Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps.

Area of Science:

• Agricultural Science
• Remote Sensing
• Data Science

Background:

• Agricultural data acquisition is challenging due to vast, remote areas and weather vulnerability.
• Despite technological advances, data collection is geographically unequal, creating data deserts.
• Agricultural environments are complex, with numerous uncontrolled factors, leading to data and knowledge gaps.

Purpose of the Study:

• To provide a comprehensive overview of data fusion in agricultural applications.
• To highlight successes and remaining challenges in agricultural data fusion.
• To suggest future research directions for improving data fusion techniques in agriculture.

Main Methods:

• Review of data fusion techniques applied to agricultural problems.
• Analysis of sensor complementarities and synergies for enhanced information extraction.
• Exploration of proximal, aerial, satellite, and ancillary data integration.

Main Results:

• Data fusion offers a potential solution to limitations of single-sensor data in agriculture.
• Successful applications of data fusion have been demonstrated, extracting more reliable information.
• Significant challenges hinder widespread adoption, especially in complex agricultural settings.

Conclusions:

• Data fusion is crucial for overcoming data gaps and complexities in agricultural remote sensing.
• Further research is needed to address current challenges and promote broader implementation.
• Integrating diverse data sources is key to unlocking the full potential of agricultural data.