Multi-Class Image Classification Model for Tomato Leaf Disease Using TensorFlow Take 2

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Tomato Leaf Disease Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 20,000 images of tomato leaves with ten diseases and one healthy class. The research team collected these images to investigate the possibilities of developing a lightweight model that can predict tomato leaf disease on a mobile app.

ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 96.74% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.96%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Tomato Leaves Dataset

Dataset Reference:

One source of potential performance benchmarks:

The HTML formatted report can be found here on GitHub.