Wadhwani AI

Pest Management for Cotton Farming

The CottonAce solution guides smallholder cotton farmers on the optimal time to take preventive action and protect their farm against avoidable crop losses due to pests such as the Pink and American bollworm.

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BOLLWM: A real-world dataset for bollworm pest monitoring from cotton fields in India

This paper presents a dataset of agricultural pest images captured over five years by thousands of small holder farmers and farming extension workers across India. The dataset has been used to support a mobile application that relies on artificial intelligence to assist farmers with pest management decisions. Creation came from a mix of organised data collection, and from mobile application usage that was less controlled. This makes the dataset unique within the pest detection community, exhibiting a number of characteristics that place it closer to other non-agricultural objected detection datasets. This not only makes the dataset applicable to future pest management applications, it opens the door for a wide variety of other research agendas.

This paper presents a dataset of agricultural pest images captured over five years by thousands of small holder farmers and farming extension workers across …
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CottonAce: Kharif 2021 Impact Report

CottonAce, Wadhwani AI’s early pest warning and advisory system, was developed to aid in the larger effort to improve the lives of cotton farmers in India. Between June to December 2021, the AI-powered solution was used by over 6,000 farmers across 60 districts and 10 states in the country.

Our latest report assesses the impact it has had on the ground, and outlines some of the challenges present in implementing an AI-powered pest management intervention in one of the most complex agricultural systems in the world. 

CottonAce, Wadhwani AI’s early pest warning and advisory system, was developed to aid in the larger effort to improve the lives of cotton farmers …
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Pest Management Codebase

Pest monitoring is an integral aspect of the CottonAce pest management AI solution by the Wadhwani Institute for Artificial Intelligence (Wadhwani AI). This project helps cotton farmers make better pest management decisions by providing pesticide advice based on photos of pests caught in specialised traps. To provide the correct advice, specific pests must be identified within a photo and then counted. This code in this repository is focused on accomplishing that task.

Pest monitoring is an integral aspect of the CottonAce pest management AI solution by the Wadhwani Institute for Artificial Intelligence (Wadhwani AI). This project …
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A Case for Rejection in Low Resource ML Deployment

Building reliable AI decision support systems requires a robust set of data on which to train models; both with respect to quantity and diversity. Obtaining such datasets can be difficult in resource limited settings, or for applications in early stages of deployment. Sample rejection is one way to work around this challenge, however much of the existing work in this area is ill-suited for such scenarios. This paper substantiates that position and proposes a simple solution as a proof of concept baseline.

Building reliable AI decision support systems requires a robust set of data on which to train models; both with respect to quantity and diversity. …
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Pest Management In Cotton Farms: An AI-System Case Study from the Global South

Nearly 100 million families across the world rely on cotton farming for their livelihood. Cotton is particularly vulnerable to pest attacks, leading to overuse of pesticides, lost income for farmers, and in some cases farmer suicides. We address this problem by presenting a new solution for pesticide management that uses deep learning, smartphone cameras, inexpensive pest traps, existing digital pipelines, and agricultural extension-worker programs. Although generic, the platform is specifically designed to assist smallholder farmers in the developing world. In addition to outlining the solution, we consider the set of unique constraints this context places on it: data diversity, annotation challenges, shortcomings with traditional evaluation metrics, computing on low-resource devices, and deployment through intermediaries. This paper summarizes key lessons learned while developing and deploying the proposed solution. Such lessons may be applicable to other teams interested in building AI solutions for global development.

Nearly 100 million families across the world rely on cotton farming for their livelihood. Cotton is particularly vulnerable to pest attacks, leading to overuse …
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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
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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.
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