PDF(Pubmed)
Abstract：
Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in-depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.
摘要：
植物病原体可以使农作物大量死亡，并使物种的本地种植无利可图。在极端情况下，这导致了饥荒和经济崩溃。治疗作物病害的时机至关重要,使用计算机视觉进行精确的疾病检测和农药施用时间正越来越受欢迎。计算机视觉可以降低劳动力成本，防止疾病的误诊，防止误用杀虫剂。农药误用既要耗费资金，又会加剧对农药的抗药性和污染。这里,本文综述了计算机视觉和机器学习方法在植物病害检测中的应用和发展。这篇综述超出了以前的工作范围，讨论了将计算机视觉应用于植物病理学时的重要技术概念和注意事项。我们提出了新的案例研究，以适应标准的计算机视觉方法和审查技术来获取训练数据，使用生物学的诊断工具，和信息特征的检查。除了对卷积神经网络(CNN)和变压器的深入讨论之外，我们还强调了支持向量机和进化神经网络等方法的优势。我们讨论精心策划训练数据的好处，并考虑计算成本较低的技术是有利的情况。这包括流行的模型体系结构的比较和它们的实现指南。
