Identification of specific genotypes can be accomplished by visual recognition of their distinct phenotypical appearance, as well as DNA analysis. Visual identification (ID) of species is subjective and usually requires substantial taxonomic expertise. Genotyping and sequencing are destructive, timeand labor-consuming. In this study, we investigate the potential use of Raman spectroscopy (RS) as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of peanut genotypes. We show that chemometric analysis of peanut leaflet spectra provides accurate identification of different varieties. This same analysis can be used for prediction of nematode resistance and oleic-linoleic oil (O/L) ratio. Raman-based analysis of seeds provides accurate genotype identification in 95% of samples. Additionally, we present data on the identification of carbohydrates, proteins, fiber and other nutrients obtained from spectroscopic signatures of peanut seeds. These results demonstrate that RS allows for fast, accurate and non-invasive screening and selection of plants which can be used for precision breeding. Continuous growth of the global population requires perpetual increase in the production of food. It is expected that by 2050 we will need to produce 70% more food 1. Such expectations can be met only though major transformations in currently used agricultural approaches. For instance, utilization of sensor-based field irrigation in 50% of ornamental operations can save up to 223 billion liters of water per year in the U.S. alone, or the water use of approximately 400,000 U.S. households 2. Satellite or unmanned aerial vehicle (UAV) guided imaging can be used to monitor agricultural crops to minimize harvest losses. This new agricultural paradigm, also known as digital farming, aims to automate agricultural processes via application of precision location (GPS) methods and artificial intelligence. Although many processes in modern agriculture have reached a substantial degree of automation, plant breeding and taxonomic identification are far from that point. Currently, to positively identify a plant, visual inspection or genotyping are the only options. The first approach is subjective and typically requires substantial knowledge and practical experience. Years of training may be required to train a plant breeder or a botanist to be the expert in one area of the plant kingdom. To exclude the human factor in plant identification, Baena et al. used RGB imaging from UAVs to identify plants 3. However, the reported results demonstrated that such approach may work only for large plants with major morphological differences. Genotyping, whether by sequencing or other methods, is broadly employed by scientists and breeders to identify the genetic material associated with traits of interest. However, these methods are destructive, time-consuming and labor-intensive. Raman spectroscopy (RS) is a label-free, non-invasive and non-destructive analytical technique that can be used to probe che...

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Proper management of nutrients in agricultural systems is critically important for maximizing crop yields while simultaneously minimizing the health and environmental impacts of pollution from fertilizers. These goals can be achieved by timely confirmatory diagnostics of nutrient deficiencies in plants, which enable precise administration of fertilizers and other supplementation in fields. Traditionally, nutrient diagnostics are performed by wet-laboratory analyses, which are both time-and labor-consuming. Unmanned aerial vehicle (UAV) and satellite imaging have offered a non-invasive alternative. However, these imaging approaches do not have sufficient specificity, and they are only capable of detecting symptomatic stages of nutrient deficiencies. Raman spectroscopy (RS) is a non-invasive and non-destructive technique that can be used for confirmatory detection and identification of both biotic and abiotic stresses on plants. Herein, we show the use of a hand-held Raman spectrometer for highly accurate presymptomatic diagnostics of nitrogen, phosphorus, and potassium deficiencies in rice (Oryza sativa). Moreover, we demonstrate that RS can also be used for pre symptomatic diagnostics of medium and high salinity stresses. A Raman-based analysis is fast (1 s required for spectral acquisition), portable (measurements can be taken directly in the field), and label-free (no chemicals are needed). These advantages will allow RS to transform agricultural practices, enabling precision agriculture in the near future.

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Forensic examination of hair is commonly performed to trace its origin and make a connection between a suspect and a crime scene.

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