Unraveling biotic and abiotic factors shaping sugarcane straw polyphenolic richness: a gateway to artificial intelligence-driven crop management

Sugarcane straw (Saccharum officinarum) is a valuable coproduct renowned for its abundant polyphenolic content. However, extracting these polyphenols for natural ingredients faces challenges due to their inherent variability, influenced by biotic stress factors and plant characteristics. We explored the impact of five crucial factors on sugarcane straw polyphenolic diversity: (i) production area (Guariba, Valparaíso), (ii) borer insect (Diatraea saccharalis) infestation, (iii) plant age (first to seventh harvest), (iv) harvest season, and (v) plant variety. Response surface methodology (RSM) and artificial neural networks (ANN) were used to optimize polyphenol extraction conditions. A second-order polynomial model guided us to predict ideal sugarcane straw harvesting conditions for polyphenol-rich extracts. The analysis identified CU0618-variety straw, harvested in Guariba during the dry season (October 2020), at the seventh harvest stage, with 13.81% borer insect infection, as the prime source for high hydroxybenzoic acid (1010 µg/g), hydroxycinnamic acid (3119 µg/g), and flavone (573 µg/g) content and consequently high antioxidant capacity. The ANN model surpasses the RSM model, demonstrating superior predictive capabilities with higher coefficients of determination and reduced mean absolute deviations for each polyphenol class. This underscores the potential of artificial neural networks in forecasting and enhancing polyphenol extraction conditions, setting the stage for AI-driven advancements in crop management.