IoT Based Smart Agriculture To Avoid Post Harvest Losses Using Machine Learning Algorithms

Abstract

This research paper explores the potential of using Internet of Things (IoT) technology to reduce post-harvest losses in the agriculture sector. Post-harvest losses have been a major challenge in the agricultural industry, leading to significant economic losses and food waste. Post-harvest losses (PHL) refer to the significant reduction in the quantity and quality of food produced from the time it is harvested until it is consumed. The paper provides a detailed analysis of the current state of post-harvest losses in the agricultural sector, the potential benefits of IoT technology in reducing these losses, and the existing IoT-based solutions that are already in use. The research also highlights the various challenges and limitations of implementing IoT technology in agriculture and provides recommendations on how to overcome them. The findings of this research have significant implications for the agricultural industry and can help stakeholders in the sector to make informed decisions about adopting IoT-based solutions to reduce post-harvest losses. Machine learning(ML) based model generated to identify and avoid losses association with IoT devices. The proposed Random Forest ML based methodology evaluate the results and report the alert messages to farmers for decision making purposes.