Smart Agriculture : A Novel Multilevel Approach for Agricultural Risk Assessment over Unstructured Data

TL;DR

This paper presents a multilevel AI-based approach to assess agricultural risks by extracting and analyzing unstructured text data, improving risk detection beyond traditional structured data methods.

Contribution

It introduces a novel multilevel framework utilizing NLP and machine learning to model uncertainties and evaluate risks from unstructured agricultural text data.

Findings

Effective extraction of risk-related information from unstructured text

Improved accuracy in risk assessment over traditional methods

Demonstrated scalability with large text datasets

Abstract

Detecting opportunities and threats from massive text data is a challenging task for most. Traditionally, companies would rely mainly on structured data to detect and predict risks, losing a huge amount of information that could be extracted from unstructured text data. Fortunately, artificial intelligence came to remedy this issue by innovating in data extraction and processing techniques, allowing us to understand and make use of Natural Language data and turning it into structures that a machine can process and extract insight from. Uncertainty refers to a state of not knowing what will happen in the future. This paper aims to leverage natural language processing and machine learning techniques to model uncertainties and evaluate the risk level in each uncertainty cluster using massive text data.