Wadhwani AI Bollworm Counting Challenge, Presented by #ZindiWeekendz

Use our training data – data captured by farmers and farm extension workers since 2018 – to build models that accurately count the number of bollworms present in the image data.
This competition, in collaboration with Zindi, FAIR Forward, and GIZ, is a step towards finding innovative ways to tackle the challenge presented by bollworms.

During the Kharif season of 2017, cotton farmers across India lost large portions of their crop to a pest known as bollworms. Since then, farmers have been encouraged to install pheromone traps in their fields as part of a larger effort to control the problem. Pheromone traps capture male bollworms that are capable of reproducing. By counting the number of bollworms caught in such traps, farmers can estimate potential bollworm infestation to make informed decisions about whether pesticides should be applied to minimise future damage.

Wadhwani AI has developed a mobile app that allows farmers to take photographs of trap catches and receive recommendations based on AI-generated counts of pests in those images. One of the biggest challenges for the app is knowing when there are no pests in the image. This can happen because there were no pests actually caught in the trap. It can also occur when users are experimenting with the app outside of their fields — during training sessions, or demonstrating the flow to friends.

When there are no pests in the image but the app says otherwise, user trust is immediately eroded.

Although the Wadhwani AI machine learning team has worked on this model for years, we are keen to explore new ideas for tackling the challenge. This competition, in collaboration with Zindi, FAIR Forward and GIZ, is a step in that direction.

Take the Challenge

The objective of this challenge is to use our training data — data captured by farmers and farm extension workers since 2018 — to build models that accurately count the number of bollworms present in the image data.

Click here to watch the launch webinar video.


This competition and the associated data preparation are made possible with support by the convening sponsor, FAIR Forward – Artificial Intelligence for All, which is implemented by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the German Ministry for Economic Cooperation and Development (BMZ).

The FAIR Forward – Artificial Intelligence for All initiative promotes a more open, inclusive and sustainable approach to AI on an international level. It is implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ). FAIR Forward seeks to improve the foundations for AI innovation and policy in five partner countries: Rwanda, Uganda, Ghana, South Africa and India. Together with our partners, we focus on three areas of action: (1) strengthen local technical know-how on AI, (2) increase access to open AI training data, (3) develop policy frameworks ready for AI. For more information see https://toolkit-digitalisierung.de/en/fair-forward/.

  • Wadhwani AI

    We are an independent and nonprofit institute developing multiple AI-based solutions in healthcare and agriculture, to bring about sustainable social impact at scale through the use of artificial intelligence.

ML Engineer

ROLES AND RESPONSIBILITIES

An ML Engineer at Wadhwani AI will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:

Early stage of proof of concept (PoC)

  • Setup and structure code bases that support an interactive ML experimentation process, as well as quick initial deployments
  • Develop and maintain toolsets and processes for ensuring the reproducibility of results
  • Code reviews with other technical team members at various stages of the PoC
  • Develop, extend, adopt a reliable, colab-like environment for ML

Late PoC

This is early to mid-stage of AI product development

  • Develop ETL pipelines. These can also be shared and/or owned by data engineers
  • Setup and maintain feature stores, databases, and data catalogs. Ensuring data veracity and lineage of on-demand pulls
  • Develop and support model health metrics

Post PoC

Responsibilities during production deployment

  • Develop and support A/B testing. Setup continuous integration and development (CI/CD) processes and pipelines for models
  • Develop and support continuous model monitoring
  • Define and publish service-level agreements (SLAs) for model serving. Such agreements include model latency, throughput, and reliability
  • L1/L2/L3 support for model debugging
  • Develop and support model serving environments
  • Model compression and distillation

We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.

DESIRED QUALIFICATIONS

Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.

Programming

  • Expert level Python programmer
  • Hands-on experience with Python libraries
    • Popular neural network libraries
    • Popular data science libraries (Pandas, numpy)
  • Knowledge of systems-level programming. Under the hood knowledge of C or C++
  • Experience and knowledge of various tools that fit into the model building pipeline. There are several – you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
  • Database concepts; SQL
  • Experience with cloud platforms is a plus
mle

ML Scientist

ROLES AND RESPONSIBILITIES

As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.

In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.  

REQUIREMENTS

Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments. 

Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good. 

DESIRED QUALIFICATIONS

  • B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field. Work experience beyond the terminal degree will determine the appropriate seniority level.
  • Solid software engineering skills across one or multiple languages including Python, C++, Java.
  • Interest in applying software engineering practices to ML projects.
  • Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
  • Strong verbal and written communication skills in English.
mls