Predicting Vase Life of Cut Lisianthus Based on Biomass-Related Characteristics Using AutoML

Hye Sook Kwon and Seong Heo ()
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Hye Sook Kwon: Department of Horticulture, Kongju National University, Yesan 32439, Republic of Korea
Seong Heo: Department of Horticulture, Kongju National University, Yesan 32439, Republic of Korea

Agriculture, 2024, vol. 14, issue 9, 1-15

Abstract: Lisianthus, a globally popular ornamental plant, has a variable vase life (5–28 days). This study investigated biomass-related characteristics of four cultivars grown in soil or hydroponic cultivation with different treatment timings (vegetative and reproductive stage) and concentrations (0, 0.1, 0.3, and 0.5 mM) of salicylic acid (SA) in order to explain vase life. The results show that the SA treatment effects varied depending on cultivar, SA treatment timing, concentration, and cultivation method. Principle component analysis revealed that Blue Picote cultivar cultivated hydroponically with 0.5 mM SA at the reproductive stage had the longest vase life. Furthermore, vase life demonstrated a high positive correlation with dry weight, SPAD, Mg content, and flowering day. We developed a model using automated machine learning algorithms to estimate postharvest vase life, based on biomass-related characteristics measured during the pre-harvest period. Similar to the PCA results, this model also identified dry weight as the most influential predictor of vase life. This model proposes the possibility of estimating vase life by setting characteristics highly correlated with vase life as features for machine learning. It is anticipated that this model will be widely utilized in the floriculture industry for standardizing cut flower quality assessments in the future.

Keywords: biomass; lisianthus; machine learning; salicylic acid; vase life (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: 2024
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