AI could detect HLB before visible symptoms, study finds

One of the citrus industry’s biggest foes is getting a taste of AI’s might. Huanglongbing (HLB), also known as citrus greening disease, attacks the plant tissue responsible for transporting nutrients, thus causing progressive and irreparable damage to trees.

Because leaves often show the first signs of attack, Chinese scientists at Guilin University of Technology and the Guangxi Academy of Specialty Crops have developed a model that identifies changes in their structure. The achievement could enable early detection and provide much-needed help to citrus growers worldwide. 

Photo by Enzo Lo Presti via Unsplash

The project integrates three types of information: standard color photographs, hyperspectral images, and leaf chlorophyll measurements.

Hyperspectral imaging captures how leaves reflect light at different wavelengths, potentially revealing changes that conventional cameras cannot detect. Researchers also used a soil-plant analysis development (SPAD) meter to estimate chlorophyll content, another indicator of plant health.

The model scored nearly 98 percent average accuracy when classifying five levels of HLB severity, outperforming approaches based on standard photographs or hyperspectral imaging alone. 

The study evaluated 2,700 citrus leaves from healthy trees and trees representing five HLB disease levels, including infections without visible symptoms.

Leaf health could hold the key to HLB defense

Early detection is a major challenge in managing the disease, because infected trees may not initially display obvious symptoms. HLB has decimated citrus groves in Florida and other producing countries such as Brazil.

A reliable early-detection tool could help growers improve surveillance and make more targeted disease-management decisions, as quarantining infected trees is key to preventing spread.

Photo by Mrtiger via Unsplash

However, the research remains at an experimental stage. Additional field trials, testing across additional citrus varieties and developing methods to assess entire tree canopies, will be priorities for future research.

*Main photo by Arya Dubey via Unsplash

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