According to the United Nations, the global population will reach 9.8 billion by 2050, increasing by 2.2 billion. This means that we will need to increase crop production dramatically to feed the expanding population. Data analytics in Agriculture has revolutionized one of the oldest human activities. And now, it is possible to solve the world’s most pressing issues by advancing data-driven agriculture technology. A data-driven approach makes a meaningful difference, especially in areas where farming is a means of livelihood and people suffer from environmental and climate issues like crop loss. But where does the implementation of big data fit in with agricultural businesses?

Data analytics can help a business enhance decision-making, assess customer trends, track customer engagement, and uncover new product and service prospects to fulfill market demand. Organizations can acquire real-time insights into marketing, product demand, sales, and finances by integrating information and technologies to gather data across the business.

Collaboration between farmers, large agricultural enterprises, communities, and governments can yield enormous benefits in AgriTech. Data science services and big data analytics are becoming increasingly popular in agriculture as a result. The AgriTech analytics market is expected to increase from $585 million in 2018 to $1,236 million by 2023, MarketsAndMarkets reports.

Opportunities of Big Data Agriculture.

  • Enhance farming productivity

Using big data analytics in agriculture enables businesses to predict agricultural yields and better crop quality.

  • Enhance farming operations

Due to the development of smart measurements and reports, data analysis for agricultural firms enhances yields while reducing resource consumption such as water and power.

  • Reduce food wastage

20% to 30% of food is thrown away at various points in the supply chain. By 2030, AgriTech will save $155–405 billion per year by overcoming this obstacle.

  • Modified seeds contributing to less hunger

Big data-driven genetic engineering of seeds may appear to be a bad thing at first, and the media frequently depicts it as such. However, seeds developed with data analytics may be able to eradicate world hunger by modifying them so they can be grown in any climate.

  • Attract greater investments

There are a lot of AgriTech businesses that know how to get people to invest in them. In 2018, AgFunder reported a total investment in AgriTech of $17 billion, up 43% from the previous year.

  • Attract and retain labor force

The growing use of data analytics in agriculture demonstrates that technology can be at the center of the world’s oldest industry, making it more appealing to experts and preventing them from seeking other sectors.

  • Environmental awareness

Data Analytics shows businesses can safeguard the environment without increasing expenditures. Farmers and agribusiness corporations have been leading the way in implementing steps to minimize their environmental effects.

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Data Analytics Implementation: A New Direction For AgriTech Farming

In the agricultural industry, the impact of data analytics has been so extensive that it is difficult to pinpoint all of its implications and even more difficult to foresee what changes this might bring. With that in mind, here are six ways that data analytics has revolutionized the agricultural industry:

  • Automated Reporting, Dashboards, And Analytics

Smart, digitized, and precision farming have significantly enhanced the amount of data available for the multiple improvement possibilities in agriculture for smart spraying, sowing, and harvesting. It’s not just the amount of data that matters, but the capacity to derive useful insights using big data in agriculture. Analytical reports and dashboards are essential for an industry that has diverse variables. Technology experts can aim to make farming technologies more transparent and easier for farmers to implement despite the difficulty.

Farmers should be able to use big data in agriculture to automate and visualize as much as possible through dashboards and analytics solutions from AgriTech companies. Data gathered in a back-end system can be shown on a customizable dashboard with user-friendly data displays.

Maps, crop and field data, and the status of integrated equipment can all be monitored and documented. A customizable dashboard can keep track of all the parameters that have been specified and notify farmers when significant alterations occur. Dashboards allow sensors, irrigation equipment, forecasts, and other data sources to be automatically updated and protected.

The way farmers get their yield analytics is also changing due to big data in agriculture. Their crops can be monitored and documented throughout the season using smartphones, satellites, drones, robots, etc. Analytical tools can then evaluate the yield potential based on the current meteorological conditions, historical data, and data that farmers have entered into the system. An automated yield report generation using data analytics can be the next step for farmers to see the results of their efforts. Based on these yield data, farmers can plan their actions to improve crop management and increase yields.

  • Supply Chain Tracking

A farmer is often tied to a certain supplier or partner in farming and agriculture outside of traditional circumstances. Numerous parties are involved in the agricultural supply chain, and data analytics has proven valuable to streamline operations across the entire chain.

Systems automatically collect and analyze data during manufacturing, monitor equipment performance, and detect problems. For example, farmers might be delivering a portion of their most recent crop to a nearby grocery store or department store chain. It’s impossible to predict exactly how much a crop will yield or when it will be ready, no matter who is involved in farming it. This, coupled with a shift in consumer demand, might result in serious supply concerns.

