Study and classication of plum varieties using image analysis and deep learning techniques
Authors:
Francisco J. Rodríguez. Department of Computer Science and Telematics, University of Extremadura, Mérida (Spain) Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
Antonio García. Mérida Campus, University of Extremadura, Mérida (Spain). Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
Pedro J. Pardo. Department of Computer Science and Telematics, University of Extremadura, Mérida (Spain). Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
Francisco Chávez. Department of Computer Science and Telematics, University of Extremadura, Mérida (Spain) Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
Rafael M. Luque-Baena. Department of Computer Languages and Computer Science, University of Málaga, Málaga (Spain) Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
Abstract.
Currently much of the pre-harvest fruit valuation is still done by farmers or technicians that visually inspect the pieces of fruit. However, this process has great limitations since their decisions have high subjectivity and a thorough analysis of the whole production, or even a significant part of it, is unapproachable. Therefore, computer vision and machine learning techniques are increasingly being introduced into this process.
In this work, we deal with the problem of automatically identifying plum varieties at early maturity stages, which is even difficult for the human expert. To face that identification, we propose a two-step procedure. Firstly, captured imaged are processed to identify the region where the plum appears. Secondly, we determine the plum variety using a deep convolutional neural network. Experimental results show that the proposed system achieves a remarkable behavior, with accuracy values that range from 91\% to 97\%.