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Title: Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
Authors: Miragaia, Rolando Lúcio Germano
Chávez de la O, Francisco
Díaz Álvarez, Josefa
Vivas, Antonio
Henar Prieto, María
Moñino Espino, María José
Keywords: agriculture digitalization;precision agriculture;computer vision;plum orchard;Prunus salicina
Issue Date: 2021
Abstract: Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness ,three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.
ISSN: 2073-4395
DOI: 10.3390/ agronomy11112353
Appears in Collections:DTCYC - Artículos

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