Machine Learning-Aided Optimization of In Vitro Tetraploid Induction in Cannabis

Abstract

Polyploidy, characterized by an increase in the number of whole sets of chromosomes in an organism, offers a promising avenue for cannabis improvement. Polyploid cannabis plants often exhibit altered morphological, physiological, and biochemical characteristics with a number of potential benefits compared to their diploid counterparts. The optimization of polyploidy induction, such as the level of antimitotic agents and exposure duration, is essential for successful polyploidization to maximize survival and tetraploid rates while minimizing the number of chimeric mixoploids. In this study, three classification-based machine learning algorithms-probabilistic neural network (PNN), support vector classification (SVC), and k-nearest neighbors (KNNs)-were used to model ploidy levels based on oryzalin concentration and exposure time. The results indicated that PNN outperformed both KNNs and SVC. Subsequently, PNN was combined with a genetic algorithm (GA) to optimize oryzalin concentration and exposure time to maximize tetraploid induction rates. The PNN-GA results predicted that the optimal conditions were a concentration of 32.98 µM of oryzalin for 17.92 h. A validation study testing these conditions confirmed the accuracy of the PNN-GA model, resulting in 93.75% tetraploid induction, with the remaining 6.25% identified as mixoploids. Additionally, the evaluation of morphological traits showed that tetraploid plants were more vigorous and had larger leaf sizes compared to diploid or mixoploid plants in vitro.

Keywords: classification; leaf-related morphological traits; optimization algorithm; oryzalin; plant tissue culture; polyploidy.

Figure 1

In vitro polyploidy induction in cannabis: (A) in vitro-grown plantlets with different ploidy levels, (B) plantlets with different ploidy levels after 6 weeks, (C) leaves of tetraploid, diploid, and mixoploid plantlets, and (D) leaf area (mm²) of tetraploid, diploid, and mixoploid plantlets.

Figure 2

Effect of different oryzalin concentrations at various exposure times on in vitro polyploidy induction in cannabis.

Figure 3

Leaf-related morphological traits in cannabis with different ploidy levels, including diploid, tetraploid, and mixoploid; (A) length of the terminal leaflet, (B) width of the terminal leaflet, (C) number of serrations of the terminal leaflet, (D) length of the right lateral leaflet, (E) width of the right lateral leaflet, (F) number of serrations of the right lateral leaflet, (G) length of the left lateral leaflet, (H) width of the left lateral leaflet, (I) number of serrations of the left lateral leaflet, (J) length of the terminal leaflet/width of terminal leaflet ratio, (K) length of the right lateral leaflet/width of right lateral leaflet ratio, (L) length of the left lateral leaflet/width of left lateral leaflet ratio.

Figure 4

Correlation between cannabis leaf-related morphological traits and ploidy levels. LA: leaf area; LLLL: left lateral leaflet length; LLLSN: left lateral leaflet serration number; LLLW: left lateral leaflet width; PL: ploidy level; RLLL: right lateral leaflet length; RLLSN: right lateral leaflet serration number; RLLW: right lateral leaflet width; TLL: terminal leaflet length; TLSN: terminal leaflet serration number; TLW: terminal leaflet width.

Figure 5

Schematic representation of the data-driven approach for modeling and optimizing in vitro tetraploid induction in cannabis; (A) different treatments for generating the dataset, (B) the distribution of the plantlets in each ploidy level, (C) probabilistic neural network, (D) support vector classification, (E) k-nearest neighbors, (F) genetic algorithm, and (G) validation experiment.

Figure 6

Schematic representation of the experimental methodology for in vitro tetraploid induction in cannabis, assessment of ploidy level, and measurement of leaf-related morphological traits. The scheme was created using BioRender.com.