CSRWire – Island Conservation Harnesses Machine Learning Solutions From Lenovo and NVIDIA To Restore Island … – CSRwire.com
Published 04-18-24
Submitted by Lenovo
Optimizing and accelerating image processing with AI helps conservation experts safeguard seabird nesting sites on Robinson Crusoe Island.
Around the world, biodiversity is under threat. We are now in what many scientists call the sixth mass extinctionand over the last century, hundreds of species of plants and animals have been lost forever.
Island ecosystems can be particularly vulnerable to human activity. On Robinson Crusoe Island in the South Pacific Ocean, native seabirds such as the pink-footed shearwater are easy prey for an invasive species: the South American coati. Introduced to the island by humans almost a century ago, coatis are housecat-sized mammals in the same family as racoons, which hunt for shearwaters in their nesting sites throughout the island.
Protecting island ecosystems
Leading the fight against invasive species on Robinson Crusoe Island is Island Conservation: an international non-profit organization that restores island ecosystems to benefit wildlife, oceans, and communities. For many years, Island Conservation has been working side by side with island residents to help protect threatened and endangered species.
For Island Conservation, physically removing invasive coatis from shearwater nesting sites is only part of the challenge. To track coati activity, the organization also carefully monitors shearwater nesting sites using more than 70 remote camera traps.
Processing thousands of images a month
The organizations camera traps generate a massive amount of dataaround 140,000 images every monthwhich must be collected and analyzed for signs of coati activity. In the past, the Island Conservation team relied heavily on manual processes to perform this task. To classify 10,000 images would take a trained expert roughly eight hours of non-stop work.
Whats more, manual processing diverted valuable resources away from Island Conservations vital work in the field. The organization knew that there had to be a better way.
Realizing the potential of machine learning
David Will, Head of Innovation at Island Conservation, recalls the challenge: We started experimenting with machine learning [ML] models to accelerate image processing. We were convinced that automation was the way to go, but one of the big challenges was connectivity. Many of the ML solutions we looked at required us to move all of our photos to the cloud for processing. But on Robinson Crusoe Island, we just didnt have a reliable enough internet connection to do that.
As a temporary workaround, Island Conservation saved its camera trap images to SD cards and airmailed them to Santiago de Chile, where they could be uploaded to the cloud for processing. While airmail was the fastest and most frequent link between the island and the mainland, the service only ran once every two weeksand there was a lag of up to three months between a camera trap capturing an image and Island Conservation receiving the analysis.
David Will comments: The time between when we detected an invasive species on a camera and when we were able to respond meant we didnt have enough time to make the kind of decisions we needed to make to prevent extinctions on the island.
Tackling infrastructure challenges
Thats when Lenovo entered the frame. Funded by the Lenovo Work for Humankind initiative with a mission to use technology for good, a global team of 16 volunteers traveled to the island. Using Lenovos smarter technology from devices to software, IT services to servers, the volunteers were able to do their own day jobs while volunteering to help upgrade the islands networking infrastructure: boosting its bandwidth from 1 Mbps to 200 Mbps.
Robinson Crusoe Island is plagued with harsh marine conditions with limited access. They needed a sturdy system that brings compute to the data and allows remote management. The solution was LenovosThinkEdge SE450 with NVIDIA A40 GPUs. The AI-optimized edge server provided a rugged design capable of withstanding extreme conditions while running quietly, allowing it to live comfortably in the new remote workspace. Lenovo worked with Island Conservation to tailor the server to its needs, adding additional graphics cards to increase the AI processing capability per node. We took the supercomputer capability they had in Santiago and brought that into a form factor that is much smaller, says Charles Ferland, Vice President and General Manager of Edge Computing at Lenovo.
The ThinkEdge SE450 eliminated the need for on-site technicians. Unlike a data center, which needs staff on-site, the ThinkEdge server could be monitored and serviced remotely by Lenovo team members. It proved to be the perfect solution. The ThinkEdge server allows for full remote access and management of the device speeding up decisions from a matter of months to days.
David Will comments, Lenovo helped us run both the A40s at the same time immensely speeding up processing, something we previously couldnt do. It has worked tremendously well and almost all of our processing to-date has been done on the ThinkEdge SE450.
