What does the pest management tool mean for farmers?

How can we help farmers get the most out of their plot of land? We spoke to a few farmers who were part of our pilot over the summer and documented the stories of two.
Pests, primarily American and Pink Bollworm, have in the past few years destroyed up to 50% of the cotton crop.

Over the past three months, the team at Wadhwani Institute for Artificial Intelligence has been working with over 100 cotton farmers in Karnataka. A small village near Hubli was our pilot location. Agriculture contributes about 18% of India’s GDP, and cotton farming forms a major part of the country’s agricultural product. Pests, primarily American and Pink Bollworm, have in the past few years destroyed up to 50% of the crop. They hurt not just the ecosystems but also several thousand families. AI can play a role in alleviating this problem.

Our blog detailed how we set up processes and managed to conduct a pilot remotely. This article by our researchers demonstrates the role AI plays in our product. But none of this works if the app is not built for the farmers, who are a unique audience. This documents the design thinking that went into building the app. But all of this leads to one place–impact. How can we help farmers get the most out of their plot of land? We spoke to a few farmers who were part of our pilot over the summer and documented the stories of two. 

“No one told me I had suffered a pest attack.”

Kariyappa D Katti

Kariyappa is a 20-something farmer, who has been growing cotton for the past ten years. He leads a simple life. But 2019 was a dark time. His one-acre plot, which usually yields 10 quintals of cotton, gave out just 5. It was not enough to cover his costs. He didn’t know what went wrong. When he picked the lint, it had a black rotten core. It is not until a year later till he used Wadhwani AI’s pest management solution did he realise that he had faced an attack by the Pink Bollworm.

“No one told me, before the app, that I had suffered a pest attack,” he says. What about his neighbours or his friends? He stops for a second before he replies. “People don’t want to tell you these things because money is tight. They believe the market can’t give everyone a good rate.” 

So what did he think of the pests on his farm?  “I didn’t know what these insects were,” he says. “Not until I caught them in the trap did I realise they were hurting my crop.” The app advised him that he had a severe attack. A red alert. It asked him to take immediate action and spray pesticide to protect his crops. 

“It was the first time I sprayed anything on my crops. I don’t even know what I sprayed. All I remember that it had a green tree on it,” he says. He didn’t question what he was asked to spray. It is not in his nature to question these things. How much was his yield this season? 13.5 quintals. “It has never been this good.”

“I thought it was the risk of farming.”

Baramappa B Mailar

Baramappa loves his phone. He uses everything. Whatsapp, Youtube, Telegram, Facebook and even GooglePay. Wadhwani AI’s pest management app is not his favourite though. That is the now banned Tiktok. When the extension workers asked him if he wanted to participate in the pilot he agreed – Another cool app on his brand new smartphone. It took a little time learning the process. Open the app, find the name of his village, upload the picture from his gallery and then wait for an advisory. The advisory, he says, was confusing. It gave him names of four pesticides he had never heard of before. And four? How could he afford four? He took a screenshot of the advisory and brought it to the pesticide retailer. The man at the counter nodded and handed him one bottle. “That’s when I realised the advisory was asking me to use one of them,” he says. The program and product team at Wadhwani AI had tracked this problem before. He was still sceptical but that was until it was the harvesting season. 

In 2019, his yield was close to 7.5 quintals. It was about what he had come to expect. There was a steady decline in his yield over the last five years. From 10 quintals per acre, it had dropped to 7.5. “I thought it was the risk of farming,” he says. “These things just happen.” This year the yield is higher–13 quintals per acre. Any complaints? “I just wish you had come to our village last year.”

ML Engineer


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.


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


  • 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

ML Scientist


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.  


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. 


  • 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.