Not only is the olive tree an important source of food and livelihood for millions of people around the world, but it is also deeply ingrained in the cultural heritage of many Mediterranean countries. Unfortunately, in recent years, olive groves around the world have been under serious threat from an infectious disease called Xylella fastidiosa.

This insect-borne bacterial pathogen can be devastating to olive trees and other crops, leading to severe economic and environmental consequences. Phytopathologists say that once such bacteria are found, they must be killed to prevent the disease from spreading on a wider scale. But the growers were very much opposed to the destruction.

Scientists are figuring out how to fight Xylella without killing every infected tree.

One of the most significant challenges in battling this disease is the lack of a reliable and efficient way to detect and diagnose an early infection. This is where artificial intelligence (AI) comes into play, offering new solutions for monitoring and predicting the spread and severity of Xylella fastidiosa.

Artificial intelligence techniques such as machine learning and computer vision can analyze vast amounts of data from satellite imagery, drone footage, and ground-based sensors to provide real-time insight into the health of olive orchards and identify potential outbreaks of disease.

Metabolic data for trees was screened using machine learning algorithms. Xylella makes complex fatty acids called lipids as key signaling molecules, while trees make their own lipids in response to infection. Algorithms were used to compare lipid profiles as well as infection status, tree species, and whether each tree had been treated with Dentamet, a metal mixture that relieves Xylella symptoms but does not cure them.

By examining subtle changes in leaf color and texture, AI algorithms can detect signs of stress and damage that could indicate infection even before visible symptoms appear.

Moreover, AI can also help predict the severity of the disease in different areas, allowing farmers and policymakers to prioritize their resources and take proactive measures to limit its spread. By analyzing data on climate, soil conditions, and other environmental factors, AI models can estimate the likelihood of Xylella fastidiosa infection and forecast the impact it may have on local olive trees.

The potential benefits of AI in diagnosing and predicting the spread of Xylella fastidiosa are enormous. By providing early detection and accurate forecasts, AI can help prevent the spread of the disease and minimize its economic and environmental impact. This, in turn, can help preserve the livelihoods of farmers and protect the cultural heritage of entire regions that depend on olive trees.