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

• Published in: Agronomy (Basel) Vol. 9; no. 2; p. 84
• Main Authors: Sabzi, Sajad, Abbaspour-Gilandeh, Yousef, García-Mateos, Ginés, Ruiz-Canales, Antonio, Molina-Martínez, José, Arribas, Juan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2019
• Subjects: agricultural video processing; Algorithms; Altitude; apple segmentation; Apples; Artificial neural networks; Classification; color classification; Computer vision; Cultivars; Dielectric properties; Drones; Fruits; Genetic algorithms; Harvest; Harvesting; Image classification; Irrigation; Irrigation water; Management decisions; Neural networks; Nondestructive testing; Orchards; ripeness estimation; Ripening; Satellites; Sensors; Unmanned aerial vehicles; Vision systems; Water demand; Water stress; United States > US
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy9020084

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.