In recent years, machine learning and deep learning techniques have emerged as effective tools to address the challenges related to smart and sustainable agriculture thanks to their ability to automate the task of processing various types of data (e.g., soil condition data and meteorological information) and transform this data into knowledge that will serve in the decision-making process. The aim of this paper is to showcase through a real case study and an exemplificative application how relevant issues related to the sustainable management of farming activities can be addressed in an effective way by employing deep learning algorithms. The real case study concerns the development of a vineyard yield prediction model using time series of satellite images whereas the exemplificative application showcases how deep learning techniques based on object detection can be usefully exploited to build a plant disease detection system.

Deep Learning for Smart and Sustainable Agriculture / Vanacore, Amalia; Ciardiello, Armando; Izzo, Annalisa; Zaffino, Pierdomenico; Vecchio, Carolina; Pio Auricchio and Luigi Uccello, Gennaro. - (2023), pp. 782-787. (Intervento presentato al convegno SIS 2023 - Statistical Learning, Sustainability and Impact Evaluation tenutosi a Ancona,Italy nel June 21-23, 2023).

Deep Learning for Smart and Sustainable Agriculture

Amalia Vanacore
;
Armando Ciardiello;
2023

Abstract

In recent years, machine learning and deep learning techniques have emerged as effective tools to address the challenges related to smart and sustainable agriculture thanks to their ability to automate the task of processing various types of data (e.g., soil condition data and meteorological information) and transform this data into knowledge that will serve in the decision-making process. The aim of this paper is to showcase through a real case study and an exemplificative application how relevant issues related to the sustainable management of farming activities can be addressed in an effective way by employing deep learning algorithms. The real case study concerns the development of a vineyard yield prediction model using time series of satellite images whereas the exemplificative application showcases how deep learning techniques based on object detection can be usefully exploited to build a plant disease detection system.
2023
9788891935618
Deep Learning for Smart and Sustainable Agriculture / Vanacore, Amalia; Ciardiello, Armando; Izzo, Annalisa; Zaffino, Pierdomenico; Vecchio, Carolina; Pio Auricchio and Luigi Uccello, Gennaro. - (2023), pp. 782-787. (Intervento presentato al convegno SIS 2023 - Statistical Learning, Sustainability and Impact Evaluation tenutosi a Ancona,Italy nel June 21-23, 2023).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/990782
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