Published Paper Details:

This literature review examines the integration of artificial intelligence (AI), sensor networks, and IoT technologies in modern agriculture, with a focus on weed detection, animal intrusion prevention, irrigation automation, and soil health monitoring. It analyzes recent journal publications (2020-2025) that utilize deep learning models such as CNN and YOLOv5/YOLOv8 for real-time object detection, along with smart sensors for environmental data collection. Mobile applications and microcontrollers like Raspberry Pi and NodeMCU are also evaluated for automation and farmer interaction. The review highlights the limitations of existing systems--including poor cross-domain performance, reliance on internet connectivity, and lack of regional language support--and identifies the need for an integrated, edge-based, multi-functional solution like the Krishi ????? - The AgriFusion project. This study is intended to guide future work on accessible and sustainable smart farming platforms for smallholder farmers.