Unleashing the power of automation
To automate both the detection and classification of coatis, Lenovo data scientists from the AI Center of Excellence built a custom AI script to detect and separate out the results for coatis and other species from MegaDetectoran open-source object detection model that identifies animals, people, and vehicles in camera trap images. Next, Lenovo data scientists trained an ML model on a custom dataset to give a multi-class classification result for nine species local to Robinson Crusoe Island, including shearwater and coatis.
This two-step GPU-enabled detector-and-classifier pipeline can provide results for 24,000 camera trap images in just one minute. Previously, this would have taken a trained expert twenty hours of laboran astonishing 99.9% time saving. The model achieved 97.5% accuracy on a test dataset with approximately 400 classifications per second. Harnessing the power of NVIDIAs CUDA enabled GPUs allowed us to have a 160x speedup on MegaDetector compared to the previous implementation.
Sachin Gopal Wani, AI Data Scientist at Lenovo, comments: Delivering a solution that is easily interpretable by the user is a crucial part of our AI leadership. I made a custom script that generates outputs compatible with TimeLapsea software the conservationists use worldwide to visualize their results. This enabled much faster visualization for a non-technical end-user without storing additional images. Our solution allows for the results to load with the original images overlapped with classification results, saving terabytes of disk space.
With these ML capabilities, Island Conservation can filter out images that do not contain invasive species with a high degree of certainty. Using its newly upgraded internet connection, the organization can upload images of coati activity to the cloud, where volunteers on the mainland evaluate the images and send recommendations to the island rapidly.
Using ML, we can expedite image processing, get results in minutes, and cut strategic decision time from three months to a matter of weeks, says David Will. This shorter response time means more birds protected from direct predation and faster population recovery.
Looking to the future
Looking ahead, Island Conservation plans to continue its collaboration with the Lenovo AI Center of Excellence to develop Gen AI to detect other types of invasive species, including another big threat to native fauna: rodents.
With Lenovos support, were now seeing how much easier it is to train our models to detect other invasive species on Robinson Crusoe Island, says David Will. Recently, I set up a test environment to detect a new species. After training the model for just seven hours, we recorded 98% detection accuracyan outstanding result.
As the project scope expands, Island Conservation plans to use more Lenovo ThinkEdge SE450 devices with NVIDIA A40 GPUs for new projects across other islands. Lenovos ThinkEdge portfolio has been optimized for Edge AI inferencing, offering outstanding performance and ruggedization to securely process the data where its created.
Backed by Lenovo and NVIDIA technology, Island Conservation is in a stronger position than ever to protect native species from invasive threats.
David Will says: In many of our projects, we see that more than 30% of the total project cost is spent trying to remove the last 1% of invasives and confirm their absence. With Lenovo, we can make decisions based on hard data, not gut feeling, which means Island Conservation takes on new projects sooner.
Healing our oceans
Island Conservations work with Lenovo on Robinson Crusoe Island will serve as a blueprint for future activities. The team plans to repurpose the AI application to detect different invasive species on different islands around the world from the Caribbean to the South and West Pacific, the Central Indian Ocean, and the Eastern Tropical Pacificwith the aim of saving endangered species, increasing biodiversity, and increasing climate resilience.
In fact, Island Conservation, Re:wild, and Scripps Institution of Oceanography recently launched the Island-Ocean Connection Challenge to bring NGOs, governments, funders, island communities, and individuals together to begin holistically restoring 40 globally significant island-ocean ecosystems by 2030.
Everything is interconnected in what is known as the land-and-sea cycle, says David Will. Healthy oceans depend on healthy islands. Island and marine ecosystem elements cycle into one another, sharing nutrients vital to the plants and animals within them. Indigenous cultures have managed resources this way for centuries. Climate change, ocean degradation, invasive species, and biodiversity loss are causing entire land-sea ecosystems to collapse, and island communities are disproportionately impacted.
The Island-Ocean Connection Challenge marks the dawn of a new era of conservation that breaks down artificial silos and is focused on holistic restoration.
David Will concludes: Our collective effort, supported by Lenovo and NVIDIA, is helping to bridge the digital divide on island communities, so they can harness cutting-edge technology to help restore, rewild, and protect their ecosystems, and dont get further left behind by AI advances.
Get involved today at http://www.jointheiocc.org.
To read the Lenovo case study on Island Conservation, click here. Or to watch the Lenovo case study video, click here.
