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YOLOvME: Crop Disease Detection & Improving Efficiency in Agriculture

Traditional crop solutions can cost plenty of time, money, and as well as effort to find the expert at the plant site.

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We sat down with Clinton Anani to find out how he overcame crop disease problems using AI.

Clinton is a Software and Robotics Engineer, and a highly passionate Deep Learning Engineer. He is also the Co-founder and CEO of 3Farmate Robotics Limited, an AgriTech R&D startup that is focused on building automation machinery to address inefficient manual labor in the Agriculture sector, by leveraging cutting-edge Robotics and Artificial Intelligence.

Clinton loves building robots! But, his interest in machines started much earlier, when he was a child. This curiosity eventually led him to AI. Clinton started with AI about 3 years ago, having close to zero knowledge of the field. He followed dozens of tutorials and built amazing stuff. But still, Clinton wasn’t able to stand on his own in the AI sector. So, he decided to take a deep dive into Machine Learning. Clinton mostly took courses from the top universities and institutions on Coursera and Udacity. Clinton stated that the courses he took from Andrew Ng on Deep Learning were especially influential in helping him arrive at where he is today.

Clinton has been using YOLOv5 since the beginning of 2021.

3Farmate Robotics Addresses Two Issues In The Food Production Industry:

1. Providing digital farm tools to the sector to improve efficiency and throughput.

2. Providing a highly efficient work labor force to till the land and maximize its potential.

Agriculture Detection with YOLOv5

For both sectors, AI is essential. Crop diseases have always plagued farms, and continue to destroy hundreds of acres of food crops every year. This destruction is due to the extremely slow and manual nature of analyzing crop diseases and how long it takes to recommend solutions.

Existing solutions generally require a plant pathologist to come to visit the farm, make surveys, gather some data, and present findings in a week or two, within which the diseases/infestations will continue to spread. Recognizing the process inefficiencies, there is a clear opportunity for improvement: both in identifying crop diseases right on the spot and recommending solutions in a matter of seconds. So, AI was found to be a top contender to solve this issue.

When it came to the choice of AI models, there are loads of options to choose from. However, YOLOv5 continuously gave Clinton excellent results and accuracy when he previously worked with it, making him consider it for both their digital tools, as well as their embedded systems.

Why Was YOLOv5 Easy To Work With?

Watch: Crop analysis for healthy foods with YOLOv5.

Training a YOLOv5 model is super straightforward and very convenient to work with. For the model deployment, we have a web-based deployment, a mobile, and an embedded system deployment.

“In the near future, we will be looking to perform quality assessments of fruits and vegetables in real-time, and for this, YOLOv5 will be used,” says Clinton.

What Advice Do You Have For Someone Getting Starting With AI?

For someone new to AI, I would recommend finding a really good learning road map for AI and following it meticulously. If you miss the foundations of AI (the Calculus, Statistics, and Differential Equations aspect of it), you will have a hard time working with AI systems and what it takes to be able to handle a real-world AI project. So take it slow, and enjoy the ride.

3Farmate Robotics provides an AI-powered platform for analyzing crops and detecting infections and making recommendations with support for multiple crops. This platform is lightweight and can run on any mobile phone. Stay up to date with 3Farmate Robotics on Linkedin.

Discover how YOLOv5 and Vision AI are changing in the agriculture industry.

We want to spotlight your YOLOv5 use case as well! Tag us on social media @Ultralytics with #YOLOvME for a chance to be featured.

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