CONFERENCE / ICCAIS-2026

Agriculture is increasingly affected by climate inconsistency, soil degradation, and pest irruption, which together pose serious threats to crop productivity and farmer livelihoods. Traditional farming methods depend heavily on experience and static advisory mechanisms, which are inefficient in the context of rapidly changing environmental conditions. This paper presents AgriSense AI, an app-based agricultural early warning and decision support system that integrates machine learning models with publicly available weather, soil, and agricultural datasets. The proposed system predicts short-term weather conditions, assesses soil health, and estimates pest risk levels to provide timely advisories to farmers. Unlike existing approaches that rely on IoT hardware, AgriSense AI strengthen affordability, scalability, and accessibility by delivering predictions through a mobile application. Conceptual and expected results indicate that the system can support data-driven agricultural decision-making and contribute to sustainable farming practices.