# Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture
## 4 Current Applications and Future Perspectives
### Other Methods
Nevertheless, Raman is still a very costly technique, and its application under real-life conditions warrants further research and new approaches that allow its upgrade from a \"proof of concept\" to their integration in plant sciences and agriculture in field-grown crops. This warrants the use of advanced analytical tools, such as the Internet of things (IoT), the incorporation of AI techniques to evaluate the spectral data acquired and assure calibration models with enough accuracy, and robustness to withstand all the biological variability met across various species, environmental conditions, farming sites and seasons [73; 97]. Then, all data produced by this analysis must be translated into meaningful and user-friendly information for plant science researchers, producers and packinghouses dealing with the crop supply chains. Although Raman spectroscopy has been successfully tested for remote sensing of some materials, such as minerals [104; 105], its use regarding plant systems has not yet been established. As far as botany focuses on spectroscopy of complicated organic molecules, the wavelength-shifting light-matter interactions manifest themselves in wide spectral intervals as fluorescence. Here, the lower reflection efficiency (with respect to the linear photon scattering process) is compensated in part by the fact that several characteristic substances, predominantly pigments and proteins, presented in plants have a high quantum yield of the fluorescence emission. Of these, the most prominent is chlorophyll, which provides information-rich and characteristic peaks in the visible and infrared spectral ranges. With the advent of the laser, capable of irradiating the sample by extremely bright light, laser-induced fluorescence (LIF) has become one of the most popular methods of spectroscopic studies. The present achievements of LIF include very reliable and non-destructive in vivo assessment of drought stress in natural and genetically modified plants [106; 107; 108]; quite selective characterization of the algae intoxication by a wide range of contaminants, such as Co, Cd, Cr, Cu, Ni, Hg, Pb, and Zn [108; 109; 110; 111], as well as the detection and characterization of photosynthetic biofilms [49; 58; 112]. All of them are predominantly connected with the particular fluorescence signature of in vivo chlorophyll obtained for a given laser wavelength. In most cases, the LIF setup is built around a cheap and robust frequency-doubled Q-switched Nd:YAG laser, generating at the 532 nm wavelength. As in the previous cases, a significant part of the advances observed is related to modern numerical signal processing methods, including the AI tools for extracting features that are more complex than just the positions and amplitudes of characteristic fluorescence peaks. This AI approach also provides the opportunity to fuse heterogeneous data, that is, enhance the measurement, diagnosis, and classification techniques by applying several of them to the same sample and then combining sensory data such that the resulting information is more precise or reliable than would be possible if these techniques were used individually. A very illustrative and promising example of such an approach is using LIF as one of the data sources in high-throughput plant phenotyping [113; 114]. In this application, several spectroscopic, imaging, and other methods can be successfully combined to monitor the performance of improved genotypes to increase the sustainability of agricultural production and to provide a more comprehensive understanding of the regional agricultural environments [114]. A significant breakthrough in this area, providing more reliable equipment and information-rich data, is the development of a fluorescence induction system in which the laser is replaced by a broad-spectrum lamp. The resulting sensor uses two monochromators and a wide-range PMT detector.