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	<title>AI &#8211; Fish Farm Management Software | KAMAHU</title>
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	<link>https://www.kamahu.com</link>
	<description>Track your fish farm from feeding to harvest.</description>
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	<title>AI &#8211; Fish Farm Management Software | KAMAHU</title>
	<link>https://www.kamahu.com</link>
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	<item>
		<title>A Practical Data Strategy for Safer Atlantic Salmon Crowding</title>
		<link>https://www.kamahu.com/safer-crowding-welfare-monitoring/</link>
		
		<dc:creator><![CDATA[KAMAHU]]></dc:creator>
		<pubDate>Sun, 06 Sep 2026 23:20:22 +0000</pubDate>
				<category><![CDATA[Farming]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[crowding]]></category>
		<category><![CDATA[density]]></category>
		<category><![CDATA[fish health]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[salmonids]]></category>
		<guid isPermaLink="false">https://www.kamahu.com/?p=17809</guid>

					<description><![CDATA[Density alone cannot steer salmon crowding. Learn which welfare, behaviour and water-quality signals farms should record.]]></description>
										<content:encoded><![CDATA[<p><em>Crowding is necessary during transfer, treatment and harvest, but it compresses risk into a short operational window. The 2026 CrowdMonitor report shows that safer decisions depend on combining behaviour, water quality, time and fish condition—not relying on biomass density alone.</em></p>
<blockquote><p><strong>The practical takeaway:</strong> Treat crowding as a live, time-based operation. Track behaviour above and below the surface alongside water quality, fish condition and operator actions—and use those signals to intervene before welfare deteriorates.</p></blockquote>
<p>Crowding Atlantic salmon is not simply a question of how many kilograms of fish occupy a cubic metre. It is a live operational process: water volume changes, pumping begins, fish behaviour evolves, oxygen conditions may vary and hazards can develop below the surface even when the water above looks calm.</p>
<p>That is the central lesson from <em>Monitoring and optimising the crowding of Atlantic salmon using health and welfare indicators (CrowdMonitor)</em>, a Nofima-led project conducted with the Institute of Marine Research and commercial partners. Its findings point to a practical management principle for fish farmers: <strong>steer the operation using several synchronised welfare signals, not one number.</strong></p>
<p>For farm-management systems, the implication is equally important. A crowding record should not be a single density field completed after the event. It should be a time-based operational log that helps staff see deterioration early, act consistently and learn from every crowd. This is where a configurable platform such as <a href="https://www.kamahu.com/">KAMAHU’s fish farm management software</a> can provide the structure: linking stock and movement records with tracking sheets, mortality, treatments, sensor data, production reporting and advanced cameras capable of detecting signs of stress.</p>
<h2>What the density figures tell us—and what they do not</h2>
<p>In controlled tank trials, post-smolts of approximately <strong>530 g</strong> were crowded for <strong>two hours</strong> at four densities corresponding to crowding intensity levels 1 to 4: approximately <strong>100, 130, 180 and 255 kg/m³</strong>.</p>
<p>The fish did not show severe behavioural or welfare responses under these experimental conditions, but the data still revealed important changes:</p>
<ul data-spread="false">
<li>heart rate increased to about <strong>65 beats per minute</strong> during crowding;</li>
<li>cortisol and magnesium were elevated one hour after crowding, then returned to pre-crowding levels within <strong>24 hours</strong>;</li>
<li>fish at level 1, around <strong>100 kg/m³</strong>, grew marginally but significantly better after the event than fish at levels 2 to 4;</li>
<li>contact between fish was higher at level 4, while activity increased at levels 2 to 4;</li>
<li>alignment against the water flow became especially marked after <strong>1 to 1.5 hours</strong> at levels 3 and 4;</li>
<li>minor skin changes were still observable <strong>168 hours</strong> later.</li>
</ul>
<p>A second tank trial compared approximately <strong>160 and 320 kg/m³</strong> over <strong>one and three hours</strong>. It found no major effect of duration or intensity on the measured welfare outcomes, and heart rate rose to about <strong>60 beats per minute</strong> during crowding.</p>
<p>These figures need careful interpretation. The tanks were partially drained before fish were crowded sideways with a clam-shell grader, leaving room to move and avoiding forcing fish above the surface. Commercial tank operations often crowd by lowering the water level while pumping fish out. The report explicitly advises caution when generalising the experimental results.</p>
<p>In other words, <strong>320 kg/m³ for three hours is not a general safe limit</strong>. The study shows that salmon can sometimes adapt to high density when the environment and method are favourable. It does not show that density or duration can be managed independently of fish condition, water quality, equipment and behaviour.</p>