Lenovo is a US$62 billion revenue global technology powerhouse, ranked #217 in the Fortune Global 500, employing 77,000 people around the world, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the worlds largest PC company by further expanding into growth areas that fuel the advancement of New IT technologies (client, edge, cloud, network, and intelligence) including server, storage, mobile, software, solutions, and services. This transformation together with Lenovos world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992)(ADR: LNVGY). To find out more visit https://www.lenovo.com, and read about the latest news via our StoryHub.
More from Lenovo
- Machine learning helps identify 'thermal switch' for next-generation nanomaterials - Phys.org - October 17th, 2025 [October 17th, 2025]
- Machine Learning Makes Wildlife Data Analysis Less of a Trek - Maryland.gov - October 17th, 2025 [October 17th, 2025]
- An interpretable multimodal machine learning model for predicting malignancy of thyroid nodules in low-resource scenarios - BMC Endocrine Disorders - October 17th, 2025 [October 17th, 2025]
- In First-Episode Psychosis Patients, Machine Learning Predicted Illness Trajectories to Potentially Improve Outcomes - Brain and Behavior Research - October 17th, 2025 [October 17th, 2025]
- Novel Machine Learning Model Improves MASLD Detection in Type 2 Diabetes - The American Journal of Managed Care (AJMC) - October 17th, 2025 [October 17th, 2025]
- Hybrid machine learning models for predicting the tensile strength of reinforced concrete incorporating nano-engineered and sustainable supplementary... - October 17th, 2025 [October 17th, 2025]
- Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning - Nature - October 17th, 2025 [October 17th, 2025]
- Association between atherogenic index of plasma and sepsis in critically ill patients with ischemic stroke: a retrospective cohort study using... - October 17th, 2025 [October 17th, 2025]
- AI enters the nuclear age: Pentagon modernizes warheads with machine learning - Washington Times - October 17th, 2025 [October 17th, 2025]
- AI and Machine Learning - Bentley Systems shares its vision for trustworthy AI - Smart Cities World - October 17th, 2025 [October 17th, 2025]
- Looking back to move forward: can historical clinical trial data and machine learning drive change in participant recruitment in anticipation of... - October 15th, 2025 [October 15th, 2025]
- Physics-Based Machine Learning Paves the Way for Advanced 3D-Printed Materials - Bioengineer.org - October 15th, 2025 [October 15th, 2025]
- Predicting one-year overall survival in patients with AITL using machine learning algorithms: a multicenter study - Nature - October 15th, 2025 [October 15th, 2025]
- Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis - BMC Nephrology - October 15th, 2025 [October 15th, 2025]
- Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia - Orphanet Journal of Rare... - October 15th, 2025 [October 15th, 2025]
- Machine learning-based prediction of preeclampsia using first-trimester inflammatory markers and red blood cell indices - BMC Pregnancy and Childbirth - October 15th, 2025 [October 15th, 2025]
- Utilizing AI and machine learning to improve railroad safety: Detecting trespasser hotspots - masstransitmag.com - October 15th, 2025 [October 15th, 2025]
- Precision medicine meets machine learning: AI and oncology biomarkers - pharmaphorum - October 15th, 2025 [October 15th, 2025]
- Aether Pro Exchange Transforms Execution Dynamics with Machine-Learning Optimization - GlobeNewswire - October 15th, 2025 [October 15th, 2025]
- Prevalence, associated factors, and machine learning-based prediction of depression, anxiety, and stress among university students: a cross-sectional... - October 15th, 2025 [October 15th, 2025]
- Artificial Intelligence vs. Machine Learning: Which skills will open better career options in the global - Times of India - October 15th, 2025 [October 15th, 2025]
- Study Reveals Impact of Negative Class Definitions on Machine Learning Accuracy in Immunotherapy - geneonline.com - October 15th, 2025 [October 15th, 2025]
- Muna Al-Khaifi: Detection of Breast Cancer Using Machine Learning and Explainable AI - Oncodaily - October 13th, 2025 [October 13th, 2025]
- Expedia Group Unveils Innovative AI and Machine Learning Solutions to Transform Partner Travel Experiences - Travel And Tour World - October 13th, 2025 [October 13th, 2025]
