This research introduces a method to help improve quinoa farming in Puno Perú area by blending programming and machine learning methods to tackle issues faced in today’s global agricultural markets efficiently and effectively using information from a span of 26 years. Through our work and study details over these years we formulated a model that balances maximizing profits while handling market fluctuations effectively and efficiently Our results highlighted that market prices for quinoa are heavily impacted by market dynamics rather, than local production conditions (correlation coefficient; 0.147; significance level; p > 0.05).The understanding influenced the linear approach that determined the best prices for selling and break even points effectively surpass traditional techniques by utilizing machine learning models while ensemble approaches showed better performance outcomes surpass the average methods used in the industry. Moreover, reactionary models identified changes in market trends accurately while long short-term memory networks forecasted price fluctuation tendencies.

The coronavirus (COVID-19) pandemic continues to claim victims. According to the World Health Organization, in the 28 days leading up to 25 February 2024 alone, the number of deaths from COVID-19 was 7141. In this work, we aimed to model the waves of COVID-19 through artificial neural networks (ANNs) and the sigmoidal–Boltzmann model. The study variable was the global cumulative number of deaths according to days, based on the Peru dataset. Additionally, the variables were adapted to determine the correlation between social isolation measures and death rates, which constitutes a novel contribution. A quantitative methodology was used that implemented a non-experimental, longitudinal, and correlational design. The study was retrospective. The results show that the sigmoidal and ANN models were reasonably representative and could help to predict the spread of COVID-19 over the course of multiple waves. Furthermore, the results were precise, with a Pearson correlation coefficient greater than 0.999. The computational sigmoidal–Boltzmann model was also time-efficient. Moreover, the Spearman correlation between social isolation measures and death rates was 0.77, which is acceptable considering that the social isolation variable is qualitative. Finally, we concluded that social isolation measures had a significant effect on reducing deaths from COVID-19.