Automatic large scale detection of red palm weevil infestation using street view images

The spread of the Red Palm Weevil has dramatically affected date growers, homeowners and governments, forcing them to deal with a constant threat to their palm trees. Early detection of palm tree infestation has been proven to be critical in order to allow treatment that may save trees from irreversible damage, and is most commonly performed by local physical access for individual tree monitoring. Here, we present a novel method for surveillance of Red Palm Weevil infested palm trees utilizing state-of-the-art deep learning algorithms, with aerial and street-level imagery data. To detect infested palm trees we analyzed over 100,000 aerial and streetimages, mapping the location of palm trees in urban areas. Using this procedure, we discovered and verified infested palm trees at various locations. We demonstrate that computer vision provides an efficient and practical solution for large scale monitoring of infested palm trees. The results indicate that by the use of publicly available online data and without a need of specialized equipment, the proposed framework can be used to automatically map palm trees at large scales and detect ones that are potentially infested. We show that the framework can be effective in both urban and open environments. This method can revolutionize current old school practices for Red Palm Weevil management, proposing much cheaper cost-effective and efficient monitoring.

Automated summary

A novel method for surveillance of Red Palm Weevil infested palm trees using deep learning algorithms and aerial/street-level imagery has been proposed, demonstrating efficiency in detecting infested trees in urban and open environments through large-scale testing.