by From attention to flashy new artificial intelligence tools like ChatGPT, the challenges of regulating AI, and doomsday scenarios for super-intelligent machines, AI is a useful tool in many areas. Indeed, it has enormous potential for the benefit of mankind.
In agriculture, farmers are increasingly using AI-powered tools to tackle challenges that threaten human health, the environment and food security. Researchers predict that the market for these tools will reach US billion by 2032.
As a researcher studying agricultural and rural policy, I see three promising developments in agricultural AI: associative learning, pest and disease detection and price forecasting.
Pooling data without sharing it
Robotics, sensors and information technology are increasingly used in agriculture. These tools aim to help farmers improve efficiency and reduce chemical use. In addition, data collected by these tools can be used in software that uses machine learning to improve management systems and decision making. However, these applications typically require data sharing among stakeholders.
A survey of US farmers found that more than half of respondents said they do not trust federal agencies or private companies with their data. This lack of trust is linked to concerns about sensitive information being compromised or used to manipulate markets and regulations. Machine learning could reduce these concerns.
Federated learning is a technique that trains a machine learning algorithm on data from multiple parties without the parties having to reveal their data to each other. With federated learning, a farmer places data on a local computer that the algorithm can access rather than sharing the data on a central server. This method increases privacy and reduces the risk of compromise.
If farmers can be persuaded to share their data in this way, they can contribute to a collaborative system that helps them make better decisions and achieve their sustainability goals. For example, farmers could pool data on the conditions of their chickpea crops, and a model trained on all of their data could each provide better forecasts of their chickpea yield than models trained on their own data only.
Detecting pests and diseases
Farmers’ livelihoods and global food security are increasingly at risk from plant diseases and pests. The Food and Agriculture Organization estimates that annual global losses from diseases and pests represent 40% of global crop production.
Farmers usually spray crops with chemicals to prevent outbreaks. However, overuse of these chemicals is associated with adverse effects on human health, soil and water quality and biodiversity. Worryingly, many pathogens are becoming resistant to existing treatments, and developing new ones is difficult.
It is therefore vital to reduce the amount of chemicals used, and AI can be part of a solution.
The Consortium of International Agricultural Research Centers has created a mobile phone app that identifies pests and diseases. The app, “Tumaini,” allows users to upload a photo of a suspected pest or disease, which the AI compares to a database of 50,000 images. The app also provides analysis and can recommend treatment programs.
When used with farm management tools, apps like these can improve farmers’ ability to target their sprays and improve accuracy when deciding how much chemical to use. Ultimately, these efficiencies can reduce the use of pesticides, reduce the risk of resistance and prevent harmful consequences for humans and the environment.
Crystal ball for prices
Market volatility and fluctuating prices affect how farmers invest and decide what to grow. This uncertainty can also prevent farmers from taking risks on new developments.
AI can help reduce this uncertainty by predicting prices. For example, services from companies such as Agtools, Agremo and GeoPard offer AI-powered farm decision tools. These tools allow for real-time analysis of price points and market data and provide farmers with data on long-term trends that can help optimize production.
This data allows farmers to respond to price changes and allows them to plan more strategically. Improving farmers’ economic resilience will increase the likelihood that they can invest in new opportunities and technologies that will benefit both farms and the larger food system.
AI for good
Human innovation always has winners and losers. The dangers of AI are clear, including biased algorithms, data privacy breaches and manipulation of human behaviour. However, it is also a technology that has the potential to solve many problems.
These uses for AI in agriculture are cause for hope among farmers. If the agriculture industry can promote the utility of these inventions while developing robust and sensible frameworks to minimize harms, AI can help reduce the impact of modern agriculture on human health and the environment while helping to improve global food security in the 21st century.
This article is republished from The Conversation, a non-profit, independent news organization that brings you reliable facts and analysis to help you make sense of our complex world. The Conversation has a variety of free newsletters.
It was written by Joe Hollis, Iowa State University.
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Joe Hollis does not work for, consult with, own shares in, or receive funding from any company or organization that would benefit from this article, and He has disclosed no relevant affiliations beyond his academic appointment.
