Agriculture is under constant pressure to increase the production rate and to provide more food or resources for other industries. Without precision agriculture it is not possible to fulfil these requirements. Medium and large-size farms already adopted this technology, but small farms are far from precision agriculture due to the high initial costs. In this paper we present a bespoke and affordable multispectral camera for precision farming and illustrate its application in the detection of Flavescence dorée. This is a grapevine disease that makes a great concern to grapevine producers in the whole Mediterranean region. Flavescence dorée is the only quarantine disease in the European region. The related mandatory control procedures include uprooting every infected plant, with the obligation to uproot the vineyard when the infection exceed the 20% of threshold infection, hence resulting in a high economical loss. Thus, it is highly important to detect even a single infected plant over an entire vineyard plot, preventing the spreading of Flavescence dorée at earliest stages. We used the in-house developed multispectral camera together with a hyperspectral camera to acquire data from two vineyards near Riva del Garda, Trentino, Italy, during the summer of 2022. These data are the starting point for selecting the optimal spectral bands for the detection of Flavescence dorée using affordable multispectral approaches and developing an appropriate classification algorithm.

A Multispectral Acquisition System for Potential Detection of Flavescence Dorée / Barjaktarovic, Marko; Santoni, Massimo; Faralli, Michele; Bertamini, Massimo; Bruzzone, Lorenzo. - (2022), pp. 1-4. (Intervento presentato al convegno 30th Telecommunications Forum, TELFOR 2022 tenutosi a Beograd nel 15-16 November 2022) [10.1109/TELFOR56187.2022.9983685].

A Multispectral Acquisition System for Potential Detection of Flavescence Dorée

Barjaktarovic, Marko
Primo
;
Santoni, Massimo
Secondo
;
Faralli, Michele;Bertamini, Massimo;Bruzzone, Lorenzo
Ultimo
2022-01-01

Abstract

Agriculture is under constant pressure to increase the production rate and to provide more food or resources for other industries. Without precision agriculture it is not possible to fulfil these requirements. Medium and large-size farms already adopted this technology, but small farms are far from precision agriculture due to the high initial costs. In this paper we present a bespoke and affordable multispectral camera for precision farming and illustrate its application in the detection of Flavescence dorée. This is a grapevine disease that makes a great concern to grapevine producers in the whole Mediterranean region. Flavescence dorée is the only quarantine disease in the European region. The related mandatory control procedures include uprooting every infected plant, with the obligation to uproot the vineyard when the infection exceed the 20% of threshold infection, hence resulting in a high economical loss. Thus, it is highly important to detect even a single infected plant over an entire vineyard plot, preventing the spreading of Flavescence dorée at earliest stages. We used the in-house developed multispectral camera together with a hyperspectral camera to acquire data from two vineyards near Riva del Garda, Trentino, Italy, during the summer of 2022. These data are the starting point for selecting the optimal spectral bands for the detection of Flavescence dorée using affordable multispectral approaches and developing an appropriate classification algorithm.
2022
30th Telecommunications Forum -TELFOR 2022
Belgrade
Institute of Electrical and Electronics Engineers Inc.
978-1-6654-7273-9
Barjaktarovic, Marko; Santoni, Massimo; Faralli, Michele; Bertamini, Massimo; Bruzzone, Lorenzo
A Multispectral Acquisition System for Potential Detection of Flavescence Dorée / Barjaktarovic, Marko; Santoni, Massimo; Faralli, Michele; Bertamini, Massimo; Bruzzone, Lorenzo. - (2022), pp. 1-4. (Intervento presentato al convegno 30th Telecommunications Forum, TELFOR 2022 tenutosi a Beograd nel 15-16 November 2022) [10.1109/TELFOR56187.2022.9983685].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/373050
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