Indiscriminate harvesting, contamination of water bodies, and accumulation of pollutants have increased the mortality rates among fish species, leading to a steady decline in population. Therefore, it is imperative to protect them. Unfortunately, manual efforts to conserve aquatic species are tedious and expensive.
This problem was faced by a group of biologists at the Nisqually River Foundation. The manual process of counting and monitoring salmon species was time-consuming, inefficient, and costly. To reduce this human effort, an AI-based deep learning algorithm for fish detection has been deployed.
The solution allows biologists to dedicate their precious time to solving sophisticated or complicated problems. This article looks at how fish detection and conservation are possible with deep learning models.
Protecting endangered fish species is essential for ecological balance. Technology and humans are working hand-in-hand to maintain this balance. Camera traps have made it possible to collect underwater footage of fish species in a safe and effective manner.
Later, these footages are run against deep learning models to identify fishes. This article looks at how fish detection is possible with AI and deep learning models.
Table of Contents
Various Ways to Preserve Fish Species
The reducing population of fishes is due to multiple reasons. A study from science journal shows the “maximum sustainable yield” of 235 different temperature-dependent populations of 124 fish species in 38 ecoregions, from 1930 to 2010.
It further states that only 4% of fish population respond positively to warming oceans whereas 8% respond negatively. So, how can we use this data to bring out initiative that can help save fish species?
Reducing Footprints
It involves reducing the human presence at the place of fishing. The objective here is to leave the spot as it is after you have completed your fishing activity. It doesn’t matter if you are fishing along a shoreline or in a boat. The aim is to leave the area in the same condition before you arrived.
Reduce Fishing Capacity
Excessive or overfishing is one of the biggest threats to the fish population. Their population becomes very low when there is unrestrained fishing. It is also wasteful fishing where excess quantity caught often gets discarded. Doing away with this unsustainable practice can help conserve fish species.
Reduce Plastic Usage
Reducing the usage of single-use plastics is critical to conserving oceans and their inhabitants. There are around 5.25 trillion pieces of plastic waste in our oceans, which is an alarming sign of the situation. Plastic waste harms marine life and affects their conservation measures. It is therefore prudent to reduce the usage of plastic items.
Fish Counting
Species detection with Machine learning models has evolved significantly in the past few years. All it takes is to create a model trained with a huge dataset (photos and videos) of any species.
Fish detection with the help of technology is a modern method to conserve fish species. It is possible to count fish in real-time using neural networks and deep learning algorithms. Leveraging technology helps carry out the underwater species detection activity in minimal time and with precision. By understanding the fish population precisely, biologists and non-profits can take the necessary steps to conserve endangered species.
Deep Learning Models Can Save Fish by Automated Counting
Fish detection and counting have traditionally been done through manual methods. These are resource-intensive practices on various fronts. It takes substantial time and human capital to carry out the activity. It also leads to increased costs.
Monitoring coastal marine ecosystems can also be complex due to its dynamic nature. Leveraging technology such as computer vision is thus vital to automate the process and get accurate results in a much shorter time. An AI-driven solution for fish identification is an excellent option.
NOTE: Check out Gramener’s computer vision solutions that help top organizations automate manual tasks through object detection and image processing techniques.
Fish identification with deep learning involves high-resolution underwater camera technologies. It enables capturing a large volume of data. They also capture the movement and behavior of aquatic species through video feeds. By gathering enormous amounts of data, the question of how to analyze it arises. It can take months to do it manually.
Using technology is critical to getting accurate results. You can use machine learning models and train them to detect and derive vital insights from these data streams. The accuracy of human assessment can also be subjective and depends on the quality of data feeds. Technology tools thus help carry out the process objectively.
Gramener partnered with Microsoft AI for Earth team to help Nisqually River Foundation augment its fish detection capabilities. The Nisqually Indian Tribe had installed a video camera and infrared sensors in a fish ladder at a dam on the river. The fish detection with AI models helped them increase accuracy by 73%.
How Automated Fish Counting Works
Counting fishes with automated technology is not new. Fish counting with image processing significantly reduces the time needed to count fish. Here’s how it’s done step by step.
Underwater Camera Traps Record Fish Images & Videos
The first step makes it a computer vision application. Advanced underwater cameras aid the process of fish detection by capturing the movement of fish species. The computer vision solution fish detection mechanism works 24/7 to gather the behavior patterns.
Fish detection using machine learning involves infrared sensors to get an alert whenever a fish passes by. The trigger leads to capturing of short videos of 30 seconds. There are several occasions throughout the day and night during which the fish movement gets captured. This fish detection mechanism leads to enough data for further analysis.
Videos Run Through Deep Learning Models
After recording the data, scientists prepare it for further analysis. They use deep learning models by training them to identify patterns. These models then use all their learnings to detect and classify fish species.
Fish Detection by Database Comparison
The fish detection data gathered can have fish species of all kinds. It is thus critical to create a database of all possible species beforehand. The deep learning model can get trained on the database to understand the different fish species.
The models can then use image classification to identify and flag specific fish species. They simplify the entire process when compared to manual work that is prone to errors and takes time to complete.
Read More: Find out the 8 best animal identifier apps for biodiversity conservation
Bottomline
Technologies like machine learning and deep learning models have large-scale use cases. Fish detection and conservation become a seamless task with deep learning models that can accurately detect and classify a range of fish species. The model helps you carry out the fish detection tasks in the most resource-efficient way.
Gramener’s fish detection mechanism has already helped non-profits and other organizations worldwide leverage the power of technology for best results.
Contact us for custom built low code computer vision solutions for your business challenges and check out ESG and AI solutions built for our clients, including Fortune 500 companies. Book a free demo right now.