<figure id="attachment_17822" aria-describedby="caption-attachment-17822" style="width: 1800px" class="wp-caption aligncenter"><img fetchpriority="high" decoding="async" class="wp-image-17822 size-full" title="Photographer : Mark Stebnicki" src="https://www.kamahu.com/storage/2026/09/KAMAHU-mark-stebnicki.webp" alt="Numerous fish crowd together at the surface of a concrete aquaculture raceway, causing turbulent water and splashing." width="1800" height="1200" srcset="https://www.kamahu.com/storage/2026/09/KAMAHU-mark-stebnicki.webp 1800w, https://www.kamahu.com/storage/2026/09/KAMAHU-mark-stebnicki-300x200.webp 300w, https://www.kamahu.com/storage/2026/09/KAMAHU-mark-stebnicki-1024x683.webp 1024w, https://www.kamahu.com/storage/2026/09/KAMAHU-mark-stebnicki-768x512.webp 768w" sizes="(max-width: 1800px) 100vw, 1800px" /><figcaption id="caption-attachment-17822" class="wp-caption-text">Crowded fish create intense surface activity, highlighting the importance of continuous welfare monitoring during handling operations.</figcaption></figure>
<h2>Turn fish behaviour into a control signal</h2>
<p>The established surface scale describes a progression from normal swimming to an emergency:</p>
<table>
<tbody>
<tr>
<th>Level</th>
<th>Observable state</th>
<th>Operational interpretation</th>
</tr>
<tr>
<td>1</td>
<td>Slow, normal swimming; no dorsal fins or white sides at the surface</td>
<td>Target condition</td>
</tr>
<tr>
<td>2</td>
<td>Normal swimming near the suction point; a few dorsal fins; no white sides</td>
<td>Low stress, but monitor trends</td>
</tr>
<tr>
<td>3</td>
<td>Excited, disorganised swimming; more than <strong>20 dorsal fins</strong> visible; some white sides continuously visible</td>
<td>Escalating welfare risk; intervene before conditions deteriorate further</td>
</tr>
<tr>
<td>4</td>
<td>Many fins and white sides; fish against the net; some lethargic fish; pumping rate cannot remain constant</td>
<td>High risk; conditions are unacceptable if they become more severe than level 3</td>
</tr>
<tr>
<td>5</td>
<td>The whole crowd is “boiling”</td>
<td>Emergency with potential for a large fish kill unless fish are rapidly released</td>
</tr>
</tbody>
</table>
<p>The operational goal cited in the report is <strong>level 1</strong>, and crowding more severe than level 3 is considered unacceptable under the referenced welfare guidance. More importantly, the level is not merely something to record. It is a prompt to act.</p>
<p>During one commercial smolt transfer, the tank remained at level 1 for the first hour while water was drawn down. Intensity rose to level 2 when pumping began and to level 3 around <strong>30 minutes later</strong>, as more fins became visible. Operators added water to the tank, reducing the observed intensity. This is monitoring closing the control loop: <strong>observe, intervene and verify the response.</strong></p>
<h2>The calm-surface problem</h2>
<p>Surface observation remains valuable, but it can miss underwater hazards.</p>
<p>In a commercial net-pen crowd preceding delousing, the operation remained at level 2 for <strong>one hour and 20 minutes</strong> before rising to level 3. At times, visible activity at the surface was not matched by a dangerous underwater state. At other times, the surface appeared stable while fish below were touching the net and risked being pressed against it.</p>
<p>The project therefore recommends combining surface observations with underwater cameras or an ROV. Operators should look for:</p>
<ul data-spread="false">
<li>fish touching or being pressed against the net;</li>
<li>folds or pockets that can trap fish;</li>
<li>crowding close to equipment or the pumping area;</li>
<li>gasping or other signs of respiratory distress;</li>
<li>burrowing behaviour;</li>
<li>loss of schooling structure, queueing or restricted movement;</li>
<li>insufficient space between the fish group, the net and the surface.</li>
</ul>
<p>The project’s preliminary underwater risk scale extends from no apparent crowding risk to catastrophic risk. Whether a farm adopts that scale or its own validated protocol, the lesson is the same: <strong>surface and subsurface observations must share one timeline.</strong></p>
<h2>Stress can rise before injuries become visible</h2>
<p>Visible injuries did not always track the stress response during the trials. In the commercial cases, cortisol, lactate and glucose rose progressively during crowding even when injury scores showed few clear changes.</p>
<p>Skin integrity also deserves special attention before another handling step. In the commercial delousing crowd, scale samples from fish exposed for only <strong>seven minutes</strong> at level 2 showed migration in more than <strong>70%</strong> of cultures after 96 hours. For fish sampled after <strong>one hour and 40 minutes</strong>, including <strong>30 minutes at level 3</strong>, the proportion was only around <strong>30%</strong>. The report interprets this as a possible sign of micro-damage that may reduce the skin’s healing capacity.</p>
<p>This does not establish a universal time threshold, but it supports two precautions:</p>
<ol start="1" data-spread="false">
<li>treat movement into level 3 as a meaningful escalation, not merely a change of label;</li>
<li>protect fish from additional stress and skin damage after an intensive crowding event, especially when further handling follows immediately.</li>
</ol>
<h2>What a useful digital crowding record should capture</h2>
<p>The report recommends a toolbox rather than a single indicator. A useful workflow in farm-management software should connect five layers of information. KAMAHU supports essential records—including stock tracking, movements between ponds, tanks or cages, site-to-site transfers, mortality, tracking sheets and production reports—and has developed dedicated AI models trained to detect signs of stress in fish cohorts. When connected to webcams, these models can identify abnormal behaviour in real time, helping farms build a consistent crowding workflow around the five layers below.</p>
