RiceMultiNet: Hybrid CNN-Based Rice Leaf Disease Detection via Multi-Model Feature Fusion with Explainable AI

The main contributions of this work are summarized as
follows:
1) We create a kind of framework that combines three
things: EfficientNetB1, ResNet50 and MobileNetV2.
This combination helps the framework to work when
it comes to classifying diseases in rice leaves that look
very similar.
2) We try out six pre-trained networks. VGG19, ResNet50,
DenseNet121, MobileNetV2, EfficientNetB0 and EfficientNetB1. To see which one works best. We keep
everything the same like how we prepare the data and
how we train the networks.
3) We also test the trained models using pictures of rice
leaves that were taken in the field. These pictures were
taken in light with different backgrounds and, with
different cameras. This helps us see how well the models
can work in life when the conditions are not perfect.
4) To make the models more understandable we use something called Grad-CAM visualization. This helps us see
which parts of the leaf the model is looking at when it
makes a prediction.