When one thinks of agriculture, what can typically comes to mind are tractors, animals, and fields of crops as far as the eye can see. It often appears simplistic. But in reality, there are pieces of technology and data touching each facet of agriculture.
Some machinery has GPS technology capabilities, there are unoccupied aerial vehicles (UAVs) flying over plants and animals, and moisture sensors help automate irrigation in huge fields. All are powered by and rely on the big data used to deploy these technologies. The opportunities and benefits of big data in agriculture are endless, as agriculturists have been working nonstop to meet the imminent population increase of over 2 billion people by 2050.
As if the increase in population wasn’t daunting enough, agriculturists are met with additional challenges. There are unprecedented changes in climate and the same or fewer acres for planting, are two of these challenges. But with the help of big data, these goals are within reach. Big data in agriculture, or larger and more complex datasets, are information that is used to provide predictive insights into future farming outcomes and drive real-time operational decisions. Big data in agriculture can include rainfall patterns for an area or region, fertilizer applications, satellite imagery for disease monitoring or animal movement monitoring, light control, automated irrigation, and so much more!
Big Data Components
Big data requires data integration, management, and analysis. With integration, big data comes together from various sources (such as multiple researchers or across various years) and is then formatted cohesively. For the management of big data, lots of storage is necessary. The storage and computation capabilities will come in handy to make sure the big data are accessible for the final step. With analysis, you can explore the data at new depths to develop meaningful insights and models for prediction. Many platforms are available that aid in the integration, management, and analysis of agricultural big data.
Benefits of Big Data
As mentioned before, big data has a place in all areas of agriculture. From animal science to agronomy, agricultural business, and more, big data can positively impact and play a role in your production. Gone are the days when producers have to uniformly, rely on intuition, or blindly apply treatments to their fields, instead they can tailor each action to what is needed by the plant or animal based on the data they have available to them. For example, for those in crop production, some of the benefits include:
- Close monitoring of field nutrient levels allows farmers to more efficiently apply fertilizers, minimizing runoff and wasted money on resources. These fertilizers can also help increase overall plant yield and production.
- Farm equipment with GPS technology, remote capabilities, automated steering, and precision planting provide mounds of data to help reduce costs, reduce inefficiencies, ensure safer working environments, and optimize time spent in the field.
- Plant sensors help farmers measure plant nutrient use and other metrics that can show signs of contamination. Being able to detect the signs of negative microbes and contamination will assist with food safety and minimize food waste.
- Using machine learning and big data, researchers are able to predict and accurately estimate plant yield and performance.
- The ability to track and optimize delivery routes help close food delivery cycles and minimize food that might have been wasted in longer transits.
As more and more data is being collected at the production level, this presents opportunities to improve the efficiency and success of these processes. Future and forward planning, remote machine management, digital farm management, precision agriculture, and increase employee productivity and safety are some of the areas that benefit from big data.
Technology Aiding Big Data Collection
While on the farm, the collection and acquisition of big data can come in many forms. From pencil and paper to cell phone and tablet applications, in-field sensors, computers in machinery, and robotics, data are collected at each level of production. The implementation of precision agriculture and technology creates more efficient, environmentally friendly, and overall safer environments.
Being able to use previously collected data to develop models for prediction means that fewer people are needed for the labor-intensive component of data collection and utilizing robotics helps collect data that might be otherwise dangerous for workers. There has been an increased interest in creating and disseminating technology for agricultural uses for these exact reasons.
Big Data Can Enable Sustainability
The interconnected forces of population growth, consumption growth, environmental change, and food security, are all pressures that have the capability of putting strain on the environment. Some opportunities to ensure sustainable production would be to develop data-driven systems that optimize water and chemical usage, identify new practices that are harmless or beneficial to the environment, and understand market trends and procurement.
Preserving the natural resources that we have is extremely important, but also minimizing the harmful activities and practices that have historically been used. This shift will require evaluating trends over decades of production, supply and demand, and other areas of big data that are all related to agriculture production. More and more, the commitment to sustainability and transparency in production is becoming more important to consumers and is becoming more attainable by growers.
Future of Big Data in Agriculture
With the growing population comes a need to provide the necessary amount of food for everyone. Because this is something that’s never been done before in history, big data will be integral in helping make it possible. Utilizing large datasets will enable farmers and researchers to identify how to optimize resource allocation such as fertilizer or pesticide applications, predict crop yield, and manage their equipment.
As Lindsay Suddon said, “Now is the time for the [agriculture] sector to harness the power of data and work together to achieve increases in productivity, profitability, sustainability, food safety, and security. It won’t just be the challenges of climate change forcing the issue, it shall be the increasing groundswell of global consumer sentiment and demand for change too.” The future of big data in agriculture is promising, potentially improving nearly every aspect of this vital sector.
Agmatix drives the agronomic innovation cycle from research and experimental data into meaningful real-life action. Using proprietary machine learning algorithms, Agmatix can read and interpret thousands of data points commonly used across the agricultural industry to help scientists, agronomists, and farmers make better decisions to increase yields and crop quality. Combining a wealth of research, real-world data, and key industry insights within a smart, cutting-edge system, the potential is vast.
Ron Baruchi is the president and CEO of Agmatix. With over 20 years of experience in the technology sphere, Ron has taken this experience to the agricultural sector. Passionate about using data to solve complex problems, he has used his expertise in technology with Agmatix to improve crop yields and quality while limiting environmental impact.