<h3>1. Pre-operation readiness</h3>
<p>Record fish group, estimated biomass, mean weight, recent mortality, appetite, relevant health issues, previous treatments and visible injury levels. KAMAHU’s stock, weighing, mortality and veterinary-prescription records can bring much of this context together before the operation begins. Add equipment checks and define who can pause, dilute or release the crowd.</p>
<h3>2. Environmental conditions</h3>
<p>Log oxygen and other relevant water-quality measurements with timestamp, position and depth. An isolated reading is less useful than a trend that can be compared with behaviour and operational actions. Where farms use connected monitoring equipment, KAMAHU can couple with sensors so environmental measurements sit alongside farm records rather than in a separate data silo.</p>
<h3>3. A live event timeline</h3>
<p>Timestamp the start of water drawdown, net movement, pumping, treatment batches, interruptions and the end of the operation. Add the estimated density where it is available, while recognising that density does not replace direct observation. A configurable KAMAHU tracking sheet can give operators one shared format across sites and crews.</p>
<h3>4. Surface and underwater welfare observations</h3>
<p>Use a consistent intensity scale and record where each observation was made. Attach camera or ROV evidence when practical. The system should highlight worsening level, repeated white sides, gasping, burrowing, fish-net contact, net pockets or impaired swimming.</p>
<h3>5. Actions and outcomes</h3>
<p>Connect each warning to an operator response—adding water, slowing or pausing pumping, changing the net configuration, adding oxygen or releasing the crowd—and document whether conditions improved. Follow the event with mortality, appetite and injury observations so the team can compare immediate signals with later outcomes. KAMAHU’s traceability and reporting functions can then help managers review events across fish groups, sites and time periods.</p>
<p>Together, these layers turn welfare documentation into operational intelligence. Instead of asking only, “What density did we reach?”, managers can ask better questions:</p>
<ul data-spread="false">
<li>At what point did behaviour begin to deteriorate?</li>
<li>Did the deterioration coincide with pumping, falling oxygen or a particular pen zone?</li>
<li>Which intervention restored level 1 or level 2 conditions most quickly?</li>
<li>Do certain fish groups, seasons, crews or equipment configurations produce more warnings?</li>
<li>Did a high-risk observation predict later mortality, appetite loss or injury?</li>
</ul>
<p>Because KAMAHU is customisable, farms can adapt the fields, reports and alerts to their own operating procedures. The aim is not to force every site into one rigid form; it is to give observations, decisions and outcomes a common structure that can be reviewed and improved.</p>
<h2>Three rules for the next crowding operation</h2>
<h3>1. Keep the crowd steady, slow and controlled</h3>
<p>The net-pen trials suggest that giving fish time to adapt, while monitoring the crowd continuously, can limit risk.</p>
<h3>2. Treat intensity as dynamic</h3>
<p>Density, duration, fish condition, water quality and equipment configuration interact. A fixed density limit cannot describe the whole risk.</p>
<h3>3. Make every observation actionable</h3>
<p>A welfare score that sits in a form after the event is documentation. A timestamped score linked to alerts, decisions and outcomes is management.</p>
<p>Crowding will always require experienced operators. Better data does not replace their judgement. It gives the team a shared picture, a common language and a record that can improve the next operation.</p>
<p>KAMAHU is designed to support that continuous-improvement loop through real-time monitoring, traceability, configurable reporting, alerts and data consolidation. <a href="https://www.kamahu.com/#features">Explore KAMAHU’s aquaculture management features</a> or <a href="https://www.kamahu.com/meet-kamahu/#meet-contact">contact the team</a> to discuss a crowding workflow tailored to your farm.</p>
<p>Paninnguaq Knudsen (September 2026)</p>
<div>
<hr />
</div>
<h2><span style="color: #808080;"><em>Sources and editorial note</em></span></h2>
<p><span style="color: #808080;"><em>This article synthesises the April 2026 Nofima report Monitoring and optimising the crowding of Atlantic salmon using health and welfare indicators (CrowdMonitor) by Noble et al. The report describes both controlled experiments and a limited number of commercial observations. Figures should therefore be used as evidence from the reported conditions, not as universal operating limits. Farms should continue to follow applicable regulations, veterinary advice, welfare standards and site-specific procedures.</em></span></p>
<p><span style="color: #808080;"><em><strong>Further operational resources cited by the report:</strong></em></span></p>
<ul data-spread="false">
<li><span style="color: #808080;"><em><a style="color: #808080;" href="https://doi.org/10.21357/edhh-5736" rel="nofollow noopener" target="_blank">CrowdMonitor handbook for commercial tanks v0.1</a></em></span></li>
<li><a href="https://doi.org/10.21357/68na-zn54" rel="nofollow noopener" target="_blank"><span style="color: #808080;"><em>CrowdMonitor handbook for commercial net pens v0.1</em></span></a></li>
</ul>
]]></content:encoded>
					
