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Open AccessArticle

An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video

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Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
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Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain
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Engineering Department, Miguel Hernandez University of Elche, 03312 Orihuela, Spain
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Food Engineering and Agricultural Equipment Department, Technical University of Cartagena, 30203 Cartagena, Spain
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Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, University of Valladolid, 47011 Valladolid, Spain
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Castilla-León Neuroscience Institute (INCYL), University of Salamanca, 37007 Salamanca, Spain
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Author to whom correspondence should be addressed.
Agronomy 2019, 9(2), 84; https://doi.org/10.3390/agronomy9020084
Received: 22 January 2019 / Revised: 5 February 2019 / Accepted: 12 February 2019 / Published: 14 February 2019
(This article belongs to the Section Innovative Cropping Systems)
The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes. Moreover, aerial images and the estimated ripeness can be used as indicators for detecting water stress and determining the water applied during irrigation. Additionally, they can also be related to the crop coefficient (Kc) of seasonal water needs. The purpose of this research is to develop a new computer vision algorithm to detect the existing fruits in aerial images of an apple cultivar (of Red Delicious variety) and estimate their ripeness stage among four possible classes: unripe, half-ripe, ripe, and overripe. The proposed method is based on a combination of the most effective color features and a classifier based on artificial neural networks optimized with genetic algorithms. The obtained results indicate an average classification accuracy of 97.88%, over a dataset of 8390 images and 27,687 apples, and values of the area under the ROC (receiver operating characteristic) curve near or above 0.99 for all classes. We believe this is a remarkable performance that allows a proper non-intrusive estimation of ripening that will help to improve harvesting strategies. View Full-Text
Keywords: ripeness estimation; apple segmentation; agricultural video processing; neural networks; genetic algorithms; color classification ripeness estimation; apple segmentation; agricultural video processing; neural networks; genetic algorithms; color classification
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Sabzi, S.; Abbaspour-Gilandeh, Y.; García-Mateos, G.; Ruiz-Canales, A.; Molina-Martínez, J.M.; Arribas, J.I. An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video. Agronomy 2019, 9, 84.

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  • Externally hosted supplementary file 1
    Link: https://youtu.be/A3ROtqRy9os
    Description: Sample video sequence after application of the proposed apple ripening classification.
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