Data driven decision making gets real with generative AI
Generative AI is fueling a new generation of data-driven companies – get tips for incorporating generative AI into your business.
Imagine a grower’s field manager opens a generative AI app, which is designed specifically for agribusiness technology use cases, and via the app’s chat function, types out a question.
“Taking into account the current maturity of my soybean crop, the pending weather micro-forecast, recent precipitation, and the soil condition of this particular field, when is the optimal time next week to irrigate and spray this field, and in which volumes?”
In moments, the grower has a detailed answer based on the parameters they provided and derived from an analysis of a huge volume of data from disparate sources, including the seed provider, fungicide manufacturer, connected sensors in the field, historical weather and precipitation data, and more.
As farfetched as a scenario like this might have seemed just a few years ago, it’s become not just a possibility, but a real option for agribusiness companies seeking to optimize crop inputs based on today’s realities and priorities. Those realities include climate change, disruptions related to extreme weather, wars, manpower shortages, carbon footprint goals and compliance requirements, and volatile commodity prices.
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The benefits of agribusiness technology
Intelligent technologies driven by data are already helping companies make better decisions about crop inputs, which in turn can lead to more efficient, resilient and sustainable production practices and ultimately, more robust yields and profits.
Eventually, they could revolutionize the agricultural and food industries by helping growers better manage risks, from weather and soil health to sustainability and water scarcity.
In southern Africa, for example, Royal Eswatini Sugar Corp. (RES), a company that produces sugar for use in beverages, ethanol, and other products, has implemented genAI- and machine learning-powered systems to better manage farm and field data, automate tasks, inform its own farms and third-party growers about optimal harvest timing, and provide them with best practices for managing crop inputs to increase yields.
As companies like RES demonstrate, intelligent management of crop inputs can yield a range of benefits. Recognizing these benefits begins by putting four foundational digital elements in place:- Data gathering and standardization capabilities
- Advanced analytics based on machine learning and AI
- Track, trace, and report capabilities
- An integrated, mobile-enabled platform for growers
1. Data: The foundation of intelligent crop management
As vital a resource as data has become to help agribusinesses mitigate risk and operate profitably, it’s become a crop input in its own right.
Much can be gleaned from data, whether the source is connected equipment, sensors in the field, seed and fertilizer manufacturers, or others along the value chain. However, companies often lack standard processes and data management and modeling practices to collect and make efficient use of this data.
That’s begun to change as companies like Agranimo arm more agribusinesses with technology tools to collect and make sense of highly specific data, including current conditions in fields and ultra-local weather information. The data then can be analyzed to trigger various interventions.
With the ability to gather, standardize, and make sense of data from disparate sources inside and outside the organization, an agribusiness can tap into the benefits that intelligent digital tools provide.
2. Leveraging advanced analytics, AI, and machine learning
This fall, farmers could use analytics capabilities from companies like Agranimo to better manage the risks associated with autumn pests like aphids, drawing from local weather data and other inputs to know when conditions are optimal for pest development, which in turn would trigger recommendations for the best treatment window and other specific interventions.
For example, satellite data company Vista GmbH is using satellite data and digital twin models to make crop predictions and optimizations to support its agribusiness customers, And in another use case involving advanced analytics, German seed producer KWS is applying intelligent technology to drone-produced aerial photos of fields to speed up the collection and analysis of data to detect factors such as fungal infestation, soil conditions, and chlorophyll levels.
“In the future, we will get more and more data from the fields. This will help our farming customers optimize the input from fertilizers, water, and crop protection so that they can work as sustainable as possible,” says Jens Hittmeyer, head of global IT and CIO at KWS.
It’s also informing the company’s efforts to develop new disease-resistant plant varieties faster. “Farmers want to use seed that precisely meets their individual requirements,” says Dr. Christoph Bauer, who’s in charge of developing digital phenotyping technology at KWS. New technology tools developed in-house at KWS support breeders in picking the most suitable plants for their work from among the hundreds of thousands of possibilities.
As use cases like these illustrate, genAI models can produce recommendations for planting patterns, fertilization, water usage/irrigation, crop protection, variety selection, harvesting and more, supporting the human decision-making process.
3. Enhancing sustainability with track, trace, and reporting technologies
A 2022 survey by the Food Management Institute and NielsenIQ found that about three-quarters of consumers want food brands and producers to share detailed information about what’s in their products and how they’re made. Meanwhile, new regulations are emerging around the world to require companies to report on the carbon footprint associated with their products.
This means it’s time for agribusinesses to develop capabilities to record and report on crop inputs and other factors that impact carbon footprint, right down to specific farms and fields. Companies could then parlay the strong sustainability performance of their products into more favorable pricing and brand differentiation.
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4. Delivering superior digital experiences to growers
Companies that find ways to keep farmers and growers engaged, productive and profitable give themselves a competitive edge in the marketplace.
One way to do that is by providing them with a superior digital user experience via a single platform with multiple integrated channels for them to easily access information, manage key facets of their business, and interact with your company and others in the value chain.
This type of common, user-friendly process and data backbone, along with the aforementioned capabilities, will be critical to thriving in increasingly volatile agribusiness markets.
