Applying Knowledge Distillation to Improve Weed Mapping With Drones

• Autorzy: Castellano, Giovanna , De Marinis, Pasquale , Vessio, Gennaro
• Języki publikacji: EN

Abstrakty

EN

Non-invasive remote sensing using UAVs can be used in precision agriculture to observe crops in visible and non-visible spectra. This paper investigates the effectiveness of state-of-the-art knowledge distillation techniques for mapping weeds with drones, an essential component of precision agriculture that employs remote sensing to monitor crops and weeds. The study introduces a lightweight Vision Transformer-based model that achieves optimal weed mapping capabilities while maintaining minimal computation time. The research shows that the student model effectively learns from the teacher model using the WeedMap dataset, achieving accurate results suitable for mobile platforms such as drones, with only 0.5 GMacs compared to 42.5 GMacs of the teacher model. The trained models obtained an F1 score of 0.863 and 0.631 on two data subsets, with a performance improvement of 2 and 7 points, respectively, over the undistilled model. The study results suggest that developing efficient computer vision algorithms on drones can significantly improve agricultural management practices, leading to greater profitability and environmental sustainability.

Słowa kluczowe

PL

wizja komputerowa; opłacalność; modelowanie obliczeniowe; zielone produkty; uprawa; transformator; model danych

EN

computer vision; profitability; computational modeling; green products; crop; transformer; data model

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 393--400
• Opis fizyczny: Bibliogr. 31 poz., wz., tab., il.

Twórcy

autor

Castellano, Giovanna

• Department of Computer Science University of Bari Aldo Moro Bari, Italy,

autor

De Marinis, Pasquale

• Department of Computer Science University of Bari Aldo Moro Bari, Italy,

autor

Vessio, Gennaro

• Department of Computer Science University of Bari Aldo Moro Bari, Italy,

Bibliografia

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Uwagi

1. The research of Pasquale De Marinis is funded by a Ph.D. fellowship within the framework of the Italian “D.M. n. 352, April 9, 2022” - under the National Recovery and Resilience Plan, Mission 4, Component 2, Investment 3.3 - Ph.D. Project “Computer Vision techniques for sustainable AI applications using drones”, co-supported by “Exprivia S.p.A.” (CUP H91I22000410007).

2. Thematic Tracks Regular Papers

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Identyfikatory

DOI

10.15439/2023F960