01 Dec 2020
Does Data-Enabled Agriculture Build a Successful Business Model in Farming?
Pooja Joshi
#Business | 6 Min Read
According to research by the United Nations, the global population is expected to be 9.5 million by 2050, approximately a 2.2 billion increase from now. This implies that the demand for food will increase, and thus, crop production will also grow. This isn’t as simple as it sounds, because there are several hurdles that hamper agricultural supply, from food security to climate change. To overcome these challenges, we are seeing a shift towards technology and smart farming in the agricultural sector. Farming using diverse information and communication technologies is a new concept and the sector has shown immense benefits. Lately, leading agricultural ventures have been mobilizing the potential of cloud technologies to solve an array of problems related to farming. For instance, John Deere, a renowned farm equipment manufacturer, came up with cloud-based software Operations Center. This software successfully keeps track of a farm vehicle’s performance for quick and effective troubleshooting. In India, on the other hand, as reported by NASSCOM in 2019, there are more than 450 agri-tech start-ups, and they are growing at a rate of 25% annually. This sector’s potential is evident from the fact that it has received more than $248 million in funding.
Countering the pressures faced by the agricultural sector has become comparatively more straightforward with modern technologies like artificial intelligence, remote sensing, data analytics, GIS, blockchain, various Internet of Things (IoT) devices, and much more. These ensure more effective, productive, and prosperous farming practices. But among these, a data-enabled business model has been the most profitable as the collection of real-time data helps predict the prospects of farming practices. Reports suggest that the advocacy of data and analytics in agriculture has been growing consistently; the market size of global agriculture analytics is likely to increase from USD 585 million in 2018 to USD 1,236 million by 2023, at a Compound Annual Growth Rate (CAGR) of 16.2% during the forecast period. Large amounts of data are collected and integrated by experts to put forward alerts and solve complicated problems related to soil quality or other operational incompetencies.
How is data derived in smart agriculture?
The Internet of Things has positively impacted many industries, and agriculture is undoubtedly one of them. IoT helps fight several farming challenges, be it weather conditions, climate change, or the quality of seeds and pesticides. Sensors were introduced a few decades ago, but they were handled conventionally. With the introduction of IoT in agriculture, technologically advanced sensors are used that derive live data. For example, remote sensing assists in tracking the condition of crops in a field regularly to recognize any possible risk and take necessary precaution accurately. Cloud-based data storage with IoT platforms plays a vital role in smart agriculture. Whether farmers want to know about crops’ live status or the weather, real-time data analysis is quite impactful. Few recent use-cases show how IoT and big data in farming have been successful, like Vodafone’s Precision Farming solution that lets farmers use only the amount of fertilizer needed or Digital Transmission Network (DTN). Using DTN, farmers can examine updated weather data to manage their farmlands better.
Space monitoring
Turning to technology has been one of the most sought-after ways to maintain smooth global food supply, and space monitoring is an integral part of smart farming. Be it frequent droughts or locust outbreaks, space monitoring through geospatial tools is a boon to agriculture. The remote sensing satellite imagery offers critical data for monitoring crops, soil development, and other climatic conditions. For example, climate, soil, and other assessments from satellites assist farmers in planning the required time and amount of irrigation needed for their crops. In this way, the adverse effects of food shortages and famines are tackled.
Use of big data in farming
While smart farming and big data are opening new gates for profitable agricultural yield, the collection of real-time data to forecast various situations is something that has improved farming practices.
Here’s how big data is being used in recent times.

  • Prognosis of yield
    Prediction of yield is one of the most meaningful uses of data in farming, as it helps farmers evaluate specific questions like what to plant, where, and when to plant crops. This is majorly done with specific mathematical and machine learning models and sensors that examine data around yield, weather status, biomass index, and much more. For farmers, the decision-making process becomes smooth and convenient, with an improved approach towards crop production.
  • Effective management of risk
    The chances of crop failure can quickly be evaluated with big data, and hence, farmers find it quite useful. Daily satellite images are combined with relevant data to identify weather scenarios like wind speed, humidity level, rainfall, and much more. Back in 2014, an integral suggestion from data scientists to Colombian rice farmers had reportedly saved millions of dollars in damages due to changing weather patterns. Damages due to changing weather conditions or other reasons can be evaded with data science.
  • Improvement of farm equipment
    Equipment manufacturing companies have integrated sensors in farming vehicles and farm equipment. This deployment of big data applications help farmers monitor the long-term health of farm equipment and also lets users know about the availability of tractors, due dates or servicing, etc.. Recently, scientists and researchers from Connecticut University have brought forward some in-field soil moisture sensors that reduce excessive water consumption during farming by at least 40%.
  • Bridging the gap between supply and demand
    Increasing supply chain transparency is one of the crucial benefits that big data offers. One of the main struggles in the food market is to bridge the inevitable gap between supply and demand. According to a report by Mckinsey, at least one-third of food produced in a year for consumption is wasted. With real-time data analysis, several forecasts have become more accurate, and integrated planning is now possible. This also helps to track and optimize routes of delivery trucks.
The cloud-based ecosystem through IoT and big data is gearing up to revolutionize agriculture as space monitoring, or cloud-based apps are helping farmers adjust production in tune with market demand. Today, the scope of big data in agri-tech is exceptionally significant. Lloyd Marino of Avetta Global, an eminent big-data expert, has pointed out, “Big data in conjunction with the Internet of Things can revolutionize farming, reduce scarcity and increase our nation’s food supply dramatically; we just have to institute policies that support farming modernization.” Working together to enhance the use of smart farming and the application of data in agriculture would strengthen agri-tech solutions for a better future. This will not only increase the production efficiency of crops but also mitigate the problems of higher demand for food and supply shortage.