India is a deeply agrarian society with roughly 65 percent of the population involved in agriculture. Thanks to the “green revolution” of the 1960s and 1970s, when new crop varieties, fertilizers, and pesticides boosted yields, the country has long been self-sufficient regarding food—an impressive feat for a country of 1.4 billion people. It also exports more than $40 billion worth of foodstuffs annually. But for all its successes, the agricultural sector is also highly inefficient.
That’s why, in 2018, India’s national government identified agriculture as one of the areas of focus of its AI strategy. Since then, India’s burgeoning agritech sector has changed how farmers operate. This paper outlines many of the developments and remaining challenges as AI is leveraged.
Many startups use AI and digital technologies to provide bespoke farming advice and improve rural supply chains. The government recently announced roughly US$ $300 million in funding for digital agriculture projects. With considerable government support and India's depth of technical talent, there's hope that AI efforts will lift the country's massive and underdeveloped agricultural sector.
It's a heavy lift: water shortages, a rapidly changing climate, disorganized supply chains, and difficulty accessing credit make every growing season a calculated gamble for farmers. The hope is that new AI-powered tools can take some of the unpredictability out of the endeavor. However, experts also caution that technology is not a panacea and say that without careful consideration, the disruptive forces of innovation could harm farmers as much as they help.
A Bengaluru-based startup called Fasal uses a combination of sensors, predictive modeling, and AI-powered farm-level weather forecasts to provide farmers with tailored advice, including when to water their crops, when to apply nutrients, and when the farm is at risk of pest attacks. Users have significantly reduced pesticide and water use.
Harish B. uses AI tools from the startup Fasal to make decisions about irrigation, the application of pesticides, and fertilizer
Ananda Verma, founder of Fasal, says the product can make farmers' lives a little easier in many ways. His company sells sensors that collect data on crucial parameters, including soil moisture, rainfall, atmospheric pressure, wind speed, and humidity. This data is passed to Fasal's cloud servers, where it's fed into machine learning models, along with weather data from third parties, to produce predictions about a farm's local microclimate. Those results are input into custom-built agronomic models that predict a crop's water requirements, nutrient uptake, and susceptibility to pests and disease.
The output of these models is used to advise the farmer on when to water or apply fertilizer or pesticides. Typically, farmers make these decisions based on intuition or a calendar, says Verma. However, this can lead to unnecessary chemical application or overwatering, which increases costs and reduces crop quality. “[Our technology] helps the farmer make very precise and accurate decisions, completely removing any kind of guesswork,” he says.
Harish C.S. says Fasal's services make his 24-hectare fruit farm more profitable.
Fasal's ability to provide these services has been facilitated by a rapid expansion of digital infrastructure in India, particularly countrywide 4G coverage with rock-bottom data prices. The number of smartphone users has jumped from less than 200 million a decade ago to over a billion today. “We are able to deploy these devices in rural corners of India where sometimes you don't even find roads, but there is still Internet,” says Verma. However, taking full advantage of Fasal's advice requires investment in other equipment like automated irrigation, which may put the solution out of reach financially for small farmers in particular.
Another avenue for farmers is leveraging technology's ability to connect farmers and help them share best practices, over YouTube, for instance. Some experts are betting that AI could help farmers with that knowledge sharing, but concerns about surveillance capitalism, which uses data collected about Internet users to manipulate their behavior, could morph into surveillance agriculture, data-based digital technologies that take decision-making away from the farmer. Not only would this erode farmers' autonomy and steer their decision-making in ways that may not always help, but the technology could also interfere with existing knowledge-sharing networks.
Proponents of AI-informed agrotech solutions agree that they don't work in isolation. To be effective, they must be combined with other digital technologies and carefully integrated into existing supply chains.
As with many AI applications, one of the biggest bottlenecks to progress is access to databases. Many of these databases are under the government’s purview, and several efforts are underway to make them accessible.
While AI-informed agritech initiatives are looking to the future, India’s rich history of past agricultural cooperatives and bottom-up social organizing provides an optimal backdrop for bringing innovative new agricultural technologies online for the benefit of India and perhaps even the world.
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