The plant disease classification based on using digital images is very challenging. In the last decade, machine learning techniques and plant images classification tools such as deep learning can be used for recognizing, detecting and diagnosing plant diseases. Currently, deep learning technology has been used for plant disease detection and classification. In this paper, an ensemble model of two pre-trained convolutional neural networks (CNNs) namely VGG16 and VGG19 have been developed for the task plant disease diagnosis by classifying the leaves images of healthy and unhealthy. In this context, CNNs are used due to its capability of overcoming the technical problems which are associated with the classification problem of plant diseases. However, CNNs suffer from a great variety of hyperparameters with specific architectures which is considered as a challenge to identify manually the optimal hyperparameters. Therefore, orthogonal learning particle swarm optimization (OLPSO) algorithm is utilized in this paper to optimize a number of these hyperparameters by finding optimal values for these hyperparameters rather than using traditional methods such as the manual trial and error method. In this paper, to prevent CNNs from falling into the local minimum and to train efficiently, an exponentially decaying learning rate (EDLR) schema is used. In this paper, the problem of the imbalanced used dataset has been solved by using random minority oversampling and random majority undersampling methods, and some restrictions in terms of both the number and diversity of samples have been overcome. The obtained results of this work show that the accuracy of the proposed model is very competitive. The experimental results are compared with the performance of other pre-trained CNN models namely InceptionV3 and Xception, whose hyperparameters were selected using a non-evolutionary method. The comparison results demonstrated that the proposed diagnostic approach has achieved higher performance than the other models.

基于使用数字图像的植物病害分类非常具有挑战性。在过去的十年中，机器学习技术和植物图像分类工具（例如深度学习）可用于识别，检测和诊断植物病害。目前，深度学习技术已用于植物病害的检测和分类。本文通过对健康和不健康叶片图像进行分类，开发了两个预训练卷积神经网络（CNN）的集成模型，即VGG16和VGG19，用于任务植物病害诊断。在这种情况下，使用CNN是因为它具有克服与植物病害分类问题相关的技术问题的能力。然而，CNN遭受各种具有特定架构的超参数的困扰，这被认为是手动识别最佳超参数的挑战。因此，本文采用正交学习粒子群算法（OLPSO）来优化这些超参数的数量，而不是使用传统方法（如人工试验和误差法），而是通过找到这些超参数的最优值来对其进行优化。在本文中，为了防止CNN陷入局部最小值并进行有效训练，我们使用了指数衰减学习率（EDLR）模式。本文通过使用随机少数群体过采样和随机多数群体过采样方法解决了不平衡使用数据集的问题，并克服了样本数量和多样性方面的一些限制。这项工作获得的结果表明，所提出模型的准确性非常有竞争力。将实验结果与其他经过预训练的CNN模型InceptionV3和Xception的性能进行比较，它们的超参数是使用非进化方法选择的。比较结果表明，所提出的诊断方法比其他模型具有更高的性能。