- Machine Learning-Guided Prediction of Formulation Performance in Inhalable CiprofloxacinBile Acid Dispersions with Antimicrobial and Toxicity... - October 13th, 2025 [October 13th, 2025]
- Machine Learning and BIG DATA workshop planned Oct. 14-15 - West Virginia University - October 11th, 2025 [October 11th, 2025]
- How Google enables third-party circularity by increasing recycling rates with Machine Learning - The World Business Council for Sustainable... - October 11th, 2025 [October 11th, 2025]
- Integrating Artificial Intelligence and Machine Learning in Hydroclimatic Research - A Promising Step Forward - University of Northern British... - October 11th, 2025 [October 11th, 2025]
- Semi-automatic detection of anteriorly displaced temporomandibular joint discs in magnetic resonance images using machine learning - BMC Oral Health - October 11th, 2025 [October 11th, 2025]
- AI and Machine Learning - Partnership to bring infrastructure intelligence to US public sector - Smart Cities World - October 11th, 2025 [October 11th, 2025]
- Between rain and snow, machine learning finds nine precipitation types - Phys.org - October 9th, 2025 [October 9th, 2025]
- Between rain and snow, machine learning finds 9 precipitation types - Michigan Engineering News - October 9th, 2025 [October 9th, 2025]
- Machine learning optimizes nanoparticle design for drug delivery to the brain - Physics World - October 9th, 2025 [October 9th, 2025]
- Development and validation of a machine learning-based prediction model for prolonged length of stay after laparoscopic gastrointestinal surgery: a... - October 9th, 2025 [October 9th, 2025]
- G Sachs: Stock Mkt Not in Bubble Yet; Machine Learning/ AI Expected to Spawn New Wave of Superstars - AASTOCKS.com - October 9th, 2025 [October 9th, 2025]
- AI and Machine Learning - See.Sense works with City of Sydney to develop AI dashboard - Smart Cities World - October 9th, 2025 [October 9th, 2025]
- Machine Learning Used to Predict Live Birth Outcomes in Fresh Embryo Transfers - geneonline.com - October 9th, 2025 [October 9th, 2025]
- RIT researchers use machine learning to better understand the pathways of disease - Rochester Institute of Technology - October 7th, 2025 [October 7th, 2025]
- Leveraging machine learning to predict mosquito bed net utilization among women of reproductive age in sub-Saharan Africa - Malaria Journal - October 7th, 2025 [October 7th, 2025]
- Machine learning-based radiomics using magnetic resonance images for prediction of clinical complete response to neoadjuvant chemotherapy in patients... - October 7th, 2025 [October 7th, 2025]
- Machine Learning Self Driving Cars: The Technology Driving the Future of Mobility - SpeedwayMedia.com - October 7th, 2025 [October 7th, 2025]
- Investigating the relationship between blood factors and HDL-C levels in the bloodstream using machine learning methods - Journal of Health,... - October 7th, 2025 [October 7th, 2025]
- AI in the fast lane: F1 teams Alpine, Audi use machine learning as force multiplier - The Business Times - October 7th, 2025 [October 7th, 2025]
- Future Scope of Machine Learning in Healthcare Market Set to Witness Significant Growth by 2025-2032 - openPR.com - October 7th, 2025 [October 7th, 2025]
- AI and Machine Learning - AI readiness and adoption toolkit launched - Smart Cities World - October 4th, 2025 [October 4th, 2025]
- Machine Learning Model UmamiPredict Developed to Forecast Savory Taste of Molecules and Peptides - geneonline.com - October 4th, 2025 [October 4th, 2025]
- Machine Learning Boosts Crop Yield Predictions in Senegal - Bioengineer.org - October 4th, 2025 [October 4th, 2025]
- Machine learning-driven stability analysis of eco-friendly superhydrophobic graphene-based coatings on copper substrate - Nature - October 4th, 2025 [October 4th, 2025]
- Integrated machine learning analysis of proteomic and transcriptomic data identifies healing associated targets in diabetic wound repair - Nature - October 4th, 2025 [October 4th, 2025]
- Development and evaluation of a machine learning prediction model for short-term mortality in patients with diabetes or hyperglycemia at emergency... - October 4th, 2025 [October 4th, 2025]
- Fast and robust mixed gas identification and recognition using tree-based machine learning and sensor array response - Nature - October 4th, 2025 [October 4th, 2025]
- Estimation of sexual dimorphism of adult human mandibles of South Indian origin using non-metric parameters and machine learning classification... - October 4th, 2025 [October 4th, 2025]
