AI-Powered Intrusion Detection Framework For Smart Agriculture In Remote And Disaster-Prone Area

Smart agriculture utilizes IoT devices, sensors, and wireless communication to enhance crop yield and promote sustainable farming methods. In India, the impacts of climate change have notably influenced key crops, leading to inconsistent yields over the past few decades. Precise crop yield forecasting prior to harvest is crucial for effective planning and resource allocation. This study introduces an AI-powered Intrusion Detection System (IDS) aimed at securing smart agriculture networks while facilitating intelligent crop yield forecasting. The system integrates IoT-driven agricultural data with machine learning approaches. A Random Forest algorithm is utilized to predict crop yields with a high level of accuracy. An interactive web-based platform has been created to offer a user-friendly experience for farmers and stakeholders. Standard datasets like NSL-KDD are employed to train and assess the IDS module. The IDS consistently supervises the IoT network. The proposed system improves overall agricultural productivity. It also strengthens cybersecurity in smart farming environments. The approach promotes sustainable and data-driven agriculture.