RT info:eu-repo/semantics/article
T1 An automatic non-destructive method for the classification of the ripeness stage of red delicious apples in orchards using aerial video
A1 Sabzi, Sajad
A1 Abbaspour Gilandeh, Yousef
A1 García Mateos, Ginés
A1 Ruiz Canales, Antonio
A1 Molina Martínez, José Miguel
A1 Arribas Sánchez, Juan Ignacio
K1 Fruit ripeness
K1 Frutas - Maduración
K1 Aerial videos
K1 Video aéreo
K1 5312.01 Agricultura, Silvicultura, Pesca
K1 3325 Tecnología de las Telecomunicaciones
AB 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.
PB MDPI
SN 2073-4395
YR 2019
FD 2019
LK https://uvadoc.uva.es/handle/10324/56434
UL https://uvadoc.uva.es/handle/10324/56434
LA eng
NO Agronomy, 2019, vol. 9, n. 2, 84
NO Producción Científica
DS UVaDOC
RD 27-dic-2024