Phenotypic Descriptors and Image-Based Assessment of Viola cornuta L. Quality Under Photoselective Shade Nets Using a Naive Bayes Classifier

Fátima Alejandrina Hagg-Torrijos, José Alfredo Carrillo-Salazar (), Juan Manuel González-Camacho and Víctor Arturo González-Hernández
Additional contact information
Fátima Alejandrina Hagg-Torrijos: Crop Modelling, Colegio de Postgraduados, Campus Montecillo, Carretera Mexico-Texcoco km 38.5, Montecillo, Texcoco C.P. 56230, Mexico
José Alfredo Carrillo-Salazar: Crop Modelling, Colegio de Postgraduados, Campus Montecillo, Carretera Mexico-Texcoco km 38.5, Montecillo, Texcoco C.P. 56230, Mexico
Juan Manuel González-Camacho: Applied Computer Science, Colegio de Postgraduados, Campus Montecillo, Carretera Mexico-Texcoco km 38.5, Montecillo, Texcoco C.P. 56230, Mexico
Víctor Arturo González-Hernández: Plant Physiology, Colegio de Postgraduados, Campus Montecillo, Carretera Mexico-Texcoco km 38.5, Montecillo, Texcoco C.P. 56230, Mexico

Agriculture, 2025, vol. 15, issue 21, 1-15

Abstract: Light quality affects the visual and morphological traits of ornamental species, and its effects on plant quality can be quantitatively assessed using image analysis combined with machine-learning classifiers. The objective was to characterize the morphological, reproductive, and color-related traits of Viola cornuta L. cv. Sorbeth ® Coconut ® Swirl ® grown under red, black, green, and blue shade nets in open-field conditions in Montecillo, Mexico, based on the combined use of traditional measurements and image-based analysis. Measurements were taken 69 days after transplanting. Image analysis with a multiclass Naive Bayes classifier (98.9% accuracy) quantified flower area and the three color classes (yellow, cream, and purple). Leaf area and ground cover were measured through color-based segmentation and adaptative thresholding. Open-field plants showed the largest ground cover, with flowers (19.4%), compact canopy (37% smaller than under the black net), and the highest number of flowers (33 flowers/plant). The yellow floral area was also the largest (0.3 cm 2 /flower). Black, green, and blue nets promoted larger leaf areas (10 to 11 cm 2 /leaf), while the black net produced the largest flowers (18.6 cm 2 ). Blue and red nets reduced cream (4.3 cm 2 ) and purple (7.3 cm 2 ) areas, respectively. Photoselective nets differentially modulated viola morphology and pigmentation, while open-field conditions yielded compact plants with large flower areas of the highest visual quality.

Keywords: plant quality; plant morphology; image analysis; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/21/2187/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/21/2187/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:21:p:2187-:d:1777617

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().