现这一目标，种植制度需要进行局部调整，这就需要全面的疾病监测。然而，目前的国家监测[例如，英国环境、食品和农村事务部(DEFRA)]由ADAS进行的调查使用视觉评分，这限制了在特定预算下抽样的田地数量，并限制了准确性/再现性。这些因素限制了数据的实用性。这名学生将开发一种新的方法，使用数字成像(DI)和人工智能(AI)来测量这五种疾病。该方法在精度和速度上都将超过现有方法。当用于疾病监测时，该方法将使样本量增加，数据质量提高。这将带来更多的信息，使决策者能够更多地了解IPM的吸收及其影响，并促进其采用。目的A.利用DI和AI技术开发一种新的方法来检测小麦叶片上是否存在每种病害。目的B.利用DI和AI技术开发一种新的方法来量化小麦叶片内的每种病害的数量。目的C.开发一个便携式平台来获取田间小麦叶片的高质量图像。目的D.在DEFRA/ADAS调查的子集上测试新方法。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。基于卷积神经网络(CNN)的人工智能技术为这两个问题提供了强有力的解决方案。该学生将基于Python TensorFlow库构建CNN模型。模型将在参考数据集上进行训练，参考数据集包括用其疾病状态标记的叶图像(用于图像分类)，并且具有被注释为对应于健康或患病叶区域的像素(用于图像分割)。学生将获得足够大的参考数据集，并捕捉叶片表型和疾病症状的多样性。主管的实验室提供了大量的STB和YR病叶图像数据集(每个6,000张图像)。学生将使用这些数据集来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，学生将进行重复的田间实验，获取对比的小麦表型和抗病水平。利用这些参考数据集，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，我们将开发一个便携式田间平台(PhenoBox)，它可以快速且非破坏性地捕获叶片图像。PhenoBox由一个塑料盒子组成，用于遮挡周围的光线，顶部将安装一个带光源的相机，底部将安装一个狭缝来插入树叶。学生将测试PhenoBox的原型以实现工作配置(目标C)。我们将PhenoBox与训练有素的CNN模型结合在一起，并作为DEFRA/ADAS调查的一部分测试系统。在ADAS的田野计分器的同时，这名学生将在农民田地的一个子集(满分为300分)中捕获大量的树叶图像。学生将使用训练过的CNN模型来测量五种疾病，确定最佳样本大小，并评估与现有方法相比的精度增益(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).