Krishi Prahari: An IoT and ML-Based Framework for Smart Agricultural Threat Detection and Resource Optimization

Agriculture remains the backbone of India’s economy, but it is increasingly vulnerable to a variety of critical threats such as climate variability, crop fires, wild animal intrusions, and poor resource utilization. These challenges not only reduce crop yield but also lead to long-term damage to soil fertility, farmer income instability, and increased water consumption. In this context, the paper introduces Krishi Prahari, an intelligent, IoT and Machine Learning-powered agricultural management system developed to detect threats at an early stage and optimize decision-making processes in real time. Using an array of linked sensors, which input data into machine learning algorithms for predictive analysis, the system is meant to constantly monitor environmental conditions. This provides for early alerts for fire threats, tracking of animal movement in agricultural areas, and irrigation optimization dependant on soil conditions. By providing a proactive, automated, low-cost strategy adaptable across multiple climatic and geographical zones, the recommended solution fills in the deficiencies in existing reactive solutions. Water consumption efficiency has increased considerably, crop protection from animals and fire has been boosted, and local farmers' simplicity of use has been proven by rigorous field testing. By strengthening farm-level situational awareness and resource management capacities, Krishi Prahari consequently provides a crucial step toward obtaining climate-resilient and precision agriculture in India [1].