Convolutional neural network (CNN) models have been used extensively in many image recognition tasks for their state-of-the-art performance in recent years. Researchers inspired by this success frequently prefer CNNs in the agricultural field, especially for disease detection and classification. Many CNN models have been proposed for plant leaf diseases and impressive performance results have been obtained. On the other hand, standard CNNs usually need millions of parameters in the network for computation, but it is difficult to implement them on embedded and mobile devices with limited resources. Therefore, it is important to obtain lighter models by decreasing the number of parameters in addition to the high performance of the models. In this paper, a new hybrid CNN approach based on Inception architecture and depthwise separable convolutions is proposed to reduce the number of parameters and computational cost for plant leaf disease detection and classification. Although the number of parameters is significantly reduced, the results show that the proposed approach has high accuracy performance. The proposed hybrid model has been trained and tested with k-fold cross-validation using a dataset of 50,136 images containing 30 classes from 14 different leaves, including healthy and diseased ones. The new model has achieved the best accuracy of 99.27% and an average accuracy of 99%, and provides about a 75% reduction in the number of parameters compared to the standard CNN.

近年来，卷积神经网络（CNN）模型已被广泛用于许多图像识别任务中。受此成功启发的研究人员经常喜欢在农业领域使用CNN，尤其是在疾病检测和分类方面。已经提出了许多针对植物叶片疾病的CNN模型，并获得了令人印象深刻的性能结果。另一方面，标准的CNN通常需要网络中数百万个参数来进行计算，但是很难在资源有限的嵌入式和移动设备上实现它们。因此，除了模型的高性能外，通过减少参数数量来获得更轻的模型也很重要。在本文中，提出了一种基于Inception体系结构和深度可分离卷积的混合CNN方法，以减少用于植物叶片病害检测和分类的参数数量和计算成本。尽管参数数量大大减少，但结果表明该方法具有较高的精度。提出的混合模型已经过培训和测试使用50,136张图像的数据集进行k倍交叉验证，该图像包含来自14种不同叶片的30类图像，包括健康叶片和患病叶片。新模型实现了99.27％的最佳精度和99％的平均精度，并且与标准CNN相比，参数数量减少了约75％。