		
		
			</item>
		<item>
		<title>Press book</title>
		<link>https://www.kamahu.com/pressbook/</link>
		
		<dc:creator><![CDATA[KAMAHU]]></dc:creator>
		<pubDate>Sun, 31 Aug 2025 19:52:07 +0000</pubDate>
				<category><![CDATA[Business]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[business]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[press]]></category>
		<category><![CDATA[SaaS]]></category>
		<category><![CDATA[satellite]]></category>
		<guid isPermaLink="false">https://www.kamahu.com/?p=17695</guid>

					<description><![CDATA[Various articles published in recent years about KAMAHU (page in construction) Sonar, La revue du Campus mondial de la mer (2025.06) : Observer les fermes aquacoles depuis l&#8217;espace  Produits de la Mer (2024.11) : L&#8217;IA au service de la filière des...]]></description>
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									<h4></h4>
<p class="wp-block-paragraph"><span style="font-size: 20px;"><em>Various articles published in recent years about KAMAHU (page in construction)</em></span></p>
<ol>
<li>Sonar, La revue du Campus mondial de la mer (2025.06) : <em>Observer les fermes aquacoles depuis l&#8217;espace</em> </li>
<li>Produits de la Mer (2024.11) : <em>L&#8217;IA au service de la filière des produits aquatiques</em></li>
<li>Fish Farmer Magazine (October-November 2018) : <em>KAMAHU offer a SAASy solution</em></li>
</ol>
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                    <h5 class="icon-box-title"><span class="neuros-heading-content">Observer les fermes aquacoles depuis l'espace</span></h5>

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                    <h5 class="icon-box-title"><span class="neuros-heading-content">L'IA au service de la filière des produits aquatiques</span></h5>

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		<title>HOW MUCH does a Machine Learning model cost ?</title>
		<link>https://www.kamahu.com/how-much-does-a-ml-model-cost/</link>
		
		<dc:creator><![CDATA[KAMAHU]]></dc:creator>
		<pubDate>Thu, 01 Aug 2024 06:00:08 +0000</pubDate>
				<category><![CDATA[AI]]></category>
		<category><![CDATA[Business]]></category>
		<category><![CDATA[cost control]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://demo.artureanec.com/themes/neuros/how-can-i-get-started-with-artificial-intelligence-for-my-business-copy/</guid>

					<description><![CDATA[AI solutions are becoming increasingly accessible to businesses. Harnessing the power of artificial intelligence, neural networks have the ability to analyze vast amounts of data, identify complex patterns, and make accurate predictions, enabling businesses to unlock new opportunities and drive growth....]]></description>
										<content:encoded><![CDATA[
<p class="wp-block-paragraph"><strong>AI solutions are becoming increasingly accessible to businesses. Harnessing the power of artificial intelligence, neural networks have the ability to analyze vast amounts of data, identify complex patterns, and make accurate predictions, enabling businesses to unlock new opportunities and drive growth. <br /></strong></p>



<p class="wp-block-paragraph">At KAMAHU, we specialize in providing comprehensive neural network services that can revolutionize your business. Whether you&#8217;re looking to enhance your data analysis capabilities, automate repetitive tasks, or optimize operational processes, our team of experts is here to help you leverage the full potential of neural networks.<br />Data lies at the heart of neural networks, and our services start with understanding your unique data landscape. We work closely with you to identify and collect relevant data sources, ensuring that your neural network models are built on a solid foundation. We employ cutting-edge techniques to preprocess and clean the data, making it ready for training the neural network models.</p>
<blockquote>
<p><strong><u>Data collection and preparation</u></strong> are key and may cost a lot, or nothing, depending on what you already store.</p>
</blockquote>





<blockquote>
<p class="wp-block-paragraph">Model design would cost <u>much less</u> than the cost of your ultrasound scanner for instance : a few hundreds or thousands Dollars.</p>
</blockquote>
<p>The development of neural networks is a specialized task that requires expertise and experience. KAMAHU excels in designing and implementing neural network architectures tailored to your specific business needs. <a title="Get in touch with us" href="https://www.kamahu.com/meet-kamahu/">Get in touch with us</a></p>
]]></content:encoded>
					
