This repository includes the final project authored for the "Research Methods" course at Ben-Gurion University of the Negev. The title of this project is TYLCV Disease Identification in Tomato Plant Images using Transfer Learning and InceptionV3. The objective was to create a high performance CNN model for recognizing disease in tomato plants.
In this paper We trained a deep CNN model to identify ill tomato plants using a relatively small dataset of 1000 images labeled by experts. To overcome the limited data challenge, we utilize a pre-trained model and transfer its learned knowledge to our task. This method is referred to as Transfer Learning. We used a model that is based on an Inception(Szegedy et al. 2016) that was pre-trained on the ImageNet dataset (Deng et al. 2009). The InceptionV3 model was adapted and fine tuned to our image classification task. We compared the performances of our model to ResNet50-based model (He et al. 2016) on the dataset. We also conducted a user study to establish the human level performance and to compare it to our model.
Our model outperformed the other model and the user study results.
