Janatahack: Machine Learning in Agriculture
About the Event
Recently we have observed the emerging concept of smart farming that makes agriculture more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is Machine Learning — the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.
Machine learning is everywhere throughout the whole growing and harvesting cycle. It begins with a seed being planted in the soil — from the soil preparation, seeds breeding and water feed measurement — and it ends when neural networks pick up the harvest determining the ripeness with the help of computer vision.
This weekend get ready to harness the power of ML to solve a problem based in agriculture and get a chance to showcase your skills and win AV points!
FAQs
1. Where can I find the dataset and the problem statement for the hackathon?
The contest will go live on the designated contest start date and time. There is a timer that is shown at the top of this page which shows the remaining time before the contest goes live. This is when you can access the problem statement and datasets from the problem statement tab.
2. Can I share my approach/code?
Absolutely. You are encouraged to share your approach and code file with the community. There is even a facility at the leaderboard to share the link to your code/solution description.
3. I am facing a technical issue with the platform/have a doubt regarding the problem statement. Where can I get support?
Join the AV slack channel by clicking on 'Join Slack Live Chat' button and ask your query at channel: janata_hack
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Data Science
Over here, you can engage in discussions, ask questions, share insights, and converse about all things Data Science, from regression models to LLMs!
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Generative AI
Over here, you can engage in discussions, ask questions, share insights, and converse about all things Data Science, from regression models to LLMs!
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Data Engineering
Over here, you can engage in discussions, ask questions, share insights, and converse about all things Data Science, from regression models to LLMs!
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Frequently Asked Questions
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There are no specific requirements to register for the hackathon, although it is recommended to have some basic knowledge of the relevant topics, such as Data Science, Machine Learning, or Deep Learning, along with proficiency in a coding language, preferably Python.
Depending on the type of competition, you can participate individually or in a team.
You can access the problem statement under the "Problem Statement" tab once the Hackathon is live.
Participants benefit from one-on-one feedback, publication on a respected platform, recognition from a global audience, and monetary rewards for each published article. Additionally, the top articles receive special rewards.
Each article must be original, and pass plagiarism and not AI generated content checks. You can submit multiple articles as long as each is distinct. Proper citation of all references and image sources is mandatory.
In the Blogathon, an article typically explores a specific topic or idea within Data Science or Generative AI and is required to be at least 1000 words long. A guide, on the other hand, is a more comprehensive resource, covering all aspects of a particular subject in data science, and must be at least 2500 words long. Guides aim to serve as a one-stop resource, providing detailed insights and practical applications, whereas articles might focus on narrower or more specific topics.
Multiple submissions of the same article are prohibited and could lead to disqualification. Articles failing to meet the required length, originality, or citation standards will be rejected.
AVCC is a community for authors who have had three or more articles published in the Blogathons. Members benefit from monetary rewards for each published article and get the opportunity to showcase their work to a larger audience.
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