https://www.flig.org/enContents © 2024 <a href="mailto:n.tesla@example.com">flig.org</a> Sun, 07 Jul 2024 14:12:05 GMTNikola (getnikola.com)http://blogs.law.harvard.edu/tech/rsshttps://www.flig.org/posts/2021/12/see-apples/Pal Denes<div><p><img alt="Solve Challenge" src="https://www.flig.org/images/cards/solve-challenge.png" style="width: 200px; float: left; margin-right: 20px; margin-top: 0px;"></p> <p>When I first started to think about the Nature’s Bounty challenge, I decided to try and find a way to automatically detect apples using a camera and OpenCV. My thinking was roughly:</p> <ol> <li>Apples are of a known size and colour (and there is nothing else around that’s round or red).</li> <li>Apple detection should be relatively generic, in the sense that we shouldn’t rely too much on knowledge about their expected positions on the tree.</li> <li>We’re only detecting apples, not tree branches (so apples are just “floating” in space, and we can grab them freely – but we might try to do this “cautiously”, expecting some invisible branches might be in the way).</li> <li>Apple detection should be as simple and efficient as possible (better to refine readings continuously as we get closer than to try something “perfect” that might be too slow or resource-hungry).</li> </ol> <p><a href="https://www.flig.org/posts/2021/12/see-apples/">Read more…</a> (8 min remaining to read)</p></div>applescameradinkyOpenCVrobothttps://www.flig.org/posts/2021/12/see-apples/Fri, 31 Dec 2021 12:00:00 GMT