Learnings from the lockdown: Invest in farmers to make agriculture supply chain uninterrupted

Covid19 led lockdowns could cause global food shortages. Farmer collectives and ICT could be the answer
Despite instructions, farmers have not been able to reach mandis

The United Nations has warned that COVID-19 could possibly trigger a global food shortage. In India, despite there being clear instructions from the government, the current lockdown has clogged the agriculture supply chain.

There are reports of trucks stuck in queues at entry points of cities waiting for passes. The lockdown is necessary to contain the spread of COVID-19, but it has affected labour availability in agriculture fields for harvesting of wheat, gram, potato and other seasonal crops. State-run Agricultural Produce Marketing Committees (APMCs) or mandis are closed for trading. There are other limited options available for farmers to sell their produce, but the current situation makes these options hard to access.

The fear is that the already unstable livelihood of the country’s 140 million farmers is going to be adversely impacted.

In times like this, the business continuity of agricultural value chain activities is crucial to make food items available. It is also important to ensure that livelihoods of farmers are not disrupted.

We urgently need ideas in not only dealing with the crisis like this, but ideas that are sustainable and will benefit the country when things go back to normal.

Here are two thoughts –

  • Promoting and strengthening farmer collectives
  • Promoting ICT in agriculture

These two are not new concepts but the business case to invest in them further, are more convincing than ever before. These are crucial to prepare the Indian farmers for the future.

The first one is related to farmer collectives, i.e. a group of farmers who collectively participate in agricultural value chain activities. The collective is a legal entity that is responsible for managing commercial transactions among individual farmers, sellers and buyers.

Mewar Green Agro Producer Company, a collective of close to 500 tribal farmers operating in south Rajasthan, has ensured that farmers have access to critical technical advisory services through phone calls. The company is also reaching out to potential buyers to secure market for their produce beyond mandis. It is also engaging with agri-input (seed, fertilizers, etc) providers so that its members are prepared for the upcoming monsoon crop.

 “In a social isolation environment, farmer collective is the only trusted connector between the individual farmers and the market.”

Lalit Joshi, promoter, Mewar Green Agro Producer Company

According to National Bank for Agriculture and Rural Development (NBARD), there are around 5,000 farmer collectives in India. Assuming each collective has about 500 members, there are currently 2.5 million farmers who are within the ambit of these collectives. Another 75% of farmers in the country will need to be connected to this model. It is a mammoth task, but not impossible. Especially, if the country wants its farmers to flourish with or without COVID-19.

The second is about the large-scale adoption of ICT in agriculture. The crisis has further strengthened the belief that adoption of ICT is a requisite for the farmers to access vital services.

The government’s e-NAM (National Agriculture Market) platform to strengthen agriculture marketing for farmers, including the collectives, is an initiative in the right direction. The platform is expanding its features based on the current lockdown situation as well. For example, the platform has aggregated the services of transport companies bringing at least 375,000 trucks accessible to the farmers. e-NAM could become the next Uber-like on-demand service for agriculture in India.

Investing in ICT could solve many systemic problems too

Now, if you add AI and other predictive models to the mix, it becomes a long term solution.

In a global lockdown, farmers across the world can get updates about the status of their crop, essential parameters such as weather, information about buyers, sellers, and other service providers all through ICT-based solutions. This is already happening but will have to be scaled up through public and private investment.

But are the farmers ready to use such solutions? The adoption of such technology-driven solutions will depend on the investment that the government and other stakeholders put to make farmers digitally literate. Also, the investments in creating digital infrastructure, i.e. IOT platforms, for example, will have to be pushed. COVID-19 has demonstrated the need and the opportunity to invest in this.

More investments to set up and strengthen producer collectives as well as to promote large-scale adoption of ICT in agriculture is the need of the hour. Specific interventions will vary according to the area and farmers, though investment will have to be universal.  This will secure farmers livelihood, protect rural community and ensure there is food on our plates.

Co-authored by Kulranjan Kujur, an international development professional.

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
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  • 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
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  • 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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