An ANALYSIS OF A BIOPESTICIDE WITH CONTROLLED DOSAGE OF BACILLUS THURINGIENSIS SUPPORTED ON HYBRID HYDROTALCITES USING ARTIFICIAL INTELLIGENCE.
DOI:
https://doi.org/10.56643/rcia.v4i1.201Keywords:
algorithmic optimization, biological control, clean technologies, machine learning, sustainable agricultureAbstract
Nowadays, controlled molecule dosage is not only essential for optimizing pharmacological processes but also a tool with great potential in agriculture. In this study, we explored how inorganic materials like layered double hydroxides (LDHs), known for their ability to adsorb bacteria, anions, and heavy metals, can be used as supports for a biopesticide based on Bacillus thuringiensis (Bt). The main goal was to analyze and improve the efficacy of this biopesticide using modern technologies. By applying a Random Forest-based artificial intelligence model, we simulated optimal dosage conditions and identified the most critical variables in the process. The results were clear: concentrations stood out as the most influential factor, while using LDHs as a support had a significant impact, accounting for 80% importance in the optimization. Additionally, the model achieved high precision, with a mean squared error (MSE) of 18.97 and a coefficient of determination (R2) of 0.87. These findings not only validate the biopesticide’s effectiveness but also highlight the importance of integrating artificial intelligence into hybrid material research. This approach enhances sustainable agriculture and opens the door to scaling these solutions for industrial applications, setting a foundation for future innovations in technological optimization.
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