Let’s face it, Indian Food (Nutrition) & Agriculture is a messy sector, but for India to be a five trillion-dollar economy, it’s clear that it has to play a much larger role.

A quagmire of complex challenges, policy neglect over decades have resulted in daunting demographics. India’s agriculture sector employs more than half of India’s workforce, consumes around 90% of freshwater resources, uses nearly half the available land area, yet generates barely 13% of GDP and around 10% of exports. With nearly half of all farmers lacking access to credit, with rapid soil degradation and a staggering Rs 92,000 Cr-plus of produce lost to spoilage annually, the path forward appears long and difficult.  

All, however, is not bleak.  

Production growth has outpaced population growth for several decades, catapulting India into becoming amongst the largest global producers for wheat, rice, sugarcane, cotton, milk, pulses, fruits and vegetables. Going forward, India needs to balance volumetric targets with efficiency and sustainability, while moving up the value chain to enhance farmer and national income. 

Encouraged by crucial policy announcements recently, can we now dare to envision a reality wherein led by bold policies, mega-scale innovation and a foundation of new-age skills, Indian Agriculture becomes profitable, resilient and sustainable? By 2030, can India reposition itself as a world-leading agricultural Innovator? 

We must. And we can, within the decade.  

Given the long term neglect of Agronomics, the only way to make progress within this decade is to “pole-vault” over deficiencies by injecting appropriate technologies and innovation on a massively parallel scale, and adopting holistic, transformative, “platform” thinking as a foundation for a collaborative national approach.

Innovation and precision technologies [e.g.: IoT, sensors, weather forecasting, remote-sensing data from satellites, drones, robotics, mobile cameras, Artificial Intelligence etc.] can transform agriculture: raising yields, optimising resources, improving profitability. They yield multi-pronged impact across the complex agricultural value-chain. 

The “India Agricultural Platform” (IAP) will accelerate sectoral transformation:  

There is a visible need for an open, scalable, integrating platform, that democratises access to Agri information, credit, insurance and markets; incubates innovative business models; and enables better decision making. The Indian Agricultural Platform (IAP) created by the eco-system, governed by the Govt., is envisioned as an “enabling framework of Data and Services (applications) around a data exchange”.  

Consider this scenario, leveraging the IAP for a credit use-case in the year 2024: 

Selvam, a rice farmer with a two-acre farm near Madurai, logs into the IAP using retina scan, fills in a loan request in Tamil, attaches photos of himself and the farm. He accords consent for his data to be accessed from different entities (Govt, Start-ups, FPOs etc.): Aadhaar, geo-location, three year’s crop type, yield and earnings. 

This data flows in, completing Selvam’s application and providing visibility of his farming history to all potential lenders. To evaluate credit risk, they use the combination of geo-location along with Aadhar to extract Selvam’s farm credit history and details of all existing and completed loans. The automated process allows a majority of the lenders to approve/reject the loan online within minutes. Artificial Intelligence flags issues needing a clarifying phone call. 

Selvam chooses a cooperative bank which pays the seed supplier directly, crediting the balance loan into Selvam’s regular bank and creating an auto-debit for the month after harvest. The bank also remits a small fee to the Start-up for providing Selvam’s cropping history.

Back in 2020, Selvam recalls filling several loan applications individually, spending money travelling to Madurai and loan sanctions took an average of two months. The IAP has reversed and democratised the process, giving him the power while lenders now bid for Selvam’s loan. 

Comparing different offers, transparently allows Selvam to choose wisely.

Powered by Artificial Intelligence (AI) & Data Analytics, IAP helps tactical and strategic decision making, leveraging multi-year, multi-source information, aggregated from the farms to state/national levels. It processes huge data flows, and using tools like video, voice, vernacular translation, facilitate farmer engagement. The platform is hosted on a Cloud and reduces duplication by integrating data sources and a vast backend of new and existing applications: Govt’s eNam, ITC’s eChoupal, NCDEX’s NeML, APEDA’s TraceNet etc. related to logistics, weather, supply-chain, warehousing, assaying, recommendation engines, etc. 

AI and Agri data interoperability across the eco-system will incubate new business models (Agri and non-Agri) providing transformative impact to agriculture similar to how Aadhaar with UPI transformed digital payments.

Key benefits of the India Agricultural Platform (IAP):

1. “Market Facing”: Agricultural marketplace, for produce and raw materials, equipment sales, rentals etc. Availability of several aggregators on the IAP offers the farmer transparency enabling real-time deal-making.
2. “Advisory”: Information a farmer needs: (weather, crop-selection, pricing, etc.), available through several advisory channels (govt or private; free or charged) on the IAP will compete for farmer’s consumption, based on credibility and usefulness.
3. “Decision Making”: Multi-year, diverse data-sets, aggregated from farms up to district, state, national levels, interoperable across the eco-system, aided by data bandwidth and AI, improves decision making by everyone and strategic policy intervention by the Govt.
4. “Integration”: The IAP uses “Open APIs” to enable software application interoperability thereby creating a seamless Agri framework, allowing end-users freedom of choice and service providers to scale exponentially. 



