“…Artificial intelligence models and optimization algorithms provide a complementary outlook for calibrating in vitro protocols, as these algorithms find optimal solutions in terms of genotype, explant source, plant growth regulators, medium composition, and incubation conditions, without the requirement for large-scale, costly, time-consuming, and tedious experimental trials [ 95 , 121 ]. Recently, different machine learning algorithms have been successfully used for predicting and optimizing different in vitro culture processes such as shoot proliferation [ 122 , 123 , 124 , 125 , 126 ], callogenesis [ 127 , 128 ], somatic embryogenesis [ 129 ], secondary metabolite production [ 130 , 131 , 132 ], and gene transformation [ 133 ]. Hence, the combination of the experimental approach and machine learning algorithms can be considered a powerful and reliable method to develop a specific protocol for cannabis.…”