For all the attention on flashy new artificial intelligence tools like ChatGPT, the challenges of regulating AI, and doomsday scenarios of superintelligent machines, AI is a useful tool in many fields. In fact, it has enormous potential to benefit 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 market for these tools to reach 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 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.
If farmers can be persuaded to share their data this way, they can contribute to a collaborative system that helps them make better decisions and meet their sustainability goals. For example, farmers could pool data about conditions for their chickpea crops, and a model trained on all of their data could give each of them better forecasts for their chickpea yields than models trained only on their own data.
For all the attention on flashy new artificial intelligence tools like ChatGPT, the challenges of regulating AI, and doomsday scenarios of superintelligent machines, AI is a useful tool in many fields. In fact, it has enormous potential to benefit 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 market for these tools to reach 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 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.
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