Using Machine Learning and Drone Datasets to Build a New Segmentation Model for Crop Measurement

Using Machine Learning and Drone Datasets to Build a New Segmentation Model for Crop Measurement

This challenge requires experience in Machine Learning, Image Segmentation, and Data Analysis. The project is hosted with UK-based agtech startup Drone AG.

A team of 50 technology change-makers are collaborating from different experience levels and backgrounds.

 

The problem

This project aims at building a new segmentation/clustering model for the project partner, DroneAg, and their flagship product Skippy Scout, which is a mobile application that automates the flight of a drone to capture data used to provide analysis and insights for farmers and agronomists. Segmentation is used on the captured imagery to measure various aspects of the crop that can be tracked and monitored across multiple flights.

 

The project goals

You will help to build a more accurate segmentation or unsupervised machine learning clustering model that works on a variety of crops, listed below, either by creating a series of models or by building/training a model with a more general training set. We may want to identify and group crops by growth stage rather than crop type. 

The models will be built using a common framework, such as TensorFlow, and can easily be retrained with new imagery (we should also consider a model that actively learns) and can be later iterated upon by our inhouse team. 

The models should be built in a modular way so that we can reuse components for other use cases. One example of this is building each component as a lambda function and orchestrating the overall pipeline with step functions, however, we are open to suggestions for this. 

Further development is likely to involve counting or measuring different parts of the image such as leaves, ears/heads, etc. The models will be accessed by our back-end services via a restful API.

The data and process

Dataset 

You will get access to a number of images of each crop type where we store both the crop type and a plant date (that can help determine the growth stage). These images are unlabeled in terms of segmented values. Also, the results of the current model will be shared, which could be used within an initial training set. 

Crops

Cereals (Wheat, Barley, Oats, etc); OSR; Beans, Peas, Sugarbeet, Soybeans; Corn, Maize; Hemp; Linseed/Flax; Broccoli; Celery; Lettuce; Tobacco; Watercress

 

 

Building a Global Company Sustainability Benchmarking System using NLP and Machine Learning

Building a Global Company Sustainability Benchmarking System using NLP and Machine Learning

A global team of 50 AI changemakers are collaborating in this high-impact 2-month challenge to build a sustainability benchmarking system.

This challenge requires experience in NLP and Machine Learning.

 

The context

SustainLab is building an ecosystem of software and AI solutions to help companies become more sustainable. The result of this project, which is related to text mining and text analysis of annual sustainability reports that companies publish globally, will be used to benchmark companies in their industry and globally. The sustainability benchmarking system combined with our software product will be precious for companies. The comparison against competitors is a compelling incentive for companies to set more ambitious goals and take bolder steps towards those goals. As companies are the main contributor to our unsustainable environment, society, and finance, helping them to become more sustainable has a significant impact on our planet and our society.

 

The project goals

A sustainability report is a textual document published by a company about the Environmental, Social, and Governance (ESG) impacts caused by their everyday activities. Most of the time, the companies tend to highlight the positive effects of ESG and may cover up the negative impacts.

This project aims to understand the sustainability work of a company from its textual report and extract valuable data in a structured way that will help in comparing with reports of other companies.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works

 

Bringing Data Science Education to Secondary Schools in India for Zero Cost with Machine Learning

Bringing Data Science Education to Secondary Schools in India for Zero Cost with Machine Learning

Create a base of Indian secondary schools and their email domains to spread the importance of having data science in schools’ curriculum. 

During the duration of the project, you will be granted free DataCamp Donates access for 1 year. Participants must be committed towards finishing the project to keep the full year of DataCamp Donates access.

 

The context

Data science is needed now more than ever. Changes in the ways we live demand changes in the ways we learn. DataCamp for Classrooms allows teachers and students to get free, comprehensive access to over 380 courses on DataCamp on the most popular technologies, like, Python, SQL, and more. It also gives teachers all the tools to manage their students’ assignments and track their progress. Currently, DataCamp for Classrooms is available to university teachers and students worldwide as well as secondary school teachers and students in the US, UK, Belgium, and Poland.

