What is the role of artificial intelligence in detecting and managing invasive species?
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The Role of Artificial Intelligence in Detecting and Managing Invasive Species
Introduction to AI in Invasive Species Management
Artificial intelligence (AI) has emerged as a powerful tool in the detection and management of invasive species. These species pose significant threats to biodiversity, ecosystems, and even human health. Traditional methods of identifying and managing invasive species are often time-consuming and resource-intensive. AI offers a more efficient and accurate alternative, leveraging advanced computational techniques to address these challenges.
AI Techniques for Detecting Invasive Species
Machine Learning and Environmental DNA Analysis
One innovative approach involves the use of machine learning to analyze environmental DNA (eDNA) for the identification of invasive species. This method has been applied to detect the invasive fish species Lagocephalus sceleratus, which is dangerous to human health. The system employs extreme learning machines combined with an evolving Izhikevich spiking neuron model to automatically recognize the species based on eDNA analysis.
Deep Learning and Aerial Imagery
Deep learning algorithms, particularly convolutional neural networks (CNNs), have shown great promise in detecting invasive plant species from high-resolution aerial imagery. For instance, a custom Retinanet-based deep convolutional neural network was used to detect the invasive tree species Miconia calvescens in Hawai'i. This AI system outperformed human analysts in both speed and accuracy, making it a cost-effective solution for large-scale surveillance.
Autonomous Underwater Vehicles and Real-Time Monitoring
AI is also being integrated into autonomous underwater vehicles (AUVs) for real-time monitoring of invasive marine species. A detection system using YOLOv4 and SSD-Mobilenet-v2 models was developed to identify lionfish, an invasive species affecting marine ecosystems from the United States to Brazil and the Mediterranean Sea. This system allows for real-time determination of lionfish numbers and locations without human intervention.
AI in Wetland Management
UAV Data and Multispectral Imaging
Unmanned Aerial Vehicles (UAVs) combined with AI techniques are being used for wetland management, particularly in detecting invasive weed species like Phragmites australis. High-resolution multispectral images captured by UAVs are analyzed using multiclass relevance vector machines (MCRVM) to classify and detect these weeds. This method has proven effective in monitoring changes in weed cover over time, providing valuable data for management decisions.
AI for Food Pathogen Detection
DNA Barcoding and Extreme Learning Machines
AI is also being utilized for the detection of food-borne pathogens associated with invasive species. For example, an AI system using extreme learning machines was developed to identify the invasive fish species Lagocephalus sceleratus through DNA barcoding. This system offers a fast and accurate method for pathogen detection, crucial for ensuring food safety and public health.
Conclusion
AI technologies are revolutionizing the detection and management of invasive species. From machine learning and deep learning to UAVs and autonomous underwater vehicles, these advanced computational techniques offer efficient, accurate, and cost-effective solutions. As AI continues to evolve, its applications in environmental management are likely to expand, providing even more robust tools for protecting biodiversity and ecosystems.
Sources and full results
Most relevant research papers on this topic
Detecting invasive species with a bio-inspired semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus
Comparing Interpretation of High-Resolution Aerial Imagery by Humans and Artificial Intelligence to Detect an Invasive Tree Species
Deep learning algorithm as a strategy for detection an invasive species in uncontrolled environment
Smart Tools for Wetland Management: UAV Data and Artificial Intelligence Technique for Change Detection of Phragmites Australis in the Bear River Migratory Bird Refuge
Intelligent Bio-Inspired Detection of Food Borne Pathogen by DNA Barcodes: The Case of Invasive Fish Species Lagocephalus Sceleratus
Detection And Alert System Of Invasive Flower Species Using Cnn
Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review
A Shiny r app to solve the problem of when to stop managing or surveying species under imperfect detection
Biodiversity assessment based on artificial intelligence and neural network algorithms
Early detection of marine invasive species following the deployment of an artificial reef: Integrating tools to assist the decision-making process.
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