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Quantifying the Production of Fruit-Bearing Trees Using Image Processing Techniques

Authors:
Laura García
Lorena Parra
Daniel A. Basterrechea
Jose M. Jimenez
Javier Rocher
Mar Parra
José Luis García-Navas
Sandra Sendra
Jaime Lloret
Pascal Lorenz
Jesus Tomás
Abdelhafid Abouaissa
Miguel Rodilla
Silvia Falco
María Teresa Sebastiá
Jesus Mengual
Juan Andrés González
Bernat Roig

Keywords: Persimmon; fruit; image processing; RGB bands, histogram.

Abstract:
In recent years, the growth rate of world agricultural production and crop yields have decreased. Crop irrigation becomes essential in very dry areas and where rainfall is scarce, as in Egypt. Persimmon needs low humidity to obtain an optimal crop. This article proposes the monitoring of its performance, in order to regulate the amount of water needed for each tree at any time. In our work we present a technique that consists of obtaining images of some of the trees with fruit, which are subsequently treated, to obtain reliable harvest data. This technique allows us to have control and predictions of the harvest. Also, we present the results obtained in a first trial, through which we demonstrate the feasibility of using the system to meet the objectives set. We use 5 different trees in our experiment. Their fruit production is different (between 20 and 47kg of fruit). The correlation coefficient of the obtained regression model is 0.97.

Pages: 14 to 19

Copyright: Copyright (c) IARIA, 2019

Publication date: November 24, 2019

Published in: conference

ISSN: 2326-9286

ISBN: 978-1-61208-758-0

Location: Valencia, Spain

Dates: from November 24, 2019 to November 28, 2019