Abstract
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.
Highlights
The population of the world is increasing at an alarming rate
We previously demonstrated that Raman spectroscopy (RS) is highly sensitive to plant biochemistry that is drastically different in different peanut varieties (Farber et al, 2020c)
Our results demonstrate that RS can be used for highly accurate identification of peanut genotypes
Summary
The population of the world is increasing at an alarming rate. The production of food must increase in order to match demand. It is predicted that we will need to produce 70% more food by 2050 to sustain our population (Food and Agriculture Organization of the United Nations, 2009). The expansion of agricultural territory is limited due to urbanization, cost of production, and other contributing factors. The continued loss of agricultural lands has led agricultural leaders to focus on increasing yields on existing croplands through the innovation of digital farming. Referred to as precision agriculture, seeks to maximize crop yield by maximizing plant production and minimizing the environmental impact with the use of technologies such as Raman spectroscopy (Farber et al, 2019a, 2020b; Sanchez et al, 2019a,b, 2020b)
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