AI-Driven Bio-Soil Analysis for Predictive Crop Disease Control in Precision Agriculture

This research describes the AI-Driven Bio-Soil Analysis System (AB-SAS), which utilizes metagenomic biosensors, Graph Neural ‎Networks, and Transformer models to forecast and preempt crop diseases as they arise. AB-SAS advances the measurement and ‎assessment of soil health with its Dynamic Soil Health Index (DSHI) and predictive capabilities relative to diseases based on microbial ‎diversity, nutrient stasis, and soil moisture equilibrium. The system features an AI biostimulant dispenser aimed at sustainable risk ‎mitigation. Moreover, AB-SAS meets the demands for secure, traceable, and accountable datasets and streams for system users through ‎blockchain. AB-SAS extends integrated, real-time solutions to demands for crop health maintenance, yield enhancement, and sustainable ‎crop production. The AB-SAS has incorporated plans for versatility in design, thereby extending its use in various agricultural systems. The ‎system’s geo-referenced and rapid interval approaches enable optimized resource use while avoiding exhaustion and ecological harm. This ‎Integrated AB-SAS is prospective in improving the system’s precision in agriculture and the system’s ability to promote sustainable ‎practices. The system will provide farmers with possible and immediately implementable plans and information‎.