# Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture
## 4 Current Applications and Future Perspectives
### Elastic Spectroscopy
As an alternative, MHS imaging has been used in robotic harvesting, as successfully demonstrated for a non-destructive fruit quality monitoring prototype system, consisting of a light detection and ranging (LiDAR) and Vis-NIRS sensors installed on an inclined conveyor for mimicking real-time citrus fruit size and SSC measurement, respectively, during harvest [67]. Another remote-controlled field robot, RobHortic, equipped with color, multispectral, and hyperspectral (400-1000 nm) cameras and specific software, has been developed and tested under field conditions for the detection of infected plants (symptomatic and asymptomatic) with \"_Candidatus Liberbacter solanacearum_\", across carrot fields, with an accuracy of about 60 %, based on several machine learning-based models [68]. Other common applications of Vis-NIRS MHS imaging comprise the sorting of horticultural products by assessing their external (EQA) and internal quality attributes (IQA) in the postharvest, with portable, benchtop devices and in automated grading lines [1; 31; 35; 36; 66; 69; 70; 71; 72; 73], commercialized by several vendors (e.g., Aweeta, Maffoda, Greefa, Compaq, Unitec). The majority of these commercial devices, particularly those installed on inline grading systems, seldom provide their actual proven assessment capacity due to industrial secrecy. However, there are numerous examples of MHS imagingsystems used under laboratory conditions that have demonstrated their high potential to convey valuable information regarding, for instance, fruit quality. It is worth mentioning the development of a hyperspectral insect damage detection algorithm in coffee, which can automatically detect insect-damaged beans using only a few bands and one spectral signature [70]. The demonstration of the feasibility of using these imaging techniques to rapidly measure pectin polysaccharides in intact mulberry fruits is also of significance [72]. Furthermore, their utility for high-throughput phenotyping of plant chemical traits was also established for maize and soybean plants in a greenhouse exposed to varying levels of either water deficit and nutritional stress [7]. Regardless of all the apparent advantages and the numerous reports on their application, there are a few drawbacks that might limit the future extensive use of MHS imaging systems [12; 33]. These include: (i) their high cost, this being particularly critical in automated grading machines that usually include various sorter lines, each of them must include a separate sensor system; (ii) the slower MHS image acquisition in comparison to other cameras; (iii) the warranted extensive investigation on the acquisition and image processing software; and (iv) the necessary step towards the development of more user-friendly spectral acquisition and results in access by the non-expert in the various fields of plant sciences, particularly those related to agriculture. Still, based on the last decade's trend, vendors seem consensual regarding the continuous cost drop in this technology despite their expected upgrade [33]. This outcome is based on the development of more powerful and comprehensive machine learning techniques dealing with big data, such as deep learning, as well as to their storage and management using the state-of-the-art Blockchain approach [74]. Overall, both vendors and researchers firmly believe that the MHS systems will be able to perform the effective sorting of various grades of horticultural produce, as well as to contribute to their traceability and safety across the supply chain, a pressing issue in the agro-food sector [75; 76; 77]. Utterly, this would lead to a reduced cost/benefit ratio for both producers and packinghouses, relying on a limited number of employees for quality inspection, and guaranteeing the best quality and the highest market prices for their commodities, while reducing food loss [37].