2024 Challenge and Winners

Winners

Congratulations to 2024 Winners!

First Prize Award Winner($5000)Riya Palanki, Texas, United States
Second Prize Award Winner($3000)Samuel Tobbs, California, United States
Third Prize Award Winner($1000)Jack Hudner, Connecticut, United States

2024 US Applied AI Olympiad Challenge: Revolutionizing Agriculture with AI

Challenge Overview

The field of agriculture is undergoing a technological revolution, and artificial intelligence is at the forefront of this transformation. Farmers spend significant time and effort manually identifying and sorting plant seedlings—a crucial but labor-intensive process. Your challenge is to develop a Convolutional Neural Network (CNN) model capable of automating this task by classifying plant seedlings into their respective species.

This competition provides a unique opportunity to use cutting-edge AI and deep learning techniques to address a real-world problem, ultimately contributing to higher crop yields, reduced manual labor, and more sustainable agricultural practices.

Data Description

The dataset in the link, provided by open research initiative focused on AI in agriculture, contains images of plant seedlings across 12 species:

  • Black-grass
  • Charlock
  • Cleavers
  • Common Chickweed
  • Common Wheat
  • Fat Hen
  • Loose Silky-bent
  • Maize
  • Scentless Mayweed
  • Shepherds Purse
  • Small-flowered Cranesbill
  • Sugar Beet

The data is stored in two files:

  • images.npy: A numpy array containing the image data.
  • Labels.csv: A CSV file with the corresponding species labels.

Your objective is to build a classifier that accurately predicts the species of a seedling based on its image.

Deliverables

Participants must submit the following:

Google Colab Notebook:

Content:

  • Preprocessing of the dataset.
  • Exploratory data analysis (EDA).
  • Implementation of a CNN classifier to predict plant species.
  • Evaluation of model performance using metrics like accuracy and confusion matrix.
  • Insights drawn from the data and model results.

Submission Format: The notebook must be submitted as an HTML file (.html).

Business Presentation:

Audience: Tailor your presentation for a Data Science Lead in the agricultural industry.

Content:

  • Business Overview: Clearly define the problem and explain your solution approach.
  • Key Findings: Highlight insights derived from the data and model results that drive business decisions.
  • Business Recommendations: Provide actionable steps to improve agricultural efficiency using your model.
  • Potential Benefits: Explain how the solution can lead to higher yields, reduced manual labor, and sustainable practices.

Submission Format: The presentation must be submitted as a PDF file (.pdf). Avoid copying code into the slides unless necessary to illustrate key points.

Submission Guidelines

  • Challenge Opens: Nov 15, 2024
  • Submission Deadline: Nov 30, 2024

Submit your deliverables through the competition portal. Ensure that all files are error-free and adhere to the submission formats.

Evaluation Criteria

Submissions will be judged on the following:

  • Model Accuracy: How effectively your CNN model classifies the plant species.
  • Insights and Analysis: The depth and clarity of insights derived from the data.
  • Business Recommendations: Relevance, feasibility, and impact of your proposed actions.
  • Presentation Quality: Engaging and clear storytelling tailored to a business audience.

Best Practices for Success

  • Use Google Colab’s GPU runtime for faster training.
  • Document your notebook with inline comments and markdown cells for better readability.
  • Ensure the notebook runs sequentially without warnings or errors.
  • Keep your presentation concise, with a focus on business impact and actionable insights.

Why Participate?

  • Gain hands-on experience with real-world datasets and deep learning techniques.
  • Develop and showcase your data science and business communication skills.
  • Be part of the growing movement to integrate AI into agriculture and solve global challenges.

Join us in transforming agriculture with AI—your innovations today can shape a sustainable tomorrow!