Amid the buzz surrounding cutting-edge artificial intelligence tools like ChatGPT, the complexities of AI regulation, and concerns about superintelligent machines, it's important to recognize that Artificial Intelligence is a valuable asset across various sectors. It holds significant potential for advancing human well-being.
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In agriculture, farmers are increasingly adopting AI-driven technologies to address pressing issues related to human health, environmental sustainability, and food security. Researchers project that the market for these tools could grow to $12 billion by 2032.
As a researcher focused on agricultural and rural policy, I, Joe Hollis, a PhD student at Iowa State University and the author of this article, identify three key areas of advancement in agricultural AI: federated learning, pest and disease detection, and price forecasting.
Pooling Data While Maintaining Privacy
The use of robotics, sensors, and information technology in agriculture is on the rise, aiming to help farmers enhance efficiency and minimize chemical usage. Data gathered by these tools can inform software applications that leverage machine learning to improve management systems and decision-making. However, these applications often necessitate data sharing among various stakeholders.
A survey conducted among U.S. farmers revealed that over half of the participants expressed distrust toward federal agencies and private companies regarding their data. This skepticism stems from concerns about sensitive information being compromised or misused to influence markets and regulations. Machine learning techniques could help alleviate these worries.
Federated learning is one such approach that enables the training of a machine learning algorithm using data from multiple sources without requiring those sources to disclose their information to one another. In this setup, a farmer stores data on a local computer accessible to the algorithm, rather than submitting it to a central server. This process enhances privacy and minimizes the risk of data breaches.
If farmers can be encouraged to share their data in this manner, they can participate in a collaborative system that empowers them to make informed decisions and achieve sustainability targets. For instance, farmers could combine data on their chickpea crop conditions, allowing a model trained on this collective data to provide more accurate yield forecasts than models based solely on individual data.
Identifying Pests and Diseases
The livelihoods of farmers and global food security are increasingly threatened by plant diseases and pests. According to the Food and Agriculture Organization, annual losses from these issues amount to $290 billion globally, affecting 40% of crop production.
To combat outbreaks, farmers often resort to spraying their crops with chemicals. However, the excessive use of these substances is associated with negative impacts on human health, soil and water quality, and biodiversity. Alarmingly, many pathogens are developing resistance to current treatments, while creating new solutions has become increasingly challenging.
Minimizing Chemical Use with AI
Reducing chemical usage is essential, and AI could play a crucial role in this effort.
The Consortium of International Agricultural Research Centers has developed a mobile application called “Tumaini,” which helps identify pests and diseases. Users can upload a photo of a suspected issue, and the AI matches it against a database of 50,000 images. The app not only analyzes the images but also suggests treatment options.
When integrated with farm management tools, applications like this can enhance farmers' ability to target their pesticide applications and make more informed decisions about chemical usage. These efficiencies could lead to decreased pesticide reliance, a lower risk of resistance, and reduced environmental and health risks.
Predicting Prices with AI
Market fluctuations and changing prices significantly impact farmers' investment decisions and crop choices, often causing hesitation regarding new ventures.
AI can mitigate this uncertainty by forecasting market prices. Companies like Agtools, Agremo, and GeoPard offer AI-driven tools that enable real-time analysis of pricing and market trends, helping farmers make data-informed decisions.
Access to this information allows farmers to respond promptly to price shifts and plan more effectively. As farmers' economic resilience improves, they are more likely to invest in innovative opportunities and technologies that benefit both their operations and the broader food system.
Harnessing AI for Positive Change
Human innovation has always produced both advantages and disadvantages. The risks associated with AI are evident, including biased algorithms, privacy concerns, and the potential manipulation of human behavior. However, AI also holds the promise of addressing many critical challenges.
The applications of AI in agriculture inspire optimism among farmers. By showcasing the benefits of these technologies and establishing robust frameworks to mitigate potential drawbacks, the agricultural sector can leverage AI to lessen its impact on human health and the environment while enhancing global food security in the 21st century.