India Agricultural Platform will incubate new “Data Partnerships”, Business models and Revenue streams:

Innovation impacts the entire agricultural value-chain: (Soil testing, Crop selection, Sowing, Irrigation, Yield estimation, Harvesting, Farm equipment, Farm operations & management including weed control & pesticide application, Price discovery, Sorting and Food processing).  This value-chain is rapidly becoming digital, leading to an increasing amount of live and real-time data being generated. Physical maps being digitized is an example, while another is through the multiplicity of payment and Agri trading platforms. In addition, there is a rapid increase in “machine-data” generated by precision farming applications: field sensors (for soil moisture etc.), satellites (for yield estimation, early pest warning etc.), robotics (for water and nutrient injection, harvesting, high precision weed removal etc.), mobile cameras (for pest attack, nutrient deficiencies), and drones (for real-time farm monitoring, surveying, 3D modelling etc.). 

This mountain of data, once anonymised, aggregated and processed can be re-purposed using AI on the IAP, for different use cases, raising yields, optimising national resources and doubling farmer income.

The IAP enables a technical and commercial framework to harness this dataflow and facilitate “data partnerships” between Govt, Start-ups, Corporates, Research, Academia based on either direct or indirect business benefit. Once AI processed data is available to be leveraged, new, innovative business models will emerge, that help monetise data contributing to improved productivity and profitability of agriculture and other sectors as well.  

Agri Fintech (Credit use-case): Half our farmers, cannot get credit easily, because of incorrect or lack of land records and financiers have limited information for credit-risk assessment. The IAP can facilitate this, as in Selvam’s example, by triangulating data real-time, from several entities: a farm management start-up revealing cropping history, satellite data for estimated yield & water source, geolocation coordinates with Aadhaar helps a lender assess credit-risk. The lender readily pays a 0.5% assessment fee to the start-up for its farm data, aiding its revenue stream. 

Similar partnerships leveraging AI and data analytics will emerge, and innovative business models will be created across the value-chain: for insurance, market access, grading of produce etc. Given that similar challenges exist across the developing world, India will establish itself as a globally recognised Agri innovator. 

Translating Agri Data into actionable Insight: (Govt. decision making use-case). 

Picture this real life scenario: 

A Bihar Govt bureaucrat is trying to forecast tomato’s post-harvest prices to avoid the heartbreaking crisis last season when prices crashed due to a sudden glut. Tomatoes dumped on the road and farmer suicides attracted bad press. He pores over submissions from each district, showing crop-wise acreage and sowing week. From experience, he knows this data could be 2-3 months old, while tomato’s harvest in 3-4 months. He observes that gross crop acreage varies 15-25% across submissions and he suspects some districts fill in data without stepping out of the office. 

Based on this, how can he forecast prices or take action?

Should he believe the input suggesting Tomato acreage is 15% lower than the previous cycle? How would the forecasted winter rains and colder weather impact tomato yield? He wishes he could advise farmers better because staggering sowing and harvesting by 1-2 weeks play a big role in smoothening market price swings. 

But for that, he needs automated, real-time data.

To solve this problem, an enabling framework like the IAP will use Agri Data-sets from multiple sources (government, enterprises, start-ups), seamlessly translating it into Information and then into precisely actionable Insight to be leveraged for varied Agri (and non-Agri) use cases.  

For instance, digital crop signature, from satellites, combined with AI, can reveal crop-wise acreage under plantation, by district within 4-6 weeks of planting. As the crop matures, it estimates crop yield, and then combined with acreage and processing capacity in proximity - likely post-harvest prices. The IAP also provides an alternate forecast by analysing aggregated seed sales data, district wise, from suppliers to predict acreage under tomatoes. Both estimates are correlated for accuracy. 

Early price forecasts enable faster tactical actions avoiding price crashes, like helping stagger harvesting, or tying-up additional quantities with processing plants in advance. 

This kind of application integration, requires seamless data interoperability across the Agri eco-system, with due conformance to Data privacy and usage policies. Currently, there is a vacuum with respect to Agri Data standardisation, calibration and certification. Disaggregated and non-standardised data is deemed un-trustworthy and rendered ineffective for further processing. Standardisation will help improve “data-trust” furthering automation using AI models, also avoiding real-world biases creeping into AI prediction.