Deep neural network and precision agriculture for grape yield estimation

Coviello, Luca (2018). Deep neural network and precision agriculture for grape yield estimation. Thesis (Master thesis), E.T.S.I. de Sistemas Informáticos (UPM).

Description

Title: Deep neural network and precision agriculture for grape yield estimation
Author/s:
  • Coviello, Luca
Contributor/s:
  • Precioso, Frédéric
  • Furlanello, Cesare
Item Type: Thesis (Master thesis)
Masters title: Data Science
Date: 2018
Subjects:
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

An interesting trend with high economic and societal impact that emerged in recent years is Precision Agriculture, namely the employment of digital technologies to better assess the conditions of agricultural fields and improve production processes. By adopting Precision Agriculture it is possible to increase productivity while reducing the amount of treatment on crops, eventually increasing availability of safer food at lower costs. This revolution is based on a systematic use of technology, including the widespread adoption of sensors, both in-field and in lab for quality control processes. In addition to the expensive and highly accurate instruments used in lab, sensors on portable devices are constantly being developed in Precision Agriculture to support quality control, to dramatically reduce costs and obtain results which are comparable to the ones obtained in labs with traditional technologies. One important and appealing opportunity for farmers is to employ the smartphone they already have and use in their daily activities, with the addition of ad hoc technologies that can help boost their productivity.

More information

Item ID: 56667
DC Identifier: http://oa.upm.es/56667/
OAI Identifier: oai:oa.upm.es:56667
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 04 Oct 2019 07:56
Last Modified: 04 Oct 2019 07:56
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