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...

Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)‐based imaging and satellite‐based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman‐based diagnostics of water deficit and salinity stresses on two greenhouse‐grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240‐mM salt water per day for the duration of 5‐day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand‐held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman‐based surveillance in commercial‐scale agriculture and digital farming.

Identification of peanut cultivars for distinct phenotypic or genotypic traits whether using visual characterization or laboratory analysis requires substantial expertise, time, and resources. A less subjective and more precise method is needed for identification of peanut germplasm throughout the value chain. In this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored. We show that RS can be used for highly accurate peanut phenotyping via surface scans of peanut leaves and the resulting chemometric analysis: On average 94% accuracy in identification of peanut cultivars and breeding lines was achieved. Our results also suggest that RS can be used for highly accurate determination of nematode resistance and susceptibility of those breeding lines and cultivars. Specifically, nematode-resistant peanut cultivars can be identified with 92% accuracy, whereas susceptible breeding lines were identified with 81% accuracy. Finally, RS revealed substantial differences in biochemical composition between resistant and susceptible peanut cultivars. We found that resistant cultivars exhibit substantially higher carotenoid content compared to the susceptible breeding lines. The results of this study show that RS can be used for quick, accurate, and non-invasive identification of genotype, nematode resistance, and nutrient content. Armed with this knowledge, the peanut industry can utilize Raman spectroscopy for expedited breeding to increase yields, nutrition, and maintaining purity levels of cultivars following release.

Synopsis Germination rates usually increased during the first few months but dropped to pre‐storage levels by the end of 5 months. Deteriorated seeds declined in speed of germination at below‐optimum temperature and the decline began sooner in the more severely deteriorated seeds. Speed of germination was reduced in “ginrun” Rex seeds, but not in unweathered M8948 seeds. Moisture content of deteriorated seeds dropped to the pre‐deterioration level by the end of the first month and varied only slightly during the remaining 4 months.