		
		
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		<title>Revolutionizing Aquaculture with AI and Machine Learning: real-world applications and success stories</title>
		<link>https://www.kamahu.com/revolutionizing-aquaculture-with-machine-learning/</link>
		
		<dc:creator><![CDATA[KAMAHU]]></dc:creator>
		<pubDate>Wed, 03 Jul 2024 17:00:49 +0000</pubDate>
				<category><![CDATA[Business]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://demo.artureanec.com/themes/neuros/how-natural-language-processing-is-revolutionizing-text-analysis-copy/</guid>

					<description><![CDATA[Machine learning is already solving practical aquaculture challenges. Discover four projects involving ultrasound image classification, fish-malformation measurement, video-based farm monitoring and satellite detection of offshore cages.]]></description>
										<content:encoded><![CDATA[		<div data-elementor-type="wp-post" data-elementor-id="15644" class="elementor elementor-15644">
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									<h4></h4>
<p class="wp-block-paragraph"><span style="font-size: 20px;"><em>Article by <a href="https://www.linkedin.com/in/mikaeldautrey" rel="nofollow noopener" target="_blank">Mikael Dautrey</a> (ISITIX) and <a href="https://www.linkedin.com/in/kiliandelorme" rel="nofollow noopener" target="_blank">Kilian Delorme</a> (KAMAHU) &#8211; July 2024</em></span></p>
<p><span style="font-size: 20px;">Machine learning is transforming aquaculture with groundbreaking applications in image classification, image segmentation, video analysis, and object detection.</span></p>
<p><span style="font-size: 20px;">In this post, we explore four innovative projects that demonstrate real results in the field:</span></p>
<ol>
<li><span style="font-size: 20px;"><strong>Image classification using ultrasound</strong> &#8211; to detect frog maturity, essential for breeding programs, improving breeding cycles and increasing yield.</span></li>
<li><span style="font-size: 20px;"><strong>Image segmentation </strong>&#8211; to identify malformations in fish, aiding in early health interventions.</span></li>
<li><span style="font-size: 20px;"><strong>Video analysis and scene understanding</strong> &#8211; to provide continuous monitoring of fish farms, detecting anomalies in behavior and environmental conditions.</span></li>
<li><span style="font-size: 20px;"><strong>Object detection algorithms</strong> &#8211; to identify fish cages from satellite images, optimizing farm management and resource allocation.</span></li>
</ol>
<h3>Image classification : detecting frog maturity using ultrasound</h3>
<p><span style="font-size: 20px;">In a frog hatchery, to optimize tadpoles production, it is necessary to estimate frog maturity in order to separate the spawning females. To do this, the breeder performs ultrasound scans on the frogs. Initially, this work requires a highly qualified biologist to decide on frog maturity. <a href="https://www.linkedin.com/in/agnes-joly-39499a/" target="_blank" rel="noopener nofollow">Agnes Joly</a> who runs the AQUAPRIMEUR hatchery wanted anyone on her farm to be able to carry out the operation, through the use of an automatic classification system. </span></p>
<p><span style="font-size: 20px;">Designing a device to automatically classify ultrasound scans in an industrial environment is quite a challenge.</span></p>
<h4>Key challenges and solutions:</h4>
<ul>
<li><span style="font-size: 20px;"><strong>Image collection and validation</strong>: we validated the feasibility of using machine learning to classify ultrasound images, despite having only a few hundred classified scans initially.</span></li>
<li><span style="font-size: 20px;"><strong>Ultrasound scanner interface</strong>: we interfaced with the ultrasound scanner to automate the task, using auxiliary electronics to read and process the video signal.</span></li>
<li><span style="font-size: 20px;"><strong>Ergonomic solutions</strong>: in the environment of an aquaculture farm, we implemented an audio interface, enabling hands-free operation, and complemented it by a touch screen.</span></li>
<li><span style="font-size: 20px;"><strong>Reliability and performance</strong>: we tackled issues like peripheral interfacing, noise reduction, and power supply.</span></li>
</ul>
<h4>Collecting images and designing a ML model, the easy part of the project</h4>
<p><span style="font-size: 20px;">When we began this project in 2020, we faced significant challenges. Traditional models were hard to optimize, and deep learning required thousands of images, while we had only a few hundred classified ultrasound scans. <strong>Today, advances in ML tools and our growing expertise have made this task routine</strong>. It remains an important part of the project, especially in collaborating with experts, whose maturity classifications can vary with their subjective judgment.</span></p>
<h4>Interfacing with the ultrasound scanner, a matter of electronics and DIY</h4>
<p><span style="font-size: 20px;">We&#8217;ve only done this for one model today, but we&#8217;re confident in our ability to interface with any ultrasound scanner on the market. After testing several methods, we decided to <strong>read the video signal directly and process it via the auxiliary electronics box</strong>. There&#8217;s still plenty of room for improvement, but it works.</span></p>
<h4>Finding ergonomic solutions to facilitate the operator&#8217;s work, still a work in progress</h4>
<p><span style="font-size: 20px;">In an aquaculture farm, the environment is obviously challenging; workers often need both hands to carry loads or handle animals. Solutions that work in an office, like a mouse and keyboard, are no longer suitable. <strong>We opted for an audio interface complemented by a touch screen.</strong></span></p>
<p><span style="font-size: 20px;">The audio interface works both ways, allowing for speaking and listening, with the user wearing a headset with a microphone and earphones. </span></p>
<p><span style="font-size: 20px;">The touch screen provides visual feedback to validate commands sent to the system, read information, or perform certain complex actions via audio. This was one of the most difficult parts of the project, and we are still working to improve the solution.</span></p>
