# Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture
## 4 Current Applications and Future Perspectives  
### Elastic Spectroscopy  
One of the most important and illustrative applications of elastic spectroscopy comprises the widespread use of MHS imaging sensors and devices in precision agriculture. The rapid development of computer and information technologies has led to a dramatic reduction in the time of complex computations. It enabled the practitioners to process previously unthinkable amounts of spectral data obtained by these sensors, used in many different applications. This is made possible not only to significantly improve the methods of vegetation detection [46] but also to develop models capable of predicting, with various degrees of accuracy, a wide range of parameters describing plants, their physiology, and interaction with the environment. Published research on this subject includes the assessment of the plant biomass, chlorophyll, nitrogen and lignin-cellulose content [8; 47; 48; 49]; vegetation classification [50; 51] and phenotyping [52]; and detection and mapping of pest (invasive species, diseases, etc.) [2; 8; 53; 54] among major crops supply chains. The most prospective approach in providing the largest and most extensive information on many crops, forests and ecosystems, is the remote light-source-free spectral imagery sensing based on sunlight reflection. Its widespread and rapid development relies on several factors, in addition to the enhanced computer and information technologies capacity. These factors comprise: (i) advances in materials engineering that have led to the creation of compact, lightweight, reliable and low-energy-consumption instrumentation for MHS imaging; (ii) with the advent of geographical information systems (GIS) and Geostatistics, the recorded images have been linked, easily and precisely, with their location on the map (georeferenced); (iii) this, in its turn, made it possible to evaluate large areas using not only inevitably limited terrestrial observations but airborne and spaceborne remote sensing as well [55; 56; 12; 50; 57]. A major example of the latter is the study by [PERSON] et al. [58], which has explored and shown the potential of the Sentinel-2 Multispectral Instrument (MSI), a launched satellite with refined spatial resolution and three red-edge bands for discriminating between yellow rust infection severity in winter wheat over large areas up to a regional scale. A wide set of hyperspectral imaging applications, both in situ and remotely, have been used in agriculture. Rice cultivation is among the major examples that has made extensive use of those applications to assess growth, stress conditions, pests, yield, harvest, as well as for the determination of grain quality, viability and geographical origin [12]. These applications have been extended to other species and crops, namely, to discriminate between multiple cultivars of a crop species [59], to predict critical crops yield, and in the early detection of potentially dangerous diseases. These include the laurel wil disease in avocado [60]; fire blight in pear trees [61; 62]; canker, black spot, decay, and Huanglongbing (HLB) in citrus [1; 10; 11; 32; 63]; Yellow Rust in wheat [64]; Southern Corn Rust (SCR) [65]; the Potato Virus Y (PVY) disease in visually asymptomatic infected potato plants [59]; or to identify and classify grapevines inoculated with the Grapevine Vein-Clearing Virus (GVCV) at the early asymptomatic stages, under field conditions [57]. However, for most of these crops, including rice [1; 26], the assessment through manual in situ measurements is still the predominant procedure [12]. For instance, when evaluating the fruit quality and ripening on-tree, the canopy foliage is still a major impediment to the specific and accurate evaluation through airborne or spaceborne imaging sensing, namely those based on Vis-NIRS [66].