Most plant diseases show visible symptoms, and the technique which is accepted today is that an experienced plant pathologist diagnoses the disease through optical observation of infected plant leaves. The fact that the disease diagnosis process is slow to perform manually and another fact that the success of the diagnosis is proportional to the pathologist's capabilities makes this problem an excellent application area for computer-aided diagnostic systems. Instead of classical machine learning methods, in which manual feature extraction should be flawless to achieve successful results, there is a need for a model that does not need pre-processing and can perform a successful classification. In this study, EfficientNet deep learning architecture was proposed in plant leaf disease classification and the performance of this model was compared with other state-of-the-art deep learning models. The PlantVillage dataset was used to train models. All the models were trained with original and augmented datasets having 55,448 and 61,486 images, respectively. EfficientNet architecture and other deep learning models were trained using transfer learning approach. In the transfer learning, all layers of the models were set to be trainable. The results obtained in the test dataset showed that B5 and B4 models of EfficientNet architecture achieved the highest values compared to other deep learning models in original and augmented datasets with 99.91% and 99.97% respectively for accuracy and 98.42% and 99.39% respectively for precision.

大多数植物病害都表现出明显的症状，当今公认的技术是有经验的植物病理学家通过光学观察感染的植物叶子来诊断病害。疾病诊断过程手动执行较慢的事实以及诊断成功与病理学家的能力成正比的事实，使得该问题成为计算机辅助诊断系统的出色应用领域。代替经典的机器学习方法（在该方法中，应准确无误地进行手动特征提取才能获得成功的结果），需要一种不需要预处理并且可以执行成功分类的模型。在这个研究中，在植物叶病分类中提出了EfficientNet深度学习架构，并将该模型的性能与其他最新的深度学习模型进行了比较。PlantVillage数据集用于训练模型。所有模型都分别使用具有55,448和61,486图像的原始和增强数据集进行了训练。EfficientNet体系结构和其他深度学习模型使用转移学习方法进行了培训。在转移学习中，模型的所有层都设置为可训练的。在测试数据集中获得的结果表明，与原始和增强数据集中的其他深度学习模型相比，EfficientNet架构的B5和B4模型获得了最高值，其准确度分别为99.91％和99.97％，准确度分别为98.42％和99.39％。