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

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 cm2/flower). Black, green, and blue nets promoted larger leaf areas (10 to 11 cm2/leaf), while the black net produced the largest flowers (18.6 cm2). Blue and red nets reduced cream (4.3 cm2) and purple (7.3 cm2) 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.