Machine Learning-Based System To Monitor, Analyze Metrics, And Reduce Disaster Risks In Crop Leaching From Rainfall

Subhakar. M; Dr.Muthireddy. P

doi:10.29284/h2afrn78

Authors

Subhakar. M Research scholar, School of Computer Science and Engineering ,REVA University, Rukmini Knowledge Park, Yelahanka, Kattigenahalli, Bengaluru, Sathanur, Karnataka 560064
Dr.Muthireddy. P Professor, School of Computer Science and Engineering, REVA University, Rukmini Knowledge Park, Yelahanka, Kattigenahalli, Bengaluru, Sathanur, Karnataka 560064

DOI:

https://doi.org/10.29284/h2afrn78

Keywords:

Drone-based intervention, IoT communication, Nutrient leaching management, Rainfall prediction, Smart agriculture.

Abstract

Rainfall prediction plays a crucial role in various sectors such as agriculture, water resource management, and disaster preparedness. Accurately forecasting rainfall within specific spatial constraints (such as a radius and height) is essential for making informed decisions and mitigating potential risks. Machine learning algorithms have demonstrated promising results in predicting complex meteorological phenomena, including rainfall patterns. This study analyses how machine learning (ML), meteorological data and IoT technology in a smart agriculture system to find risks from rainfall as soon as they occur. It aims to achieve three key results: first, creating a rainfall prediction system using meteorological data and advanced ML models to give localized forecasts; second, establishing a communication system using IoT and ICT that sends local alerts to farmers and authorities through SMS, voice prompts and apps; and third, deploying biodegradable thin membrane wrappers from meteorological data to save crops from water and nutrient loss. It is necessary to invest in drone teams, many environmental sensors and ongoing use of AI-based software to use precision agriculture tools. Cloud services are used for storing and analysing past data, presenting it to users, visualizing information and monitoring from anywhere. All of these components are part of a flexible architecture that supports ongoing environmental monitoring, forecasting, two-way communication and automatic actions in the field. The framework, by combining digital progress and sustainability, provides a strong answer for agriculture that can weather climate change and reduce risks of disasters.

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