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Researchers at UL's Faculty of Computer and Information Science (FRI) have made a significant breakthrough in drone-based precision agriculture with a new AI technology. Limited onboard processing power in drones usually leads to the processing of the acquired images to take place post-flight, when they are downloaded and analyzed on a separate computer. Dr. Octavian Machidon's team, working on the H2020 Smart4All AgriAdapt project in 2023, tackled this limitation head-on. They developed an energy-efficient, adaptive framework enabling real-time processing of UAV images onboard the drone itself.

This intelligent technology, demonstrated effective in weed detection applications, analyzes each image during flight and assesses its complexity. For clear images with easily identifiable weeds, the AI uses minimal processing power. Conversely, it uses a more significant portion of its neural network for more challenging images, ensuring optimal efficiency and minimal energy consumption.


The benefits of AgriAdapt are three-fold. First, it fosters real-time analysis of UAV images onboard the drone itself, eliminating processing delays. Second, the AI's efficient design significantly reduces battery drain by requiring less processing power. As a result, larger areas can be covered during a single flight. Third, the framework introduced in AgriAdapt is highly adaptable; it can be harnessed for various computer vision tasks in precision agriculture aside from weed detection, such as disease identification in crops, plant health monitoring, and even crop counting – all in real-time and in an energy-efficient manner.

The AgriAdapt project results pave the way for new UAV solutions in precision agriculture that leverage the power of computer vision using regular, less expensive drones for real-time onboard processing. This translates to faster decision-making, more targeted interventions, and ultimately, a more sustainable and efficient approach to farming.

The AgriAdapt project was the winner of the AgroBiznis (finance.si) Agro Hi-Tech Competition 2023 for the “Best Idea” category.

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