Saffron In Vitro Propagation: An Innovative Method by Temporary Immersion System (TIS), Integrated with Machine Learning Analysis

Abstract

The propagation of Crocus sativus L. relies exclusively on corm multiplication. As underground storage organs, corms are susceptible to a wide range of pathogens, environmental stresses, and diseases, making traditional propagation methods often ineffective with the loss of valuable material. In vitro propagation offers an alternative for the saffron culture under controlled conditions. In particular, the innovative application of the Temporary Immersion System (TIS) represents a technological advancement for enhancing biomass production with a reduction in operational costs. The current study utilized the Plantform™ bioreactor to propagate in vitro saffron corms from the ‘Abruzzo’ region (Italy), integrating machine learning models to assess its performance. The evaluation of saffron explants after 30, 60, and 90 days of culture showed a marked improvement in growth and microcorm production compared to conventional in vitro culture on semisolid medium, supported by the machine learning analysis. Indeed, the Random Forest algorithm revealed a predictive accuracy with an R2 value of 0.81 for microcorm number, showcasing the capability of machine learning models to forecast propagation outcomes effectively. These results confirm that applying TIS in saffron culture could lead to economically viable, large biomass production within a controlled environment, irrespective of seasonality. This study represents the first endeavor to use TIS technology to enhance the in vitro propagation of saffron in conjunction with machine learning, suggesting an innovative approach for cultivating high-value crops like saffron.

Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation

“…Additionally, ML has optimized somatic embryogenesis [ 45 , 46 ] and haploid production [ 47 , 48 ]. Furthermore, ML has been used to improve gene transformation efficiency [ 49 , 50 ], indirect shoot regeneration [ 51 ], root formation [ 52 , 53 ] and, secondary metabolite production [ 54 ], shoot proliferation [ 45 , 46 , 55 – 57 ]; these applications highlight the versatility and effectiveness of ML in improving in vitro culture outcomes. By leveraging ML techniques, this study aims to enhance the precision and predictive power in assessing genotype-specific responses to cd stress in goji berry micropropagation, thereby providing valuable insights for developing more resilient crop varieties.…”

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“…Additionally, ML has optimized somatic embryogenesis [45,46] and haploid production [47,48]. Furthermore, ML has been used to improve gene transformation efficiency [49,50], indirect shoot regeneration [51], root formation [52,53] and, secondary metabolite production [54], shoot proliferation [45,46,[55][56][57]; these applications highlight the versatility and effectiveness of ML in improving in vitro culture outcomes. By leveraging ML techniques, this study aims to enhance the precision and predictive power in assessing genotype-specific responses to cd stress in goji berry micropropagation, thereby providing valuable insights for developing more resilient crop varieties.…”

Untitled

“…Additionally, ML has optimized somatic embryogenesis [45,46] and haploid production [47,48]. Furthermore, ML has been used to improve gene transformation efficiency [49,50], indirect shoot regeneration [51], root formation [52,53] and, secondary metabolite production [54], shoot proliferation [45,46,[55][56][57]; these applications highlight the versatility and effectiveness of ML in improving in vitro culture outcomes. By leveraging ML techniques, this study aims to enhance the precision and predictive power in assessing genotype-specific responses to cd stress in goji berry micropropagation, thereby providing valuable insights for developing more resilient crop varieties.…”