Plant disease detection is a critical issue that needs to be focused on for productive agriculture and economy. Detecting plant disease using traditional methods is a tedious job as it requires a tremendous amount of work, time, and expertise. Automatic plant disease detection is an important research area that has recently gained a lot of attention among the academicians, researchers, and practitioners. Machine Learning and Deep Learning can help identify the plant disease at the initial stage as soon as it appears on plant leaves. In this state-of-an-art review, a thorough investigation has been performed to evaluate the possibility of using Machine Learning models to identify plant diseases. In this study, diseases and infections of four types of crops, i.e., Tomato, Rice, Potato, and Apple, are considered. Initially, numerous possible infections and diseases on these four kinds of crops are studied along with their reason for the occurrence and possible symptoms for their detections. An in-depth study of the different steps involved in plant disease detection and classification using Machine Learning and Deep Learning is provided. Various datasets available online for plant disease detection have also been presented. Along with this, a detailed study on various existing Machine Learning and Deep Learning-based classification models proposed by different researchers across the world for four considered crops in terms of their performance evaluations, the dataset used, and the feature extraction method is discussed. At last, various challenges in the use of machine learning and deep learning for plant disease detection and future research directions are enumerated and presented.

植物病害检测是生产性农业和经济需要重点关注的关键问题。使用传统方法检测植物疾病是一项繁琐的工作，因为它需要大量的工作，时间和专业知识。自动植物病害检测是一个重要的研究领域，最近引起了院士，研究人员和从业人员的广泛关注。机器学习和深度学习可以在植物叶子出现后立即帮助其在初始阶段识别植物疾病。在此最新的综述中，已经进行了彻底的调查，以评估使用机器学习模型识别植物病害的可能性。在这项研究中，考虑了四种农作物的疾病和感染，即番茄，水稻，马铃薯和苹果。原来，研究了这四种农作物的许多可能的感染和疾病，以及它们发生的原因和检测的可能症状。提供了对使用机器学习和深度学习进行植物病害检测和分类的不同步骤的深入研究。还介绍了在线可用于植物病害检测的各种数据集。除此之外，还讨论了由世界各地的不同研究人员针对四种考虑的农作物提出的各种现有基于机器学习和深度学习的分类模型，从其性能评估，使用的数据集和特征提取方法方面进行了详细讨论。最后，