Technological advancements in the agricultural sector became possible due to numerous factors, big data being one of them. To enhance the productivity of individual farms and to help mitigate the global food crisis, collecting and analysing big data is the key. These data help produce more food, using less land; and to achieve this goal, policymakers and industry leaders require technical assistance in the form of modern innovations such as big data, Internet of Things (IoT), analytics, and cloud computing.
According to a report put forward by the United Nations, the global population will reach 9.8 billion by 2050; hence, crop production should be enough to feed the growing population. But urbanisation and drastic climate changes have impacted the share of farmlands significantly, and the world is left with an urgent need to produce more food but less land to grow it on.
In this blog, we have talked about how beneficial big data is for the agricultural sector.
What is Big Data?
Data comprising an expansive variety, flowing in increasing volumes, with more velocity is called big data. Using big data, farmers can get access to granular data on rainfall patterns, water cycles, the requirement of fertilisers, and more such information. These data help farmers make data-driven and smart decisions, for example, which crop to plant and what profitability they can expect from that crop. Making an informed decision would therefore help enhance farm yields.
The industries that benefit from big data are:
- Government: Data flow from sources like sensors, satellites, CCTV, traffic cameras, calls, emails, social media, IT spaces, academia, etc. require efficient storage and analysis for effective governance and management of the public sector.
- Banking: In the banking and insurance sector, big data analytics are used to store data, enhance scalability, and develop business insights.
- Healthcare: Implementing big data protocols helps meet and eradicate the communication problems faced by the healthcare industry.
- Telecom: Analysing big data in real-time is beneficial as it provides useful predictions to reach business insights and strategies like delivering revenue-generating services while considering both mind network and customer requirements.
When it comes to agriculture, big data combines technology with analytics and entails the collection, compilation, and on-time processing of new data to aid scientists and farmers make informed decisions. With the availability of smart devices and sensors that generate an enormous amount of farm data, farming processes are becoming data-driven. Modern-day farming processes have replaced traditional tools with sensor-equipped machines that can collect data from the environment. Technology coupled with big data sources like weather data, market data, etc. is the contributing factors behind the development of smart farming.
To combat the pressures of increasing food demand and climate changes, policy makers and industry leaders require technological backup in the form of the Internet of Things (IoT), Big Data, analytics, and cloud computing.
To facilitate the process, the below-mentioned steps are followed:
- The first step is data collection which is done using the Internet of Things (IoT). Here, the sensors plugged in tractors and trucks, as well as in fields, soil, and plants help with the collection of real-time data directly from the ground.
- In the second step, the analysts integrate large amounts of data with other information (such as weather data and pricing patterns) available in the cloud to determine patterns.
- Lastly, these patterns and insights help in controlling the problems by pinpointing existing issues such as inefficiencies and issues related to soil quality, thereby formulating predictive algorithms that can alert even before a problem occurs.
Application of Big Data in Agriculture
We know that smart farming and precision agriculture help farmers save costs and start new business opportunities.
Listed below are some of the applications of big data:
- To Satisfy Food Demand: The best way to meet the growing demand for food without utilising more land and resources is by using the already existing farmland efficiently to increase yields. Using big data, farmers are provided with information related to weather changes, rainfall, soil moisture, and other factors that impact crop yield. All these data help growers make accurate and reliable decisions, enhancing farm yields.
- Use of Pesticides: Pesticides as we know have side effects on the ecosystem. With big data, pesticide application can be done smartly and precisely, allowing farmers to decide when and where to apply pesticides. Monitoring this data helps food producers limit the use of chemicals and boosts the farmer’s profits by cutting costs on unwanted pesticide use.
- Supply Chain Problems Management: With big data, it is possible to enhance supply chain efficiency by offering delivery trucks scope for tracking and optimisation. Therefore, food delivery cycles from producer to the market become less time-consuming. At the same time, it is ensured that no food is wasted in the process.
- Predicting Yield: As the name suggests, yield prediction is the use of technology and different algorithms to analyse information related to weather, chemicals, vegetation, etc. The objective of such prediction is for farmers to improve their decision-making skills. Using these different technologies, farmers can decide when and where to plant seeds, how to space them properly, when to water them, the amount of chemicals the plantation process would require, and when to harvest. Access to these data reduces manual labour.
- Food Safety: It is known that millions of people get affected by food-related illnesses and diseases. One of the major benefits of modern farming is enabling immediate detection of microbes and signs of contamination. Collecting data on temperature, humidity, and chemicals to access the health of a growing plant helps with instant detection. The availability of different online tools helps with receiving access to previously analysed information collected from the farm. Data can be retrieved from several sources like satellite imagery and ground sensors.
Challenges Faced while Implementing Big Data Solutions in Agriculture
- One of the major concerns in farm management information systems is generating high-quality data, a problem that big data does little to lessen.
- The innovation process gets obstructed due to strict application of data ownership, privacy, and security issues.
- To cater to large volumes of unstructured and heterogeneous data, assistance from domain experts and skilled data scientists would be required.
- Integrating data from every source is a huge task and is an essential requirement for a successful business model.
- Developing affordable solutions for farmers living in developing countries can be challenging.
Big data analytics is the new trend that has impacted some of the notable sectors of the economy and will continue to do so. Although big data applications in the agricultural sector are in the early stages these days, with numerous challenges that need to be addressed, it will be extremely crucial once the full potential will be realised.
If you require more information on the Australian agricultural industry, get in touch with us at KG2, Australia’s largest independent farmer database.