Now, we want to bring DataCamp for Classrooms to secondary schools in India. To equip the next generation with the skills to thrive, DataCamp is partnering with Omdena to collaborate on a list of tech-ready secondary schools in India that we can give access to DataCamp for Classrooms, enabling teachers to integrate world-class data science education into their curriculum and bridge the country’s skill gap.

 

The problem

DataCamp wants to start integrating data science education into the secondary school system of India at zero cost to its teachers and students. As the world’s second-most populous country, India is poised to grow rapidly in innovations that stem from data science. But this potential is going unrealized because young people don’t have access to the data education resources. Bringing DataCamp for Classrooms to India will be an essential stepping stone to fostering a more data-literate world by educating the youth about the importance of data science and how it can be harnessed. This will lead to more young people being prepared for college, tomorrow’s job market, and generally becoming more autonomous in their decisions regarding data from their own lives. DataCamp believes that everyone, regardless of their background, deserves the education they need to thrive in our data-driven world.

The aim is to gather the names and unique email domains of Indian secondary schools through the use of web scraping and data entry in order to have a comprehensive database of schools whose teachers and students are eligible for DataCamp for Classrooms access. DataCamp provides the online learning platform that these students can use to start learning while being guided by their teachers who ensure their progress and development.

 

The project goals

  • Identify and gather all Indian secondary schools to spread the importance of having data science in schools’ curriculum.
  • Collaborate on creating a robust Google Sheet spreadsheet that contains essential information about the secondary schools in India by August 1, 2022. The spreadsheet should present, at minimum, these two essential data into two separate columns:
    • School name
    • Email domain
  • Additionally, the list could include: 
    • Main email contact
    • School website link
    • Address
    • City
    • Province
    • Postcode
    • Telephone number
  • Schools with their own unique email domains should be prioritized in their own list. Information on schools that don’t have their own email domain can be gathered on a separate sheet but are not a high priority.

 

What is DataCamp for Classrooms?

DataCamp for classrooms enables all higher education teachers and students in any part of the globe to receive free renewable 6-months DataCamp access. Additionally, all secondary education teachers and students in the US, UK, Belgium, and Poland can benefit from this free offer.

Find out more on how to receive 6 months of renewable access to your class here.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works

 

Building Conscious AI for Sophia Robot

Building Conscious AI for Sophia Robot

This challenge requires experience in one or more of the following – familiarity with conscious AI research, consciousness, cognitive architecture, AI ethics, and advanced deep learning.

 

The problem

Humanity is facing a lot of challenges due to AI. We can already see AI algorithms suppressing people’s opinions and trying to control us. So to face future challenges, from bad actors (and AI), we have to build compassionate algorithms. Otherwise, we will make intelligent machines that kill, fight and suppress each other.

 

The project goals

The first step of building a compassionate AI is building a conscious AI. There is already much research done on conscious AI and this project will explore different existing approaches and implement the most promising ones. The project also aims to further the research and development with the potential to try findings on the Sophia Robot developed by Hanson Robotics.

 

The project mentor 

If accepted to this project you will join a diverse team of 50 engineers to address this challenge. David Hanson, founder, and Chief Executive Officer will act as a mentor to the team.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works

 

Facilitating Agricultural Sustainability and Financing Possibilities to Local Farmers in Nigeria With Credit Scoring

Facilitating Agricultural Sustainability and Financing Possibilities to Local Farmers in Nigeria With Credit Scoring

Develop an ethical credit score algorithm for farmers and provide traceability data analytics for crop buyers across supply chains and facilitate credit access to local farmers. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

This challenge requires experience in Data Analysis and Machine Learning.

 

The problem

Crop buyers are struggling with a broken agricultural supply chain to track and monitor value chains from farm to fork.

Smallholder farmers lack access to affordable credit due to a lack of valid data and exclusive credit scoring for farmers for financial institutions to make credit decisions.

Our partner Zowasel directly works with farmers to improve food security and address the impact of Covid-19 through:

  • Free GAP & Crop Quality Standardization 
  • Access to Improved Farm Inputs
  • Access to Financial Services
  • Access to premium Market

 

The project goals

The goal of the project is to build an ethical credit scoring machine learning algorithm for farmers and provide traceability data analytics for crop buyers across supply chains.

 

The data

This challenge will require the collection of data.

 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

 

Find more information on how an Omdena project works