For all the eye on flashy latest artificial intelligence tools like ChatGPT, the challenges of regulating AI, and doomsday scenarios of superintelligent machines, AI is a great tool in lots of fields. In fact, it has enormous potential to learn humanity.
In agriculture, farmers are increasingly using AI-powered tools to tackle challenges that threaten human health, the environment and food security. Researchers forecast the marketplace for these tools to succeed in US$12 billion by 2032.
As a researcher studying agricultural and rural policy, I see three promising developments in agricultural AI: federated learning, pest and disease detection and forecasting prices.
Pooling data without sharing it
Robotics, sensors and data technology are increasingly utilized in agriculture. These tools aim to assist farmers improve efficiency and reduce chemical use. In addition, data collected by these tools could be utilized in software that uses machine learning to enhance management systems and decision-making. However, these applications typically require data sharing amongst stakeholders.
A survey of U.S. farmers found that greater than half of respondents said they don’t trust federal agencies or private corporations with their data. This lack of trust is linked to concerns about sensitive information becoming compromised or getting used to manipulate markets and regulations. Machine learning could reduce these concerns.
Federated learning is a method that trains a machine learning algorithm on data from multiple parties without the parties having to disclose their data to one another. With federated learning, a farmer puts data on an area computer that the algorithm can access relatively than sharing the info on a central server. This method increases privacy and reduces the danger of compromise.
If farmers could be persuaded to share their data this fashion, they will contribute to a collaborative system that helps them make higher decisions and meet their sustainability goals. For example, farmers could pool data about conditions for his or her chickpea crops, and a model trained on all of their data could give each of them higher forecasts for his or her chickpea yields than models trained only on their very own data.
Detecting pests and disease
Farmer livelihoods and global food security are increasingly in danger from plant disease and pests. The Food and Agriculture Organization estimates that worldwide annual losses from disease and pests total $290 billion, with 40% of worldwide crop production affected.
Farmers typically spray crops with chemicals to preempt outbreaks. However, the overuse of those chemicals is linked to harmful effects on human health, soil and water quality and biodiversity. Worryingly, many pathogens are becoming immune to existing treatments, and developing latest ones is proving to be difficult.
Reducing the quantity of chemicals used is subsequently paramount, and AI could also be a part of an answer.
The Consortium of International Agricultural Research Centers has created a cell phone app that identifies pests and disease. The app, “Tumaini,” allows users to upload a photograph of a suspected pest or disease, which the AI compares with a database of fifty,000 images. The app also provides evaluation and might recommend treatment programs.
If used with farm management tools, apps like this may improve farmers’ ability to focus on their spraying and improve accuracy in deciding how much chemical to make use of. Ultimately, these efficiencies may reduce pesticide use, lessen the danger of resistance and stop spillovers that cause harm to each humans and the environment.
Crystal ball for prices
Market volatility and fluctuating prices affect how farmers invest and judge what to grow. This uncertainty can even prevent farmers from taking risks on latest developments.
AI can assist reduce this uncertainty by forecasting prices. For example, services from corporations reminiscent of Agtools, Agremo and GeoPard provide AI-powered farm decision tools. These tools allow for real-time evaluation of price points and market data and present farmers with data on long-term trends that can assist optimize production.
This data allows farmers to react to cost changes and allows them to plan more strategically. If farmers’ economic resilience improves, it increases the likelihood that they will spend money on latest opportunities and technologies that profit each farms and the larger food system.
AI for good
Human innovation has at all times produced winners and losers. The dangers of AI are apparent, including biased algorithms, data privacy violations and the manipulation of human behavior. However, it’s also a technology that has the potential to resolve many problems.
These uses for AI in agriculture are a cause for optimism amongst farmers. If the agriculture industry can promote the utility of those inventions while developing strong and sensible frameworks to attenuate harms, AI can assist reduce modern agriculture’s impact on human health and the environment while helping improve global food security within the twenty first century.
This article was originally published at theconversation.com
