Soil Analysis for Crop Recommendation using Machine Learning Algorithms

Abstract

Modern surveillance systems often struggle to detect and respond to threats in real time. This paper introduces a system that uses context-aware threat intelligence to detect weapons, recognize violent actions, analyze time-based patterns, and assess potential threats based on the situation, all to spot unusual behaviour in real-time video feeds. The proposed system utilizes YOLO-based deep learning models to detect weapons and classify human behaviour. On the other hand, a scoring engine determines the level of risks from the objects observed, the behaviour pattern, the location, and past occurrences. High-risk occurrences will automatically be accompanied by raising an alarm, collecting the evidence, reporting the occurrence, and sending emails. In order to foster openness in the process and increase the level of confidence, Explainable Artificial Intelligence (XAI) methods including SHAP (SHapley Additive exPlanations) and Grad-CAM are applied to enhance transparency. Also, a real-time dashboard for incidents is put up for monitoring and analyzing incidents.