Artificial Intelligence in Agriculture: Impacts and Opportunities titles image with two photos of AI uses on a farm

Artificial Intelligence in Agriculture: Impacts and Opportunities

July 01, 2024 Written by Kofi Britwum –Assistant Professor of Farm Management (

Transforming agriculture


As artificial intelligence (AI) continues to evolve, its potential to transform agriculture is expanding, offering a myriad of opportunities. Over the past century, technology has profoundly shaped U.S. agriculture, long before the advent of AI. To put some of these impacts in perspective, consider the US in 1880, a time when half of the working population worked on farms. By 1920, this number had decreased to 30%, and by 1980, less than 3% of the population was involved in farming, overall paralleling advances in technology and consequent structural shifts in the economy.  As of 2023, less than 2% of the population worked directly on farms.


A photo a the edge of harvested wheat field on a cloudy day with two barns in the distance


Challenges and solutions


Despite the significant influence of technology, new challenges in agriculture require solutions that neither technology nor policy has yet fully resolved. For instance, persistent farm labor shortages remain inadequately addressed, demonstrating that technology cannot fully replace certain aspects of the agricultural workforce. Additionally, climate change and variable weather patterns pose threats to agriculture, impacting crop yields and farmer profitability. These may be areas where AI could make a difference.

Driven in large part by volumes of data, AI now stands on the horizon of modern technological advancement, with a potential that is simultaneously promising but also alarming to some. The full scope of AI’s impacts across economic sectors and on society as a whole is still unraveling. In the realm of farming, AI is enhancing the benefits of current agricultural technologies, propelling farming into a new era of efficiency and productivity. From precision farming to weather forecasting, crop health monitors, fertilizer application, spraying, pest management, nutrient management, and produce sorting, AI is optimizing resource allocation. These have largely been enabled by drones and GPS technology. AI's impact is understandably not confined to crop agriculture; in dairy farming, sophisticated algorithms enable robotic milking, tracking of cow health and behavior, and the provision of real-time data to farmers. In the poultry industry, AI can optimize feed consumption, identify disease outbreaks, and help with key metrics in poultry houses, such as temperature, humidity, and water quality. The ubiquity of smartphones and personal computers will be crucial in leveraging AI.


A photo of a drone spraying a specific section of the cornfield

While these interventions should inarguably reduce costs and increase yields, the reality may be slightly more nuanced. Take, for example, the labor shortages in US agriculture, which remain a perennial problem. This is further compounded by an aging farmer population across the nation and a hesitant next generation to take over the reins of farming. With AI-enabled technologies, this challenge can be alleviated to some extent. In particular, AI-enabled self-driving tractors and combines, robotic harvesters, and drones can address labor shortages, although farmers will need to balance these benefits against the costs of these technologies. This will also potentially shift farm labor needs from manual to a more skilled workforce that is able to maintain, operate, and manage AI systems and machinery. At this same time, this can pose a threat to existing manual agricultural labor, with potential impacts across rural communities, some of which are already struggling with depopulation.


Agricultural marketing


AI benefits can also be instrumental in agricultural marketing. For example, using predictive analytic tools, AI can serve as an important decision support tool, helping farmers identify optimal prices to lock in contracts or suggest time of sale. Commodity prices are influenced by various factors such as prevailing basis, exports, harvest periods, and the time of year, among other factors. By utilizing AI with data on these variables, models can be developed to predict market trends with specific probabilities. This will add a level of certainty to farmers' marketing strategies, aiding them in making better-informed decisions. While this development could be exciting to commodity brokers by enhancing their ability to advise their clients, it also carries the risk of job displacement. This will depend on how rapidly and widely farmers adopt these decision tools, and the extent to which tasks traditionally performed by brokers are automated, potentially reducing demand for certain roles.

Another key benefit of AI is its outsized impact on production-related decision-making processes. At its core, farming—particularly crop agriculture—revolves around the essential activities of planting and harvesting, with productivity being pivotal to farm profitability. However, extreme weather situations are making growing conditions increasingly unpredictable. Using relevant data, AI can be instrumental in forecasting optimal planting and harvesting times, as well as predicting crop yields in advance. This capability can substantially reduce production uncertainty associated with adverse weather conditions, minimize the risks of crop damage, and improve overall productivity. 


a view of the edge of a cornfield with the sun setting

The promise of AI’s impact in agriculture is enormous. Like any emerging technology, though, AI comes with its share of costs and risks. While some challenges, such as high initial expenses and job displacement, are foreseeable and expected, the unforeseen drawbacks warrant careful consideration.


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