Fruitpunch AI - Blogs, Publications & Stories

AI for Good Articles

April 12, 2024

Looking inland to clean up oil spills with AI

Oil spills are a significant threat to marine ecosystems, wildlife, and public health. Ships sinking, boats crashing, or leaking tankers pollute our rivers and ports, causing ecological damage. In our latest collaboration with Rijkswaterstaat (The Dutch Ministry of Infrastructure and Waterways), we aimed to use Artificial Intelligence to improve the response times of their Oil Clean-up teams. By working with drone-captured RGB images, we aimed to develop an advanced oil-volume estimation tool that would enhance the efficiency and accuracy of oil spill assessments. This tool would also facilitate prompt and effective response measures to minimize the damage caused by oil spills

February 21, 2024

Saving Marine Ecosystems with Artificial Intelligence

Discover how the AI for Coral Reefs Challenge is revolutionizing marine conservation. With a blend of supervised and unsupervised AI approaches, this initiative aims to enhance coral reef monitoring, offering hope for the preservation of marine ecosystems through advanced technology.

February 1, 2024

Tracking Turtles: How AI helps conservationists to re-identify sea turtles

Discover how AI revolutionizes sea turtle conservation, enabling accurate re-identification with non-intrusive methods. This collaboration with Sea Turtle Conservation Bonaire utilizes cutting-edge AI to match sea turtle photos against a database, enhancing our understanding of their migratory patterns and aiding in their preservation. The solution employs innovative techniques for turtle face detection, feature extraction, and matching, proving pivotal for habitat preservation efforts.

November 20, 2023

From Pixels to Preservation: How AI Gives Rise to a Birdwatching Revolution

At the interface of ecology and the ever-increasing applications to use artificial intelligence for good, FruitPunch AI and the Swedish University of Agricultural Sciences (SLU) joined together to better understand how our ecosystems function. Over decades, passionate ornithologists have been taking thousands of images of birds across the world. In the AI for Eagles Challenge, we exploited this dataset to build a machine learning pipeline that would allow rapid assessment of a bird’s behavior, species, and age. Traditional manual labeling of such images is a time-consuming process, and the need for speed in assessing individual birds and population states underscores the urgency of the task. The successful implementation of AI has the potential to revolutionize the efficiency of bird image analysis. By automating the labeling process, the application can significantly accelerate the assessment of individual birds and contribute valuable insights into the broader state of bird populations.

October 18, 2023

Leveraging Large Language Models to make businesses around the world more sustainable!

With rising global temperatures, the world is facing more and more natural disasters in the form of extreme drought and subsequent fires, as well as extreme rainfall and flooding. Amidst this climate crisis, it is the responsibility of every single organization to implement sustainable business practices. One company that aids organizations in becoming a more sustainable version of themselves is Metabolic.

August 25, 2023

Flying High with AI: Counting Pelican Breeding Pairs in the Danube Delta

Imagine flying in a small airplane over the vast wetlands of the Danube Delta on the shores of the Black Sea in Romania looking for patches of small white dots: great white pelicans (Pelecanus onocrotalus). While flying over the colonies researchers like Sebastian Bugariu from the Romanian Ornithological Society (ROS) take photos which will be used to count the number of breeding birds when back in the office. The number of breeding pairs has grown from ~5,000 pairs 15 years ago to recently ~18,000 pairs. Keeping good records of the breeding numbers is important but not an easy task. Back at the office, it can take weeks to go through the images and manually count the pelicans. Wouldn’t it be great if this process could be automated, freeing up time that could be dedicated to other important conservation? This is where the AI for Pelican challenge started.

August 10, 2023

Solving automated wildlife taxonomy with AI

In the wild landscapes of Europe, camera traps have become essential tools for ecologists and wildlife researchers, sharing glimpses into the lives of the continent's diverse and fascinating animals. These devices have revolutionized our understanding of European wildlife, but their use comes with a challenge - the sheer volume of data they generate. To tackle this issue, a team of dedicated FruitPunchers recently developed an AI solution for the European Wildlife Challenge.

July 27, 2023

Listening to the Giants: Protecting Forest Elephants Through Audio Monitoring

In the dense rainforests of Central Africa, a captivating endeavor is underway, driven by researchers from Cornell University. Their mission? To track and protect elephants through the power of audio monitoring. These majestic creatures emit resounding rumbles at an incredibly low frequency, nearly imperceptible to the human ear. These deep calls, traversing vast distances, serve as a concealed communication network for elephants, veiled from our understanding for centuries.

March 23, 2023

Understanding seals with AI

On 6th December 2022, the FruitPunchAI community joined forces with researchers from Colgate University to build a facial recognition model that enables the non-invasive study and monitoring of harbor seals and other marine mammals. The team’s goal was to build upon the existing SealNet model developed by Colgate University to improve its accuracy, generalizability across sea species and mammals, and increase development velocity across data processing and model development workflows.

February 10, 2023

How we detect oil spills on open sea and support response teams

“Deepwater Horizon Oil Spill” might ring a bell for most of you. But did you know that there are numerous oil spills caused by small accidents and deliberate discharges, which do not make the headlines? Did you also know that these smaller incidents actually contribute to the bulk of oil pollution, and are just as, if not more threatening than a single major oil spill?

November 24, 2022

Can AI track reforestation projects using drone and satellite data?

Satellite and drone data was used to monitor tree coverage of Justdiggit re-greening projects in Tanzania and Kenya to measure the efficiency of carbon capture! To help fight climate change by improving carbon capture efficiency, three teams of AI engineers came together to build and implement machine learning models.

October 14, 2022

How to use vehicle sensors to make cities more sustainable

A case study on making cities greener by vegetation monitoring and detecting traffic density; differentiating between heavy vehicles, buses and private transport.

October 3, 2022

Can AI detect the risk of heart failure from ECGs?

Electrocardiogram data was subjected to a sweeping array of machine learning and deep learning models. Is it as good a predictor of heart failure risk as blood tests?

September 21, 2022

The pains of classifying flooded forests in satellite data

About a tricky detection use case - from weeks of data pre-processing to training 2 CNNs; and why the answer might be in infrared band data.

September 14, 2022

AI-based early warning system for river floods

Forecasting flash floods with LSTM, ARIMA and Prophet using time series data from hydrological sensors monitoring French rivers.

September 6, 2022

How we applied AI to prevent sepsis in preterm babies

A case study on using XGBoost for time series forecasting to predict the onset of sepsis in preterm infants within a 12-hour prediction horizon.

August 29, 2022

AI-powered Wildlife Conservation in Africa

An account of 10-week teamwork developing multiple machine learning and hardware pipelines to bring production-ready AI to edge hardware on flying rangers.

August 28, 2022

Model Optimization and Pruning of Poacher-detecting YOLOv5

Optimizing a YOLOv5 model for NVIDIA Jetson Nano to increase the inference speed and reduce memory footprint, focusing on inference speed not the absolute mAP.

August 27, 2022

User-friendly, Wilderness-proof MLOps

Developing a CI/CD pipeline that automatically retrains the existing poacher-detecting model, which can be deployed to the drone with improved performance.