Toward Objective Variety Testing Score Based on Computer Vision and Unsupervised Machine Learning: Application to Apple Shape

Zine-El-Abidine M., DUTAĞACI H., Rasti P., Aranzana M. J., Dujak C., Rousseau D.

4th International Conference on Image Processing and Vision Engineering, IMPROVE 2024, Angers, Fransa, 2 - 04 Mayıs 2024, ss.15-22 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.5220/0012549700003720
  • Basıldığı Şehir: Angers
  • Basıldığı Ülke: Fransa
  • Sayfa Sayıları: ss.15-22
  • Anahtar Kelimeler: Apple Shape, Shape Description, Variety Classification, Variety Testing
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

While precision agriculture or plant phenotyping are very actively moving toward numerical protocols for objective and fast automated measurements, plant variety testing is still very largely guided by manual practices based on visual scoring. Indeed, variety testing is regulated by definite protocols based on visual observation of sketches provided in official catalogs. In this article, we investigated the possibility to shortcut the human visual inspection of these sketches and base the scoring of plant varieties on computer vision similarity of the official sketches with the plants to be inspected. A generic protocol for such a computer vision based approach is proposed and illustrated on apple shape classification. The proposed unsupervised algorithm is demonstrated to be of high value by comparison with classical supervised and self supervised machine and deep learning if some rescaling of the sketches is performed.