Blog 3: Updates & Final Model
By Cameron Webster, Jillian Green, and Chris Rohlicek

We are graduate students in the Data Science Master’s program at Brown University. As part of our Deep Learning and Special Topics in Data Science course (Data 2040), we worked on a Kaggle Competition:
Cassava Leaf Disease Classification.

Project Overview

The dataset consists of 21,367 images of Cassava crops from a Ugandan survey. Our goal is to correctly classify the images into five classes: Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mottle (CGM), Cassava Mosaic Disease (CMD), and healthy leaf. …


Blog 2: Image Preprocessing and Transfer Learning Models
By Chris Rohlicek, Cameron Webster, and Jillian Green

A Brief Recap

In Blog 1 we discussed the dataset, EDA, and our baseline model. The dataset consists of 21,367 images of Cassava crops. Our goal is to correctly classify the images into five categories: Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mottle (CGM), Cassava Mosaic Disease (CMD), and healthy leaf. During EDA we found that CMD makes up ~61% of the images. We adapted the LeNet5 architecture to create a baseline model, achieving a val_accuracy of 0.5999 …


Blog 1: Exploratory Data Analysis and a Baseline Model
By Jillian Green, Chris Rohlicek, and Cameron Webster

Introduction

Have you ever run into an unidentifiable plant and wondered, is this plant safe? Nowadays, there are new apps like PlantSnap and Leafsnap that can take a picture and provide information about particular species.

By the end of our blog post series, our goal is to decipher the difference between healthy plants and diseased plants using the Cassava Leaf Disease Classification data from Kaggle. Cassava is a crop grown by African smallholder farmers. It is a carbohydrate that can be found in at…

Classy Classifiers

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