Multi-Sensor Fusion For Soil Quality Assessment: Challenges And Opportunities

Abstract

Soil quality assessment is vital for sustainable agricultural practices, environmental management, and food security. The conventional methods for soil analysis are timeconsuming, labor intensive, and cannot provide real-time data. This paper presents a comprehensive review of the emerging role of multi-sensor fusion in soil quality assessment, focusing on the unique challenges and untapped opportunities that it presents. We explore a variety of sensors, including but not limited to, optical, thermal, electrical, and electromagnetic sensors, and their collective potential when integrated for assessing soil quality parameters such as pH, moisture, nutrients, and organic matter content. Further, we discuss the advances in sensor fusion algorithms and machine learning techniques that are enabling precise, real-time soil analysis. The challenges related to data fusion, sensor calibration, system integration, and validation of sensorbased soil predictions are also addressed. We conclude with a forward-looking perspective, suggesting potential areas for future research and the implications of these technologies for precision agriculture and environmental management.