The Future of Agriculture
Discover how data and data science enable extraordinary advances in efficiency, productivity, and scalability across all sectors of the agriculture industry.
Successful businesses thrive when consistently making smarter decisions than their competitors, and the agriculture industry is no exception.
Through the application of artificial intelligence (AI), machine learning (ML), geospatial intelligence, and data science, companies can access increasingly sophisticated data and analytics tools, which enable better decisions, improved efficiencies, and reduced waste in food and biofuel production, all while minimizing negative environmental consequences.
The future of agriculture has never been so bright. There are an abundance of scalable technologies that reduce risk, improve sustainability, and place the grower in the center of predictively informed decisions … the new smart ag is emerging.
MINIMIZE
RISK
Leverage geospatial information systems to analyze unstructured data, identify opportunities, prescribe action, and even predict future conditions.
Optimize
Opportunities
Expand your market with advanced modeling algorithms that reveal patterns in consumer behavior, providing insights that enable you to strengthen customer engagement.
CULTIVATE
SUSTAINABILITY
Analyze and optimize water and soil resources, minimize negative environmental consequences of pesticides and crop disease treatments, and improve supply chain logistics.
CASE STUDY
Measuring Agricultural Carbon Intensity
Industry-Specific Expertise
CROP PROTECTION and forecasting
Many plants have similar leaf compositions, colors, and shapes, making it difficult to label them using the human eye. Farmers can now rely upon ML to assess complex patterns and accurately identify related plant and weed species.2 Digital identification of plant species saves farmers time, allowing them to increase productivity in other critical areas.
ML-driven image processing allows farmers to rely upon digital tools to recognize weed species and to determine which crops are healthy and which ones are infested with disease caused by fungi, bacteria, or viruses.
The ability to identify weeds with digital tools makes it possible to train mechanical devices (robots) to pull weeds from fields, protecting the environment from damage caused by pesticide use and saving farmers time, effort, and money.
Additionally, digital applications that can evaluate crops for disease can also provide an accurate disease diagnosis and recommend an optimal treatment plan.
This technology helps farmers avoid settling on a one-size-fits-all solution that not only fails to address a specific disease, but may inadvertently cause undesirable side effects, such as pollution or bee-population reduction. It also allows the companies that manufacture crop disease treatment products to better serve their customers.
YIELD PREDICTION AND QUALITY ASSESSMENT
Through the application of ML technology, a farmer can log into a customized dashboard on a computer or tablet and access an accurate assessment of the harvestable versus non-harvestable acres on a given day. The weight and maturity of harvestable crops can also be measured and predicted.
Additionally, using a variety of technologies, including image analysis, crops can be evaluated both before and after harvest for the presence of desirable features, extent of damage (if applicable), nutritional makeup, and other factors that may impact the ultimate viable yield and product price.
Field OPERATIONs and SUPPLY CHAIN
Thousands of fields across the United States corn belt are harvested annually.
When it comes to routing equipment and teams to fields that are ready for them, there is a limited window of opportunity. Having the right equipment and manpower in the right place at the right time for optimal scouting presents a significant challenge for large agricultural operations with vast acreages to monitor and manage.
Our operations research team uses math to find the best solution to any decision-based problem. This translation act from real-world desires, restrictions, and requirements to a representative model that can be reasoned about mathematically not only ensures that solution is feasible, but often that no better (more optimal) solution exists.
By systematically generating, evaluating, and comparing solution alternatives, we are able to met all constraints, identify a preferred option, and produce a clear roadmap on how to achieve this result.
Today's Agriculture professionals rely upon Data-Driven Solutions To Improve:
- Field Productivity and Scouting
- Sustainable Practices
- Harvest Logistics
- Crop Protection Recommendations
- Traits Discovery and Biotechnology
- Commodity Price Forecasting and Strategy
- Yield Maps and Visualizations
- Data Quality Detection
- Customer Segmentation
- Market Insights and Reports
- Business Modeling
CUSTOMER INTELLIGENCE
Our experienced engineers have built a system of machine-learning algorithms that consolidate and process data from multiple sources, including geospatial and weather data, to perform high-resolution imagery and vector analyses for hundreds of fields throughout the Midwest.
The results can then be shared with customers through a modern digital portal that connects up-to-the-minute field data to analytical models that deliver dynamically-calculated, effective insights. Our platform and component portfolio enables the data and operations research teams to develop customer models tailored to high performance requirements. We've created some of the world’s leading blockchain and DLT solutions and have a broad portfolio of concepts and reference architectures to accelerate value creation.
Biotechnology
WATER AND SOIL MANAGEMENT
Through ML-assisted analysis of precipitation and evapotranspiration (the process by which water transitions from soil and plant transpiration to the atmosphere), technologists develop more efficient resource management procedures and irrigation systems.2
ML is equally well equipped to analyze data regarding soil conditions, including moisture level, temperature, and chemical makeup, all of which have an impact upon crop growth and livestock well-being.
PLANT BREEDING
As with humans, plants' characteristics are determined by their genes. Certain genes help plants absorb water and nutrients better than others, while others help them fight disease more effectively. Some genes even affect how a plant may end up tasting!
An entire industry revolves around developing commercial seed products that combine the best features of various plant strains.
Without the aid of ML-based technology, a single hybrid development cycle can take scientists seven or eight years (although this is still faster than the speed at which nature performs the process!).
By evaluating masses of data on plant performance in various conditions over time, ML algorithms help scientists better optimize the identification of biotech traits needed to profitably increase yields, given the likelihood of harmful environmental factors, such as unfavorable weather conditions and insect populations, in a given season. This optimized use can also improve the longevity of these hugely beneficial and expensive-to-create biotech traits by reducing resistance buildup.
Simply put, ML helps scientists make predictions regarding which gene combinations will lead to desirable traits in new plants, providing an excellent starting point for developing hardier (and possibly more flavorful!) plant species.
1 Transforming Field Data Into Meaningful Insights | 2 Machine Learning in Agriculture: A Review | 3 Podcast: The Age of Digital Agriculture - Seed Advisor | 4 Herd of AI Startups Milking the Internet of Cows | 5 Using TensorFlow to keep farmers happy and cows healthy | 6 Even sheepdogs aren’t safe: A new robot can herd animals on its own | 7 AI, Machine Learning Blossom in Agriculture and Pest Control
ENGINEERING OUTCOMES
The opportunities to enhance agriculture operations using data science, machine learning, and operations research are by no means exhausted; in fact, we currently stand at the beginning of one of the most impactful chapters the agriculture industry has ever seen.
Information – and more importantly insight – is essential to modern agriculture, from the creation of new hybrid and varietal products, to the placement of products in the correct management zones, to capturing value at harvest time.
As technology advances, industry experts and technologists are working together to imagine solutions that were nothing but science fiction even a few years ago.
Is it time to reimagine what's possible for your business?
Contact us today to explore the many ways data science, artificial intelligence, and machine learning positively impact agricultural progress.
