Automatic Large Scale Detection of Red Palm Weevil Infestation using Aerial and Street View Images

Abstract

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 street-images, mapping the location of palm trees in urban areas. Using this procedure, we discovered and verified infested palm trees at various locations.

References in corpus (8)

• On Calibration of Modern Neural Networks
• fastai: A Layered API for Deep Learning
• Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US
• Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
• Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
• From Google Maps to a Fine-Grained Catalog of Street trees
• High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
• Treepedia 2.0: Applying Deep Learning for Large-scale Quantification of Urban Tree Cover