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Sustainable commercial fishing

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NAUTILAB

Improving the

sustainability of
the fishing 
industry.

OVERVIEW

Our team of four had to design an app enabling accessible, achievable, and measurable climate action for a 48-hour Adobe Creative Jam.

We set out to solve the problem of erratic fish migration patterns caused by rising ocean temperatures. The unpredictability causes commercial fishers to travel further, increasing their fuel usage and carbon footprint.

The outcome helped promote sustainable fishing by improving location accuracy with real-time, crowd-sourced data.

MY ROLE

UI/UX Design, User Research,

Visual Design, Wireframing

IN A NUTSHELL

General overview
RESEARCH

We brainstormed different ways to combat climate change before deciding to tackle the erratic migration of the global fish population.

Product backlog
IDEATION

Commercial fishers need an accurate fish tracking app, a way to share their catch on deck, and a predictive tool suggesting ideal areas for fishing.

Nautilab app
OUTCOME

An app that helps commercial fishers reduce their carbon footprint by improving efficiency with accurate, real-time fish tracking maps and tools.

NAVIGATING CLIMATE CHANGE

DISCOVERY

How might we address climate change through an app? The discovery phase kicked off with a brainstorming session exploring various climate change issues industries are facing.

RESEARCH

CATCHING THE BIG FISH

Our initial brainstorming session covered various climate change issues. We narrowed it down to fifteen ideas and selected an idea using the "three-dot method." We choose the fishing industry because it struggles with the increasingly erratic global fish migration patterns.

The unpredictability causes major issues because fishing vessels are forced to travel upwards of 1000 miles to find fish when they previously only traveled 400 miles. Even if they travel that far, there was no guarantee they would find fish. This was a major waste of fuel and causes the fishing industry to drastically increase its carbon footprint.

Business Model Canvas

Business Model Canvas, Assumptions, and User Personas

We moved forward by identifying the larger market demands, and focused on users, pain points, and “jobs to be done.” Our research showed that fishing vessels needed more accurate maps, wanted a more diverse yield, and desired a healthier ecosystem. Another benefit is the value of the data to academia. They would analyze the data in order to arrive at a better understanding of the global fish population.

Fishing vessels need a better way to accurately locate fish in order to save fuel, reduce costs,
and minimize their carbon footprint.
PRODUCT BACKLOG

PRIORITIZING FEATURES

Once we understood user needs, we ideated ways to best solve for them. How might we provide more accurate fishing maps?

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Using innovative technology

The primary goal of our users is to accurately identify fish migration patterns so in order to have a more efficient and cost-effective trip.

Emerging technology in the fishing industry was using a crew member's phone to photograph their catch as it came in. The images were uploaded to a database, and if enough ships participated, there could be a live map from real-time data.

We adapted this idea because it solved our users' needs. Taking it a step further, we created an additional feature that would use A.I. to analyze the data, and recommend areas of interest to fishing vessels based on their input (such as desired fish).

Product Backlog

SITEMAP

MAPPING THE JOURNEY

The three sections of the app are Capture, Map, and Kelp. Capture is the first screen, and the most used because deckhands would use it frequently to photograph the catch. Map is the real-time data map of the fish migration patterns. Kelp (a play on help) is the A.I. backed tool that recommended the ideal places to travel to.

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Sitemap

REELING IT IN

DESIGN

In the final stretch of the Creative Jam, we quickly wireframed and rapidly prototyped in order to deliver the final design concept.

An app made for the sea

Rachel and I designed the wireframes with a deckhand's day-to-day in mind. In order for the app to achieve our desired outcome it needed to quickly capture photographs of fish, provide a clear real-time map of fish migration patterns, and easily allow users to input fishing information for A.I. recommendations.

WIREFRAMING

ILLUSTRATING THE VOYAGE

One key to good design is understanding the context in which a design is used. We knew deckhands would be in rough sea conditions

so we built the design around this context.

PROTOTYPE

ARRIVING AT THE HARBOR

The final prototype developed the Capture, Map, and Kelp screens in to interactive elements that conveyed each use case and utility.

Capture

The Capture section provides fishing vessels with a way to photograph and upload images of their catch. These images are uploaded to a database that recognizes and records the type of fish, weight, size, and location of the catch. The gathered data is analyzed to predict fish migration patterns in real-time.

 

The ability to accurately predict fish migration patterns helps cut down on wasted voyages (and fuel), as well as provide vessels with the location of underfished areas in the world.

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Successful image processing is confirmed

Camera analyzes image, outlines fish

Image is shared then processed

Map

The Map is a real-time result of the images captured from the initial Capture screen. It consists of Basemaps and Filters. Basemaps allow users to see weather, VMS fishing, and fishing efforts. These features are important to users because they benefit from knowing other fishing vessel locations, and if an area is under or over fished. The Filters section allows users to filter the map based on species. The ability to filter is important because ships are setup to catch and harvest particular species.

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"Filter by species" option

Map landing screen

VMS Fishing shows vessel locations

Kelp

“Kelp” (a play on the word “help”) is an algorithm based tool that helps users accurately plan their fishing voyages. Users input the type of fish, 

travel distance, amount of time for travel, and

cost of fuel. The algorithm receives the data

points and analyzes them with the real-time fish data and weather. Then it creates a customized trip forecast, allowing the shipping vessel to accurately plan their trip and reduce fuel costs, therefore reducing their carbon footprint.

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A.I. powered recommendations

Kelp landing screen

Species, distance, time, and $/gallon inputs

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NAUTILAB

Commercial fishing meets sustainability

CONCLUSIONS

Nautilab was created over the course of 48 hours, with hard work and dedication from Isaiah Harvin, Rachel Bugge, and Tommy Legg.

 

This was our first time working closely together. It was a great experience in collaborating with a cross-disciplinary team under tight deadlines while applying the

human-centered design approach.

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