Year: 2025
Type: article
Abstract: 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.
DOI: 10.14419/8j7gyr88
Institution: Kalinga University
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDGs: Zero hunger, Life on land
Open Access status: diamond
OpenAlex ID: W4415764523
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