Diseases of the betel plant are a hindrance to healthy production which causes severe economic losses and is a major threat to the growing betel leaf industry. This paper introduces an efficient recognition approach based on deep neural networks to diagnose betel plant diseases rapidly to ensure the quality safety and healthy development of the betel leaf industry. A dataset of 10,662 betel leaf images is used for the generalization of deep learning models via the transfer learning technique. EfficientNet B5 has acquired 99.62% training, 98.84% recognition accuracy with 80 epochs. AlexNet, VGG16, ResNet50, Inception V3, and EfficientNet B0 have achieved 81.09%, 83.51%, 86.62%, 94.08%, and 97.29% test accuracy, respectively. During the training phase, AlexNet has taken less time compared to others and misclassified 195 images where EfficientNet B5 misclassified 12 images of the test set. The experimental results validate that the introduced architecture can accurately identify betel plant diseases.