Data analytics can help mitigate some supply chain issues simply by providing better control over the crops and harvest each season. From growers to wholesalers to packagers to retailers, everyone in the supply chain can benefit from this, not just those working directly with the crops. As long as the data is shared, it can assist everyone adapts the current progress, even if that progress involves more or less than predicted numbers. Preventing the degradation of materials such as seeds, plants, and food is a major concern.

Farmers and suppliers can use data analytics to improve fleet management software to increase the reliability of their deliveries. GPS-oriented analytics and big data tracking solutions, such as smart meters, reduce transportation expenses and provide a sophisticated mapping of animals’ and vehicles’ whereabouts.

  • Avoiding Environmental Challenges

Environmental issues that affect the agri-business supply chain include unpredictable weather, violent storms, drought, and changing insect behavior. However, farmers can benefit from using data analytics to understand environmental trends better so that they can better plan for problems and take advantage of opportunities without wasting resources.

Crop health can be monitored in real-time using data analytics; predictive analytics can predict future yields while facilitating resource management decisions based on proven trends.

Data science can increase yields in the future by putting historical and current data into a system and extracting insights using appropriate algorithms. As a result, farmers and supply chain stakeholders can save money and improve distribution patterns and supplies.

Land patterns can be surveyed using drones or crewless aerial vehicles (UAVs). Once the mapping data has been gathered, it can be examined and searched for usable information.

  • Optimize Pesticides Usage

Pest infestations and crop diseases are a few examples of the myriad dangers farmers face that they have no control over. Until the onset of data analytics, farmers could not foresee early warning signals of failing crops; hence it was too late to do something about it. To determine if crops are at risk from illness, scientists can use data analytics to look for indicators of disease in plants and make predictions about future harvests.

Secondly, since pesticides negatively affect the ecosystem, many view them as problems. Data analytics can help farmers judge when and where to apply pesticides, making more informed decisions about which pesticides to use. As a result, it is easier for food producers to avoid overusing chemicals. Farmers’ revenues are also boosted when wasteful pesticide usage is reduced.

  • Food Security

Customers can have greater confidence in the safety and security of their products due to the insights provided by data analytics. Sensors, drones, and phones collect data at precise points in smart agricultural facilities and the fields. And one of the challenges solved by modern farming is the ability to detect bacteria and indicators of contamination quickly.

Data analytics allows organizations to collect and analyze high-resolution data on humidity, temperature, chemicals, and other variables. Consumers can find out where and how items were cultivated, transported, and processed with the help of data. It’s yet another incentive for producers and logistics agencies to keep quality high.

  • Risk Assessment

Data analytics is used in farming risk assessment for benchmarking, monitoring, analytics, and predictive modeling. Applying these methodologies to make predictions employing big data can aid farmers in modeling and managing risks linked with producing livestock and growing crops.

Smart contracts backed by big data and built on a blockchain platform are another prominent combination in agricultural risk assessment nowadays. Such a method changes the complex framework into speedier and automated systems in agriculture insurance.

Insurance companies want to be clearer about what they’re paying for, and farmers want to make their businesses more resilient. Smart insurance policies deal with various risks, including natural disasters. Insurers subsequently calculate a premium based on the probability of a specific weather occurrence and its impact on animals or crops at a given period. Whenever the number of incidents surpasses a predetermined level, farmers are reimbursed accordingly.

Bottom Line

An integrated cloud-based ecosystem is necessary for the agricultural sector to benefit from big data technology properly. Farmer-friendly technologies should be able to gather information on anything from climate change to agronomy to water to farm equipment to supply chain to weeds and fertilizers.

Any business purpose or goal can benefit from data analytics. Because it leverages the data mentioned above to produce actionable steps, it is best to utilize business intelligence for decision-making.

Ascend accomplishes this by providing a single set of predictive analytics and data analytics consulting services. It allows you to stream data in real-time from many sources and aids in the extraction of critical insights based on high-quality data.

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  • What role will IoT play in agriculture in the future?

According to a BI Intelligence survey, the adoption of IoT devices in the agriculture business will reach 75 million by 2020, expanding at 20% each year. Simultaneously, the worldwide smart agriculture market is predicted to quadruple by 2025, reaching $15.3 billion (up from slightly more than $5 billion in 2016).

  • What is the most up-to-date agricultural technology?

Agriculture in the future will rely heavily on advanced technologies such as robotics, temperature and moisture sensors, aerial photographs, and GPS. Farms will be more profitable, efficient, safe, and environmentally friendly due to modern equipment, precision agriculture, and robotic systems.

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