Artificial Intelligence

How is AI Getting Popular in the Agricultural Sector? Is India Pacing up?

The advent of Big Data and sector-specific Machine Learning tools related to the sector can increase agriculture yields
How is AI Getting Popular in the Agricultural Sector? Is India Pacing up?
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Former Staff, Entrepreneur India
4 min read
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The sole driver and promotion of AI and its cognitive implications across industries, has not been just to reduce manual calculations significantly, but progressively and accurately predict results for future outcomes. While initially limited to technology-driven businesses, the gradual onset of global warming and climate change, over the last decade, has slowly-yet steadily allowed AI to penetrate into a traditional sector like agriculture, to help cope with the increasing amount of complexity in modern day farms.

Farm Analytics, driven by the cognitive ability of neural networks to run through large datasets, is one of the hottest drivers of the farming industry right now. And while the development of AI algorithms can be challenging in an agricultural setting, the advent of big data and sector-specific machine learning tools related to the sector can increase agriculture yields.

The prospects of AI in farming becomes even more important for a country like India, where more than 54.6% of the population is still engaged in direct agriculture, while close to 70% still depends on the sector directly or indirectly for their livelihood. Unlike the west, however, India’s agricultural problems cannot be dealt with just advanced agritech solutions like plant breeding and yield multiplying, as farming in our country still remains largely scattered and unorganized.

Scope of AI and ML in Indian Agriculture

To tackle India-specific agricultural problems, many agritech startups are using AI not just to asses direct-on farming, but also in development of improved seeds, crop protection, and fertility products. Take Aibono, for instance, a Bengaluru-based startup that assesses farms and crunches related data of soil, weather condition and so on, to build an imagery of the region and assesses the same to advise farmers about the right fertilizer mix to be used based on the soil condition so they get maximum yield returns. Similar services are offered by some other companies like PEAT, Earth Food and V Drone Agro, which use AI to assess soil conditions over the cloud to help farmers.

Moving a step further and using satellite data, SatSure, a London-based startup, that has its roots in India, uses ML techniques to assess imageries of farms and predict monetary prospects of their future yield. The company helps financers and insurers decide, what the value of agricultural land may be just based on assertive satellite imagery. Their networks can also predict weather iterations and its effect on the said farms.

And it’s not just startups in India that are using neural networks to run through large datasets. While the Andhra Pradesh government has is actively working with startups like SatSure, Microsoft India has signed a Memorandum of Understanding (MoU) with the Karnataka government to develop several forecasting models, that will help empower farmers with solutions based on Cloud, AI and advanced analytics.

The use of programmed  drones instead of airplanes for crop dusting, and deployment of robots as a service  to replace larger tractors are some of the other used of AI in farming, which has not been adopted so far in India, but can see development in the coming years.

Challenge of AI in Agricultural Sector

Although the use of AI is promising when it comes to farming, the development of AI algorithms can be challenging in an agricultural setting. The first and foremost block is the requirement of large amounts of data, particularly clean data  to efficiently train the algorithms. Also, while there is a significant amount of spatial data in agriculture, much of the data is only available once per year during the growing season, making research cycles limited.

For India in particular, non-availability of data from remote areas, and farmlands that don’t meet minimum hectre criteria during surveys, are often left out. Given that majority of our farmlands still remain fragmented, a mass assertion, or holistic data collection is far too  ambitious.

Investment a Positive Sign

Despite the challenges, and the nascent stage that AI in farming is still at, prospects of cognitive application to AI are promising. A tangible indicator of the same is the increase in the number of investments in the agritech sector, particularly in startups working with ML and AI.

In 2016, close to 53 Indian agritech startups raised $313mn, according to Agfunder.com. Of these the significant ones were Taaza ($8mn), AgroStar ($10 mn) and EM3 Agri Services ($10 mn). On a global scale too, the country remained one of  the most active geographies  to invest agritech startups, treading with the likes of US,Canada, UK, Israel, and France.

Taizo Son, Founder & Chairman of Mistletoe, and brother of SoftBank magnate Masayoshi Son also launched ‘Gastrotope’, an accelerator platform to help integrate the entire AgriFood startup ecosystem, in which he said, AI will play a crucial role.

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