Biodegradable Biofilms with AI-Driven CRISPR Interference for Regulating Resistant Starch Pathways and Predicting Geo-Spatial Pest Migration in Vegetative Andean Tuber Crops

This report explores the application of biodegradable biofilms, CRISPR interference (CRISPRi), and AI-based predictive modeling to boost the yield and nutritional content and pest resistance of Andean tuber crops. CRISPRi constructs on specific starch biosynthesis genes (GBSS, SBE, ISA) were delivered into the biodegradable biofilms, and thus time-dependent regulation of the gene was achieved by using these biofilms. An experiment involving three high-altitude regions showed that the biofilm + CRISPRi treatment
indeed raised the resistant starch proportion up to 17.4 g/100g, which is a 24% increase compared to the plots in control conditions. At the same time, there was the stimulation of crop production to 15.2 t/ha, and the reduction of pest occurrence to 31, which demonstrated synergistic advantage of metabolic control and specific pest control. Gene expression and geo-spatial pest migration were predicted using AI models which consisted of Random Forest, Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM). Random Forest predicted expression of GBSS at a rate of 0.91R 2, CNN identified pest species as high as 94% and SVM identified high-risk zones of pests at 87% accuracy. These findings prove the fact that using molecular interventions with AI-based predictive tools is the best way to increase crop quality and sustainability. The paper offers a scaled system of precision agriculture so that crops in high altitudes can perform better in terms of metabolic, less damage by pests and resilience to changing environmental conditions.