Measurement and analysis of agricultural field state using cloud-based data processing pipeline Preventing potential robbery crimes using deep learning algorithm of data processing

Abstract

The increasing demand for precision agriculture has prompted the integration of advanced technologies to optimize agricultural practices. This article presents an approach to agricultural field data processing using a cloud-based data pipeline. The system leverages data from various sensors deployed in the fields to collect real-time information on key parameters such as soil moisture, temperature, humidity, etc. The collected data is transmitted to the cloud where it undergoes a series of data processing and analysis stages. The article demonstrates the effectiveness of the cloud-based data pipeline in enhancing agricultural resilience. It facilitates prompt decision-making by farmers and stakeholders based on real-time data analysis. Additionally, the system offers a valuable tool for monitoring and optimizing irrigation strategies, resource allocation, and crop management practices. This research highlights the potential of cloud-based data pipelines in revolutionizing precision agriculture. The ability to measure and analyze agricultural field data accurately and efficiently opens new avenues for sustainable farming practices and mitigating risks related to wildfires and droughts.