<h4>Addressing various reliability and performance issues, 80/20 law</h4>
<p><span style="font-size: 20px;">A complex project like this brings <strong>its share of time-consuming little problems</strong>, such as headset peripheral interfacing, noise reduction, power supply, shock and humidity protection, model and response time optimization, and application architecture. This has a direct impact on development costs, and at some point you have to ask yourself whether it&#8217;s worth it.</span></p>
<h4>A long-term investment, beyond bottom-line improvement</h4>
<p><span style="font-size: 20px;">Automation and work simplification were the primary drivers for this project. The system  will eliminate the access barrier to using the ultrasound scanner, ensuring consistent use and standardized results, regardless of who performs the operations.</span></p>
<p> </p>
<h3>Image segmentation : detecting malformations</h3>
<h4>A tedious but essential work</h4>
<p><span style="font-size: 20px;">Research laboratories studying fish deformities need to measure various dimensions of fingerlings, such as head size, body size, camber, yolk sac appearance and eye shape. The specific measurements often depend on the research focus or the requirements of a particular publication. <strong>Traditionally, this involves taking photos and measuring dimensions in pixels using a scale shown in the photo. </strong>This method is slow, time-consuming, and prone to inaccuracies.</span></p>
<h4>A model for this task</h4>
<p><span style="font-size: 20px;"><strong>A research laboratory</strong> which had supervised a <a href="https://theses.hal.science/tel-03614998/file/These-2021-EGAAL-Biologie_et_physiologie_animales-LE_DU_CARREE_Jessy.pdf" target="_blank" rel="noopener nofollow">doctoral thesis about the direct and transgenerational ecotoxicity of glyphosate on the health of rainbow trout</a> <strong>approached us to test the feasibility of using machine learning for measuring fish deformities</strong>. Using a sample of around 100 measured images, we developed a model tailored to this task.</span></p>
<h4>Annotations reloaded</h4>
<p><span style="font-size: 20px;">The images had been annotated by drawing lines with an image editor and then recording the values in an Excel file. Although the information was available, it couldn&#8217;t be easily integrated into a machine learning pipeline. Therefore, we had to redo all the annotations using a dedicated tool. For this project, we used <a href="https://www.robots.ox.ac.uk/~vgg/software/via/" target="_blank" rel="noopener nofollow">VGG VIA</a>, a lightweight image annotation software developed by the Visual Geometry Group (VGG) at the University of Oxford. </span></p>
<h4>From pixels to geometry</h4>
<p><span style="font-size: 20px;">We then trained a segmentation model that allowed us to easily measure the main characteristics monitored by the researchers. Next, we had to convert the pixel measurements into units of measurement (mm). This task was complicated by the absence of documentation or traces of the device used to take the photos, including camera optics and mounting, making it difficult to accurately estimate the geometric deformations of the images.</span></p>
<h4>A well-functioning model, but with a limited scope</h4>
<p><span style="font-size: 20px;">The model worked well, but it was limited by the specific research topic for which it had been designed. <strong>Each research topic would require a different model.</strong> We don&#8217;t see a cost-effective opportunity to create customized Deep Learning models for biology researchers, as each research topic comes with its own set of measurements and therefore a dedicated model that will serve only once and for a few thousand images.</span></p>
<p><span style="font-size: 20px;"><strong>It would be more advantageous to develop a software environment dedicated to customizing ML models for researchers.</strong> This environment would provide a standardized photography tool, standardize the image annotation process and automatically produce customized models tailored to their specific research topics.</span></p>
<p> </p>
<h3>Video analysis and scene understanding : monitoring fish farms</h3>
<p><span style="font-size: 20px;">Many aquaculture start-up projects use underwater cameras to monitor fish, providing information on fish quantity, size, and health status. While this idea is excellent in theory, operating underwater cameras is difficult due to water turbidity, the need for frequent glass cleaning, and the high cost of watertight housing. To address these issues, we tested <strong>a different solution by positioning aerial cameras above the farm&#8217;s tanks</strong>. Since birds excel at spotting fish from above, we thought, why not us?</span></p>
<h4>Positioning cameras</h4>
<p></p>
<p><span style="font-size: 20px;">A fish farm with a small but efficient facility on the banks of a river in Brittany wanted to monitor its tanks more effectively. Our test site breeds trout with moderately sized raceways, approximately 50 meters by 5 meters, and uses a RAS system located under cover. We strategically positioned cameras around the tank, taking into account the constraints of the location, available height, power supply, and network. <strong>This setup allowed us to collect data on a NAS, which we then processed.</strong></span></p>
<h4>Processing videos, more data, but not significantly more information, except time</h4>
<p><span style="font-size: 20px;">Processing videos brought more data, but information varies slowly from image to image. When we started this project in 2021, we had primarily been working with still images. Initially, we were overwhelmed by the volume of data produced by just a few cameras. <strong>Gradually, we developed our tools and methods to better handle the specifics of video data:</strong> a large quantity of similar images with different temporalities, ranging from seconds to minutes, hours, days, and months.</span></p>
<h4>Going further: a territory to explore</h4>
