Ribence Kadel

Abstract

The paper explores the integration of advanced technologies such as IoT, apps, machine learning, and image recognition in the development of a smart farming system for disease detection and crop protection in Nepalese agriculture. Emphasizing the importance of timely disease detection in crop management, the paper discusses the utilization of IoT-based sensors for real-time monitoring of crop health parameters. Furthermore, it examines the application of image recognition techniques and machine learning algorithms for automated disease detection and identification. By leveraging these technologies, the smart farming system aims to address disease control challenges, optimize resource utilization, and promote sustainable agricultural practices in Nepal. The use of IoT and ML for Smart Farming Systems using timely disease detection and further crop management has been theoretically discussed.

Paper Details