  In the last decades, the area under cultivation of maize products has
increased because of its essential role in the food cycle for humans,
livestock, and poultry. Moreover, the diseases of plants impact food safety and
can significantly reduce both the quality and quantity of agricultural
products. There are many challenges to accurate and timely diagnosis of the
disease. This research presents a novel scheme based on a deep neural network
to overcome the mentioned challenges. Due to the limited number of data, the
transfer learning technique is employed with the help of two well-known
architectures. In this way, a new effective model is adopted by a combination
of pre-trained MobileNetV2 and Inception Networks due to their effective
performance on object detection problems. The convolution layers of MoblieNetV2
and Inception modules are parallelly arranged as earlier layers to extract
crucial features. In addition, the imbalance problem of classes has been solved
by an augmentation strategy. The proposed scheme has a superior performance
compared to other state-of-the-art models published in recent years. The
accuracy of the model reaches 97%, approximately. In summary, experimental
results prove the method's validity and significant performance in diagnosing
disease in plant leaves.