<p><span style="font-size: 20px;">Guided by <a href="https://www.linkedin.com/in/robert-le-coat-3174211a4" target="_blank" rel="noopener nofollow">Robert Le Coat</a> and <a href="https://www.linkedin.com/in/amaury-guet-329116136" target="_blank" rel="noopener nofollow">Amaury Guet</a>, we have today a solid understanding of the production process. <strong>We can detect simple activities such as movement around the tanks, feeding, and aerator activation.</strong> However, there is still much work to be done in optimizing camera positions, selecting the right optics, and defining what truly constitutes valuable information in images of an aquaculture farm. This ongoing exploration promises to refine our approach and enhance the effectiveness of our monitoring solutions.</span></p>
<p> </p>
<h3>Object detection : detecting cages from satellite images</h3>
<p><span style="font-size: 20px;">Satellite imagery offers a vast potential for monitoring aquaculture installations globally.</span></p>
<h4>The globe at your fingertips</h4>
<p><span style="font-size: 20px;">We were put in touch with a satellite data operator who was looking for new fields of application and new opportunities to resell its data. Satellites enable us to work on an unimaginable scale, covering the surface of the globe in just a few clicks. </span></p>
<h4>A very simple challenge : detecting fish cages</h4>
<p><span style="font-size: 20px;">To test the potential of satellite data, we started with a very simple problem: detecting fish cages installed by offshore fish farmers around the world. This initial challenge allowed us to explore the capabilities of satellite imagery in identifying and monitoring aquaculture installations on a global scale.</span></p>
<h4>Satellite Data Sets: a complex field</h4>
<p><span style="font-size: 20px;">Each satellite operator manages its own constellations, comprising specific types of satellites equipped with sensors that have distinct capabilities, measured wavelengths, measurement precision, and spatial resolution. Additionally, the route taken by a constellation is well-defined depending on its orbit, and the resulting covered area of the globe. These routes and coverage areas can also vary with the seasons. Furthermore, some measurements may or may not be available, depending on the sun&#8217;s position and the cloudy weather when the satellite passes over the area.</span></p>
<h4>Starting up with products of level 3 or higher</h4>
<p><span style="font-size: 20px;">The raw data thus produced, representing thousands of small squares of data covering the surface of the globe at close but different times, are then reprocessed to produce the satellite images we consult, such as Google maps, which are in fact assemblies of images to reconstruct a global view of the earth.</span></p>
<p><span style="font-size: 20px;">There are five levels of products sold by satellite operators.</span></p>
<p><span style="font-size: 20px;">We&#8217;ve started our development work with products of level 3 or higher.</span></p>
<div class="table-responsive">
<table class="table table-striped table-bordered">
<thead>
<tr>
<td>Level</td>
<td>Description</td>
</tr>
</thead>
<tbody>
<tr>
<td><strong>0</strong></td>
<td>Raw telemetry data; includes received signals without any correction</td>
</tr>
<tr>
<td><strong>1</strong></td>
<td>Data corrected for instrumental errors</td>
</tr>
<tr>
<td>1A</td>
<td>Radiometrically corrected data</td>
</tr>
<tr>
<td>1B</td>
<td>Georeferenced and calibrated data</td>
</tr>
<tr>
<td><strong>2</strong></td>
<td>Data derived from Level 1 with specific products</td>
</tr>
<tr>
<td><strong>3A</strong></td>
<td>Data synthesized on regular grids (mosaics); used for global or regional analyses</td>
</tr>
<tr>
<td><strong>4</strong></td>
<td>Modeled or assimilated data; combination of observational data and numerical models</td>
</tr>
</tbody>
</table>
</div>
<h4>Geojson and image annotation</h4>
<p><span style="font-size: 20px;">We first used geojson data from farms we wanted to track. It turned out that reconciling geojson and satellite data was complex. </span></p>
<p><span style="font-size: 20px;">Reconciling GeoJSON data with satellite imagery presented challenges, particularly due to differences in projections and coordinate systems. GeoJSON typically uses the WGS 84 coordinate reference system (CRS) with geographic coordinates (latitude and longitude). Satellite imagery might use different projections (e.g., UTM, Mercator) or datums, leading to discrepancies.</span></p>
<p><span style="font-size: 20px;">As we didn&#8217;t have much time, we reverted to the classic image annotation that we had mastered. </span></p>
<h1>AI in Aquaculture &#8211; Conclusion: model training</h1>
<p><span style="font-size: 20px;">Training a model is also a real challenge. Imagine that, to cover a coastal region such as Brittany, several tens of thousands of 240&#215;240 images are required. Approximately 1/10000th of these images will include an aquaculture farm. The model must therefore be adapted to this situation.</span></p>
<p><span style="font-size: 20px;">We are still fine-tuning the model and hope to produce other significant results soon.</span></p>
<p><span style="font-size: 20px;">For more insights and updates on our innovative projects, connect with us on LinkedIn:</span><br /><span style="font-size: 20px;"><a href="https://www.linkedin.com/in/mikaeldautrey" target="_blank" rel="noopener nofollow">Mikael Dautrey</a>, <a href="https://www.linkedin.com/in/kiliandelorme" target="_blank" rel="noopener nofollow">Kilian Delorme</a></span></p>
<p></p>
<p class="wp-block-paragraph"><em><span class="field-value text-secondary font-family-lato fs-5 lh-normal d-block">The AQUASCAN project led by Isitix and KAMAHU initially received part funding from the Brittany Region. KAMAHU received part funding from the European Space Agency BIC Nord France for a project using satellite imagery in her farm management software.</span></em></p>
<p></p>								</div>
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		<title>Is Machine Learning in aquaculture THE RIGHT CHOICE ?</title>
		<link>https://www.kamahu.com/is-ml-the-right-choice/</link>
		