- Cloud-Based Machine Learning Platforms Technologies Market Growth and Future Prospects - Precedence Research - October 4th, 2025 [October 4th, 2025]
- Machine Learning Framework Developed to Optimize Phosphorus Recovery in Hydrothermal Treatment of Livestock Manure - geneonline.com - October 4th, 2025 [October 4th, 2025]
- Unifying machine learning and interpolation theory via interpolating neural networks - Nature - October 2nd, 2025 [October 2nd, 2025]
- Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records - BMC Veterinary... - October 2nd, 2025 [October 2nd, 2025]
- The Future of Liver Health: Can Human Models and Machine Learning Reduce Disease Rates? - Technology Networks - October 2nd, 2025 [October 2nd, 2025]
- Machine Learning Radiomics Predicts Pancreatic Cancer Invasion - Bioengineer.org - October 2nd, 2025 [October 2nd, 2025]
- Next-generation COVID-19 detection using a metasurface biosensor with machine learning-enhanced refractive index sensing - Nature - October 2nd, 2025 [October 2nd, 2025]
- Machine learning-based models for screening of anemia and leukemia using features of complete blood count reports - Nature - October 2nd, 2025 [October 2nd, 2025]
- Estimating the peak age of chess players through statistical and machine learning techniques - Nature - October 2nd, 2025 [October 2nd, 2025]
- Optimizing water quality index using machine learning: a six-year comparative study in riverine and reservoir systems - Nature - October 2nd, 2025 [October 2nd, 2025]
- Physics-informed machine learning-based real-time long-horizon temperature fields prediction in metallic additive manufacturing - Nature - October 2nd, 2025 [October 2nd, 2025]
- The Silicon Revolution: How AI and Machine Learning Are Forging the Future of Semiconductor Manufacturing - FinancialContent - October 2nd, 2025 [October 2nd, 2025]
- Machine learning model for differentiating Pneumocystis jirovecii pneumonia from colonization and analyzing mortality risk in non-HIV patients using... - October 2nd, 2025 [October 2nd, 2025]
- Radiomics and Machine Learning Applied to CECT Scans Show Potential in Predicting Perineural Invasion in Pancreatic Cancer - geneonline.com - October 2nd, 2025 [October 2nd, 2025]
- Machine learning and response surface optimization to enhance diesel engine performance using milk scum biodiesel with alumina nanoparticles - Nature - October 2nd, 2025 [October 2nd, 2025]
- Landmark Patent Appeal Decision Strengthens Protection for AI and Machine Learning Innovations - The National Law Review - October 2nd, 2025 [October 2nd, 2025]
- Machine learning researchers and industry leaders gathering at Santa Clara University - Stories - News & Events - Santa Clara University - September 30th, 2025 [September 30th, 2025]
- Building better batteries with amorphous materials and machine learning - Tech Xplore - September 30th, 2025 [September 30th, 2025]
- Machine Learning-Supported Fragment Hit Expansion in Absence of X-Ray Structures - Evotec - September 30th, 2025 [September 30th, 2025]
- Machine learning model predicts which radiotherapy patients are most vulnerable to adverse side effects - Health Imaging - September 30th, 2025 [September 30th, 2025]
- How AI and Machine Learning Are Revolutionizing Laser Welding - Downbeach - September 30th, 2025 [September 30th, 2025]
- What if A.I. Doesnt Get Much Better Than This? - Machine Learning Week 2025 - September 30th, 2025 [September 30th, 2025]
- Sex estimation from the sternum in Turkish population using various machine learning methods and deep neural networks - SpringerOpen - September 30th, 2025 [September 30th, 2025]
- Predictive AI Must Be Valuated But Rarely Is. Heres How To Do It - Machine Learning Week 2025 - September 30th, 2025 [September 30th, 2025]
- Interpretable machine learning incorporating major lithology for regional landslide warning in northern and eastern Guangdong - Nature - September 28th, 2025 [September 28th, 2025]
- Building Machine Learning Application with Django - KDnuggets - September 28th, 2025 [September 28th, 2025]
- Evaluating the use of body mass index change as a proxy for anorexia nervosa recovery: a machine learning perspective - Journal of Eating Disorders - September 28th, 2025 [September 28th, 2025]
- Prediction of cutting parameters and reduction of output parameters using machine learning in milling of Inconel 718 alloy - Nature - September 28th, 2025 [September 28th, 2025]