Efficient Deep Learning Approach for Olive Disease Classification

• Autorzy: Bruno Antonio , Moroni Davide , Martinelli Massimo

Identyfikatory

DOI

10.15439/2023F4794

• Języki publikacji: EN

Abstrakty

EN

Olive culture is one of the most important for the Mediterranean countries. In recent years, the role of Artificial Intelligence in agriculture is increasing: its use ranges from monitoring of cultivated soil, to irrigation management, to yield prediction, to autonomous agricultural robots, to weed and pest classification and management for example by taking pictures using a standard smartphone or a unmanned aerial vehicle, and all this eases human work and makes it even more accessible. In this work, we propose a method for olive diseases classification, based on an adaptive ensemble of two EfficientNet-b0 models, that improves the state-of-the-art accuracy on a publicly available dataset by 1.6-2.6%. Both in terms of number of parameters and on number of operations, our method reduces complexity roughly by 50% and 80\% respectively, that is a level not seen in at least a decade. Due to its efficiency, this method is also embeddable into a smartphone application for real-time processing.

Słowa kluczowe

EN

adaptation model; irrigation; soil; predictive models; real-time systems; complexity theory; ensemble learning

PL

model adaptacyjny; nawadnianie; gleba; modele predykcyjne; systemy czasu rzeczywistego; teoria złożoności; uczenie zespołowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 889--894
• Opis fizyczny: Bibliogr. 31 poz., il.

Twórcy

autor

Bruno Antonio

• Institute of Information Science and Technologies National Research Council Via Moruzzi 1, Pisa, Italy

autor

Moroni Davide

• Institute of Information Science and Technologies National Research Council Via Moruzzi 1, Pisa, Italy

autor

Martinelli Massimo

• Institute of Information Science and Technologies National Research Council Via Moruzzi 1, Pisa, Italy

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Uwagi

1. Thematic Tracks Short Papers

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