		<dc:creator><![CDATA[KAMAHU]]></dc:creator>
		<pubDate>Thu, 19 Oct 2023 16:18:53 +0000</pubDate>
				<category><![CDATA[AI]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[machine learning]]></category>
		<guid isPermaLink="false">https://demo.artureanec.com/themes/neuros/ai-and-robotics-advancing-automation-and-human-robot-collaboration-copy/</guid>

					<description><![CDATA[In today&#8217;s fast-paced and data-driven world, businesses are constantly seeking innovative ways to gain a competitive edge, make smarter decisions, and deliver exceptional customer experiences. One technology that is transforming industries across the globe is neural networks. Harnessing the power of...]]></description>
										<content:encoded><![CDATA[
<p class="wp-block-paragraph"><strong>In today&#8217;s fast-paced and data-driven world, businesses are constantly seeking innovative ways to gain a competitive edge, make smarter decisions, and deliver exceptional customer experiences. One technology that is transforming industries across the globe is neural networks. Harnessing the power of artificial intelligence, neural networks are used for machine and deep learning, and have the ability to analyze vast amounts of data, identify complex patterns, and make accurate predictions, enabling businesses to unlock new opportunities and drive growth. To determine whether the use of machine learning, in aquaculture or whatever industry, is the right choice, you need to consider these 4 key points : </strong></p>





<figure class="wp-block-pullquote has-text-align-left has-background" style="background-color: #f0f2f4;">
<blockquote>
<ol>
<li><strong>VOLUME</strong> : there are a lot of <u>repetitive counting or analysis</u> to be done<br /><br /></li>
<li><strong>CONSISTENCY and REPRODUCIBILITY</strong> : the actual human process is boring, biased and <u>error prone</u>.<br /><br /></li>
<li><strong>SHORTAGE OF SKILLED WORKERS</strong> : the process requires <u>skills</u> that few of your employees have or that cost too much.<br /><br /></li>
<li><strong>DECISION SUPPORT</strong> : the process is easier when additional information is provided to <u>assist</u> the operator.<br /><br /></li>
</ol>
</blockquote>
</figure>



<p class="wp-block-paragraph">The development of neural networks is a specialized task that requires expertise and experience. Our KAMAHU team excels in designing and implementing neural network architectures tailored to your specific business needs. From traditional feedforward networks to advanced convolutional and recurrent networks, we have the knowledge and skills to build models that deliver superior performance and accuracy.</p>
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