The automatic recognition of plant diseases is of crucial importance for the current development of agriculture. Fast and efficient identification can greatly reduce the natural, economic, and human resource loss caused to agricultural practitioners. Deep neural networks allow computers to learn plant disease detection in an end-to-end manner, thereby obtaining better results and higher efficiency. While Convolutional Neural Network (CNN) models have become a well-established tool for detecting plant diseases, the lack of robustness of the models due to environmental variations remains to be a critical concern. Recent research into overcoming this challenge includes domain adaptation (DA) algorithms like classic Domain-Adversarial Neural Network (DANN) or the innovative Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification (MSUN). However, the topic remains under-explored as the newly developed methods were not tested on many crop species and diseases. This research focuses on four deep CNN models (MobileNet, VGG, GoogLenet, and ResNet). The models are developed and tested using the New Plant Diseases dataset on Kaggle, which comprises 70,000+ training images (offline-augmented) and 17,000+ validation images encompassing 38 different classes of healthy and diseased plant leaves. The models would be cross-evaluated upon their accuracy and training speed, as well as their change in performance after optimization and applying DA methods. With an uppermost accuracy of 86.4% in test dataset from the wild, results show that Transfer Learning, Model Ensemble as well as Domain Adaptation works effectively to increase the robustness of models which will ultimately benefit farmers in detecting plant diseases and deciding on the best treatment in real-time.

中文翻译：

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使用深度卷积神经网络进行叶病检测

植物病害的自动识别对于当前农业的发展至关重要。快速高效的识别可以大大减少给农业从业者造成的自然、经济和人力资源损失。深度神经网络让计算机能够以端到端的方式学习植物病害检测，从而获得更好的结果和更高的效率。虽然卷积神经网络（CNN）模型已成为检测植物病害的成熟工具，但由于环境变化而导致模型缺乏鲁棒性仍然是一个关键问题。最近克服这一挑战的研究包括域适应 (DA) 算法，如经典的域对抗神经网络 (DANN) 或具有跨物种植物病害分类不确定性正则化的创新多表示子域适应网络 (MSUN)。然而，由于新开发的方法尚未对许多作物品种和疾病进行测试，因此该主题仍未得到充分探索。这项研究重点关注四种深度 CNN 模型（MobileNet、VGG、GoogLenet 和 ResNet）。这些模型是使用 Kaggle 上的新植物病害数据集开发和测试的，该数据集包含 70,000 多张训练图像（离线增强）和 17,000 多张验证图像，涵盖 38 种不同类别的健康和患病植物叶子。这些模型将根据其准确性和训练速度以及优化和应用 DA 方法后的性能变化进行交叉评估。野外测试数据集的最高准确度为 86.4%，结果表明，迁移学习、模型集成以及领域适应可以有效提高模型的稳健性，最终有利于农民检测植物病害并决定最佳治疗方法实时。