Table 1.
Some related studies to the proposed work.
Fig 1.
The smart agriculture system’s IoT architecture.
Fig 2.
The general framework for plant disease classification approach.
Fig 3.
The main data flow diagram for the proposed framework and required tasks.
Fig 4.
The Use case diagram for the implemented mobile application.
Fig 5.
The main block diagram of the suggested framework.
Table 2.
Used technologies and tools.
Table 3.
The trained dataset for 38 different classes of plant leaves.
Table 4.
The performance evaluation for different deep learning depicted approaches.
Fig 6.
Confusion matrix for 38-class plant disease classification based on the proposed ResNet-50-InceptionV3 model.
Table 5.
Statistical test results.
Fig 7.
The Roc for the training process of the proposed ResNet 50 with the InceptionV3 algorithm.
Fig 8.
The Roc for the training process of the proposed MobileNet-1 algorithm.
Fig 9.
The Roc for the training process of the proposed MobileNet-2 algorithm.
Fig 10.
The Roc for the training process of the CNN algorithm.
Fig 11.
The implemented prototype for the proposed integrated IoT system.