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Model description

The system is designed to segment crops from the background in images collected by Unmanned Aerial Vehicles, (UAVs).

It employs a Deep Neural Network (DNN) with a U-NET model, an encoder-decoder cascade structure, for semantic segmentation. The system also utilizes the K-means algorithm for further segmentation of crops in RGB color images.

It is capable of processing images from different datasets and generalizing its performance across them.

The system has demonstrated more accurate segmentation and convincing results compared to traditional approaches, making it a valuable tool for precision farming and sustainable agriculture.

The model card is available here.

The data card is available here.

Team members

Eleonora Ghizzota
Eleonora Ghizzota
Master's Degree Student in Computer Science
Mariangela Panunzio
Mariangela Panunzio
Master's Degree Student in Computer Science
Katya Trufanova
Katya Trufanova
Master's Degree Student in Computer Science
Alberto G. Valerio
Alberto G. Valerio
Master's Degree Student in Computer Science
This demo-tool has been realized as a lab activity for the exam in Software Engineering for AI-Enabled Systems with Professor Filippo Lanubile at University of Bari "Aldo Moro", Italy.
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