Computer Vision for Tea Leaves
Analysis
We performed experiments using VGG and Resnet18 neural networks with transfer learning(TL) and data augmentation(DA) on RGB and gray scale images with a split of 70% training, 15% validation, and 15% test. We used early stopping in all the experiments.
Brainstorming
Led a team of 7 where we exchanged ideas
Each member shared an idea of a dataset and model
We compared options
Selected the most voted project
Computer Vision Model
Transfer learning
Data Augmentation
Modeling and experimentation
Determined the variables and hyperparameters
Planning/drafting the model
Implementing the model
Testing and evaluating the model
Publishing the model
Results
From some global metrics, we found out that the highest recall value was 0.92 for the resnet18(RGB) + DA + TL when using the test dataset.
Also, we discovered that the Alga leaf disease class showed the highest accuracy among the 7 classes for the VGG RGB network whereas the gray light class showed the highest accuracy for the Resnet18(RGB) + DA + TL.
Lastly, we found that 2 classes had the highest accuracy in both models.
Conclusions
In conclusion, the best results were obtained using restnet18 RGB with data augmentation and transfer learning. The hardest challenge I had was to persuade the team of 4 engineers and scientists to get to the goal in such a short period of time.
