Machine Learning to Detect, Classify, and Count Blackbirds Damaging Agriculture Using Drone-Based Imagery: Supporting Ai-Driven Automation for Deployment of Damage Management Tools
Academic Article
Overview
Overview
Abstract
As human populations expand and land use changes, human-wildlife conflicts are increasing, requiring cost-effective management tools that balance human well-being and wildlife conservation. Hazing devices are often used to mitigate conflicts between wildlife and agriculture with drones serving as both frightening devices and monitoring systems. Artificial intelligence (AI) enables automated detection, identification, and counting of wildlife, allowing for field-based Internet of Things (IoT) systems to selectively deploy tools when target species reach a critical threshold. We acquired drone imagery of mixed-species blackbird flocks – dominated by red-winged blackbirds (RWBL) Agelaius phoeniceus – damaging sunflowers (Helianthus annuus) in North Dakota (September–October 2021–2022). We trained a ResNet-18 convolutional neural network (CNN) model to A) detect flocks (accuracy = 95.0%); and Faster Region-based Convolutional Neural Network (Faster-RCNN) models to B) detect individual blackbirds (accuracy = 65.7%, precision = 97.6%), C) classify individual blackbirds by species and for RWBL sex and age class, and D) count blackbirds (% difference: birds = 37.5%; male RWBL = 39.6%; female RWBL = 43.1%). The model correctly classified RWBL to species (87.6%), sex, and age class (adult males: 89.8%; hatch-year males: 27.6%; females: 80.0%). The RWBL were misclassified as other blackbird species, rather than non-target species. Variability in background type (e.g., sky, green vegetation, tan vegetation) and complexity (e.g., contrast, texture), along with bird camouflage, required background removal to enhance machine learning performance. Camera orientation (i.e., depth perception, target overlap) and image quality (i.e., blurred, shadowed objects) affected accuracy of detection, classification, and counts. Automated deployment systems reduce labor and wildlife habituation, increasing longevity and efficacy of management tools.
