Deep learning plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of deep learning within this research domain, there remains a notable absence of an empirical study to offer insightful comparisons due to the employment of varied datasets in the evaluation. Furthermore, a majority of these approaches tend to address the problem as a singular prediction task, overlooking the multifaceted nature of predicting various aspects of plant species and disease types. Lastly, there is an evident need for a more profound consideration of the semantic relationships that underlie plant species and disease types. In this paper, we start our study by surveying current deep learning approaches for plant identification and disease classification. We categorise the approaches into multi-model, multi-label, multi-output, and multi-task, in which different backbone CNNs can be employed. Furthermore, based on the survey of existing approaches in plant pathology and the study of available approaches in machine learning, we propose a new model named Generalised Stacking Multi-output CNN (GSMo-CNN). To investigate the effectiveness of different backbone CNNs and learning approaches, we conduct an intensive experiment on three benchmark datasets Plant Village, Plant Leaves, and PlantDoc. The experimental results demonstrate that InceptionV3 can be a good choice for a backbone CNN as its performance is better than AlexNet, VGG16, ResNet101, EfficientNet, MobileNet, and a custom CNN developed by us. Interestingly, empirical results support the hypothesis that using a single model can be comparable or better than using two models. Finally, we show that the proposed GSMo-CNN achieves state-of-the-art performance on three benchmark datasets.

翻译：深度学习在现代农业中发挥着重要作用，尤其是利用叶片图像的植物病理学领域，卷积神经网络（CNN）正受到广泛关注。尽管已有诸多综述探讨了深度学习方法在该研究领域的应用，但由于评估中使用了不同数据集，目前仍缺乏能够提供深入比较的实证研究。此外，现有方法大多将问题视为单一预测任务，忽视了预测植物种类和病害类型多方面特性的复杂性。最后，对植物种类与病害类型之间的语义关系进行更深入考量显然十分必要。本文从调查当前用于植物识别与病害分类的深度学习方法入手，将其分为多模型、多标签、多输出和多任务四类，每类均可采用不同的骨干CNN。基于对现有植物病理学方法和机器学习可用方法的研究，我们提出了一种名为"广义堆叠多输出CNN"（GSMo-CNN）的新模型。为探究不同骨干CNN和学习方法的有效性，我们在Plant Village、Plant Leaves和PlantDoc三个基准数据集上进行了密集实验。结果表明，InceptionV3作为骨干CNN具有良好性能，其表现优于AlexNet、VGG16、ResNet101、EfficientNet、MobileNet以及我们自定义的CNN。有趣的是，实证结果支持"使用单一模型可与使用双模型相媲美甚至更优"的假设。最终，我们证明所提出的GSMo-CNN在三个基准数据集上均取得了最先进的性能。