Deep Learning to Accelerate Breeding for Crop Disease Resistance

深度学习是人工智能领域一个新颖、令人兴奋的发展，特别适合解决涉及图像分析的复杂问题。它广泛地模仿人类大脑编写自己的规则来学习如何对环境中的不同物体进行分类的方式。该项目将为学生提供一个绝佳的机会，将这一快速发展的技术应用于当前具有重大实际意义的问题。农业的一个关键问题是如何在没有农药的情况下控制作物病害。植物育种家希望推出能抵抗所有重大疾病的新品种，但目前，这需要训练有素的人员在田间试验中同时评估几种疾病和压力。这是一个缓慢而困难的过程，尤其是因为许多病原体会产生表面相似的斑点和斑点。该项目的目的是开发一个深度学习网络，该网络能够被训练和重新训练，以识别，区分和量化不同的疾病。这将应用于田间情况，以提高育种试验中疾病筛选的准确性和严格性，以选择新的植物品种。

Deep learning is a novel, exciting development in artificial intelligence, particularly suited to solving complex problems involving image analysis. It broadly mimics the way a human brain writes its own rules to learn how to categorise different objects in the environment. This project will give a student an outstanding opportunity to apply this fast-developing technology to a current issue of great practical importance.A key problem in agriculture is how to control crop diseases without pesticides. Plant breeders wish to release new varieties which are resistant to all significant diseases but at present, this requires a highly-trained person to assess several diseases and stresses in a field trial simultaneously. This is slow and difficult, not least because many pathogens produce spots and blotches of a superficially similar appearance.The aim of this project is to develop a deep learning network which is capable of being trained and retrained to identify, distinguish and quantify different diseases. This will be applied in field situations to increase the accuracy and rigour of disease screening in breeding trials to select new plant varieties.