Advancing soil health: Challenges and opportunities in integrating digital imaging, spectroscopy, and machine learning for bio-indicator analysis

With the evolution of digital imaging technologies, our capacity to assess soil bio-indicators has significantly expanded. Whether it is through the use of optical tools capturing visible light in the time domain, such as cameras and optical microscopes, or instruments that function in the frequency domain, i.e., spectroscopies, we're now able to garner a richer understanding of soil health. The acquisition of digital data presents a thrilling new realm of possibilities. Through the seamless integration of machine learning (ML) and computer vision (CV), these data can be meticulously refined and interpreted. The union of ML and CV not only bolsters the accuracy of predictions but also paves the way for transitioning from time-consuming manual evaluations to swift, precise automated detections. This review delves deeper into the exciting potential of ML and CV for data processing in tandem with contemporary spectroscopy and imaging technologies.