To date, the sole method of determining whether weevils have infested a tree has been a visual and acoustic assessment of individual trees. Such a process is laborious – time consuming and impractical for large-scale municipal monitoring. Dr. Michael Fire works at the Department of Software and Information Systems Engineering (BGU) and head of the Data4Good lab. Dr. Fire lives in a little town with multiple palm trees on almost every street. In fact, Dr. Fire has one in his front yard. He knew he had one, but the municipality had no record of such a tree, Dr. Fire discovered. The municipality did send someone to spray the tree to prevent it from infection. For most people, that would be the end of the story – my tree protected. But Dr. Fire is not most people. “I started thinking, what if I could help the municipality by developing a way for them to monitor all of the palm trees?” explains Dr. Fire.
Dr. Fire took his individual tree problem back to his team, Dima Kagan and Dr. Galit Furhmann Alpert, and they devised a global monitoring solution. First, they collected Google aerial and street view images of palm trees. Then, they trained three deep learning models: one to detect palm trees in aerial images, one to detect them in street view images and a third – an infected palm classifier. They analyzed more than 100,000 images to build their models. They tested their detection system on photos of San Diego, CA because they knew there had been an infestation there in 2016. They were able to find three out of four of the infected trees noted in a study from 2016. They also successfully located trees in Israel and in Miami-Dade county in Florida.