The Philippine Star
November 10, 2024 | 12:00am
From bytes to bushels
MANILA, Philippines — Global demand for nutrition continues to increase, creating new economic pressures — and opportunities — for farmers. At the same time, the agriculture industry must contend with the push toward more sustainable practices.
The emergence of rapidly evolving technologies, such as artificial intelligence (AI), offers agriculture players another powerful tool to meet these challenges head on and unlock greater efficiency and effectiveness throughout their businesses. Generative AI (gen AI), in particular, has captured the imaginations of many leaders in agriculture and beyond and could be the impetus to create significant change.
It has also brought to light the application of many other, long-existing approaches, such as analytical AI, with proven use cases and still relatively low levels of adoption.
Applying gen AI in agriculture
Generally speaking, “gen AI” refers to applications that process large and varied sets of unstructured data, including geospatial and weather data, and perform more than one task.
In this way, gen AI can generate new ideas by identifying patterns in large unstructured data sets, particularly when it comes to complex tasks such as molecular research, marketing or agronomy and code generation.
By contrast, analytical AI typically solves specific tasks by making predictions based on well-structured data sets and predefined rules. Examples here include forecasting sales, segmenting customers and conducting sentiment analysis.
Agriculture is particularly well suited for disruption by AI and gen AI because of its high volumes of unstructured data, significant reliance on labor, complex supply chain logistics and long R&D cycles, as well as the sheer number of farmers who value customized offers and low-cost services.
As an example, gen AI can develop testing scenarios by synthesizing millions of data points on weather, soil conditions, and pest and disease pressure and analytical AI models can then simulate those scenarios. Using both technologies in tandem has the potential to increase efficiencies, lower costs and improve environmental impact for all agricultural players.
The significant value at stake AI can create significant value for agriculture in two key areas: 1) on the acre, which refers to crop and livestock production and 2) for the enterprise, which refers to business functions.
On the acre
AI and gen AI can help optimize the use of inputs and manage labor efficiently. For example, gen AI–enabled virtual agronomy advisers, which mine data sets such as weather, soil conditions, and pest and disease pressure, can help farmers make better informed decisions to improve yields.
Up to half of the value at stake will be driven by such solutions for better yield management. Additional value will be driven by reducing labor costs via autonomous solutions to enhance the existing workforce and reduce dependency on labor in operations, as well as by input cost savings via new insights and data handling for precision agriculture to optimize inputs and reduce waste.
It remains unclear which agriculture players will take the lead and create products and services that combine analytical AI and gen AI for farmers.
Gen AI technologies are more accessible than ever before, which means there is increased potential for startups and new entrants to capture value on the acre from larger companies. In any case, players across the value chain will need to move quickly in the months and years ahead.
For the enterprise
Across use cases, the combination of analytical AI and gen AI can create additional value by driving functional efficiency gains. The majority of this value will likely be enabled by analytical AI and complemented by solutions that are enabled or enhanced by gen AI.
On this point, many organizations have historically focused their AI solutions on support functions, while our research shows that analytical AI and gen AI use cases add the largest value to core functions, such as R&D and products, marketing and sales, agronomy and sustainability and operations.
– McKinsey and Co.
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