Big data refers to the large amounts of complex information that are collected, processed, and analysed to help make better decisions. In farming, big data is important in tackling the increasing global demand for food, with the United Nations predicting that the global population will reach 9.8 billion by 2050, a 2.2 billion increase from now. This means that we will need to substantially boost crop output in order to meet a growing population. Unfortunately, increased urbanisation and climate change have taken a significant portion of farmlands. In the United States alone, cropland area has decreased from 913 million acres in 2014 to 899 million acres in 2018.
Today, there is an urgent need to produce more food for the rising population on less land. In this post, we’ll look at how big data and agtech (or agricultural technology) can assist address this issue.
What Role Does Big Data Play in Agriculture?
Policymakers and business leaders are turning to technology such as IoT, big data, analytics, and cloud computing to help them cope with the demands of rising food demand and climate change.
IoT devices aid in the initial phase of this process, which is data collection. Sensors installed on tractors and trucks, as well as in fields, soil, and plants, help capture real-time data directly from the ground.
Secondly, analysts combine the huge volumes of data collected with other cloud-based information, such as weather data and pricing models, to identify patterns.
Finally, these patterns and insights help to control the situation. They assist in identifying current difficulties, such as operational inefficiencies and soil quality issues, as well as developing predictive algorithms that can notify users even before a problem arises.
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The adoption of analytics in agriculture has been increasing consistently; its market size is USD 1.4 Billion in 2024 and will expand at a compound annual growth rate (CAGR) of 13.10% from 2024 to 2031.
Key challenges of implementing Big Data in the agriculture sector
- One of the main challenges in farm management information systems is generating high-quality data.Â
- The vast amount of unstructured and varied data needs skilled experts to analyse it.
- In developing countries, it is difficult to create affordable solutions for farmers.
- Bringing together data from different sources in a sustainable way is essential for success, but it remains one of the toughest tasks.
Key Applications of Big Data in Farming
The possibilities for big data applications are vast, and we have only scratched the surface. The ability to track physical goods, collect real-time data, and forecast situations has the potential to completely transform farming methods.
Let’s take a look at a few use cases where big data can make a difference.
Feeding an Expanding Population
This is one of the most pressing issues, and nations are working together to address it. One approach to accomplish this is to boost yields from existing farmlands. Farmers may use big data to get detailed information about rainfall patterns, water cycles, fertiliser requirements, and other topics. This allows them to make more informed decisions, such as which crops to sow for higher profitability and when to harvest. The appropriate actions ultimately lead to higher farm output.
Ethical Pesticide Use
Pesticide administration has been a sensitive issue because of the environmental consequences. Big data helps farmers manage pesticides more effectively by recommending which pesticides to use, when, and in what quantities. Farmers can comply with regulatory laws and avoid the abuse of pesticides in food production by regularly monitoring them.
Optimising Agricultural Equipment
Companies such as John Deere have integrated sensors into their farming equipment and implemented big data applications to better manage their fleet. For big farms, this kind of monitoring can be lifesaving because it informs users of tractor availability, service due dates, and fuel refill warnings. In essence, this optimises consumption while ensuring the long-term health of farm equipment.
Managing Supply Chain Issues
According to McKinsey, one-third of all food produced for human consumption is lost or wasted every year. This is a terrible fact given the industry’s effort to close the supply-demand gap. To address this, food supply chains from producer to market must be shortened. Big data can aid in supply chain efficiency by tracking and optimising delivery vehicle routes.
Current Research and Applications of Big Data in Agriculture
There is an increasing amount of research being conducted on the application of Big Data in agriculture. According to one recent study, the use of Big Data in agriculture can lead to considerable increases in crop production while also reducing waste. According to the study, precision agriculture technology like GPS mapping and sensor networks can enhance crop yields by up to 30%.
In addition to precision agriculture, Big Data is being used to improve supply chain management in agriculture. Farmers may cut waste and discover supply chain inefficiencies by tracking produce movement from field to supermarket. Walmart, for example, uses blockchain technology to trace the transit of produce from farm to store, which has resulted in considerable reductions in waste and improved food safety.
Here are some companies that use Big Data:
- SatAgro: a Spanish startup that provides satellite-based agricultural monitoring and analysis to farmers. They employ satellite imagery and machine learning algorithms to detect agricultural stress, nutrient deficiencies, and other factors that might reduce crop yields. This enables farmers to take specific steps to solve these concerns and boost yields.
- Syngenta: a major agribusiness company that takes advantage of big data to create new crop varieties and increase crop yields. They use modern analytics and machine learning to analyse vast volumes of data from field trials and genetic studies, allowing them to find the most promising crop kinds and optimise their performance.
- Carbon Robotics:Â They offer an autonomous weeder that combines computers with deep learning to identify and “zap” weeds using carbon dioxide lasers installed on a four-wheel platform driven by diesel and hydraulics. With its eight laser modules, the weeder can eliminate more than 100,000 weeds every hour. This startup uses deep learning approaches in the development of sensors and camera resolutions for speedy development.
- Soiltech:Â a Spanish startup that provides soil analysis services based on big data and machine learning. Their technology consists of sensors that assess soil parameters such as pH, nutrient levels, and moisture content, which are then evaluated to provide fertiliser and other soil management recommendations. The device can also forecast crop yields depending on soil and weather conditions, allowing farmers to plan their operations for optimal efficiency.
Conclusion
Big data is reshaping agriculture by giving farmers the tools they need to increase crop yields and reduce waste. Farmers may use AI and other technologies to evaluate large amounts of data and make informed decisions about when to plant, how much fertiliser to use, and when to harvest. As the amount of data created grows, it is evident that Big Data will play an increasingly essential role in agriculture, and experts in this area need to know how to use it.
