Economic impact of soil bioremediation with sugarcane monoculture using Artificial Intelligence

Resumen

Objective: Develop an intelligent system capable of predicting the best bioremediation strategy at the laboratory scale for a Vertisol soil sample with sugarcane monoculture, to achieve a target of 90 tons per hectare, with the aim of achieving an economic impact on sugarcane producers in the high mountains of the State of Veracruz.

Design/Methodology/Approach: The following soil bioremediation techniques are used: Bioremediation using Bacillus megaterium, bioremediation using vermicompost to promote bioaugmentation, and bioremediation using Azospirillum brasilense + Pantoea dispersa; to estimate the economic impact of using an intelligent system system for soil bioremediation with sugar cane monoculture.

Results: An intelligent system was developed for soil bioremediation, with the goal of achieving 90 tons of sugarcane per hectare, thus achieving a significant economic impact on sugarcane producers in the High Mountains Region of the State of Veracruz.

Limitations/Implications: To carry out the tests for each bioremediation, samples of five Vertisol soils from the High Mountain Region of Veracruz were placed, with different degrees of degradation and whose chemical characterization is already known for each soil type. A control sample was used for each sample, which was not modified in any way, but was simply placed under the same environmental and irrigation conditions as the other samples.

Findings/Conclusions: Software was developed that analyzes the physicochemical properties of the soil in order to recommend the most feasible bioremediation strategy to the user, with the goal of achieving 90 tons/hectare, representing an economic improvement of almost 30% for sugarcane producers.