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Başlık: Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network
Yazarlar: Lee, Won Suk
Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.
0000-0001-6349-9687
Kurtulmuş, Ferhat
Vardar, Ali
R-8053-2016
AAH-5008-2021
15848202900
15049958800
Anahtar kelimeler: Computer vision
Fruit detection
Immature peach
Yield mapping
Statistical classifiers
Trees
Fruit
Agriculture
Prunus persica
Color
Image analysis
Mapping
Pattern recognition
Vector
Yield
Yayın Tarihi: Şub-2014
Yayıncı: Springer
Atıf: Kurtulmuş, F. vd. (2014). "Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network". Precision Agriculture, 15(1), Special Issue, 57-79.
Özet: Detection of immature peach fruits would help growers to create yield maps which are very useful tools for adjusting management practices during the fruit maturing stages. Machine vision algorithms were developed to detect and count immature peach fruit in natural canopies using colour images. This study was the first effort to detect immature peach fruit in natural environment to the authors' knowledge. Captured images had various illumination conditions due to both direct sunlight and diffusive light conditions that make the fruit detection task more difficult. A training set and a validation set were used to develop and to test the algorithms. Different image scanning methods including finding potential fruit regions were developed and used to parse fruit objects in the natural canopy image. Circular Gabor texture analysis and 'eigenfruit' approach (inspired by the 'eigenface' face detection and recognition method) were used for feature extraction. Statistical classifiers, a neural network and a support vector machine classifier were built and used for detecting peach fruit. A blob analysis was performed to merge multiple detections for the same peach fruit. Performance of the classifiers and image scanning methods were introduced and evaluated. Using the proposed algorithms, 84.6, 77.9 and 71.2 % of the actual fruits were successfully detected using three different image scanning methods for the validation set.
URI: https://doi.org/10.1007/s11119-013-9323-8
https://link.springer.com/article/10.1007/s11119-013-9323-8
http://hdl.handle.net/11452/28202
ISSN: 1385-2256
1573-1618
Koleksiyonlarda Görünür:Scopus
Web of Science

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