Abstract
Bioethanol production obtained from cereal straw has aroused great interest in recent years, which has led to the development of breeding programs to improve the quality of lignocellulosic material in terms of the biomass and sugar content. This process requires the analysis of genotype–phenotype relationships, and although genotyping tools are very advanced, phenotypic tools are not usually capable of satisfying the massive evaluation that is required to identify potential characters for bioethanol production in field trials. However, unmanned aerial vehicle (UAV) platforms have demonstrated their capacity for efficient and non-destructive acquisition of crop data with an application in high-throughput phenotyping. This work shows the first evaluation of UAV-based multi-spectral images for estimating bioethanol-related variables (total biomass dry weight, sugar release, and theoretical ethanol yield) of several accessions of wheat, barley, and triticale (234 cereal plots). The full procedure involved several stages: (1) the acquisition of multi-temporal UAV images by a six-band camera along different crop phenology stages (94, 104, 119, 130, 143, 161, and 175 days after sowing), (2) the generation of ortho-mosaicked images of the full field experiment, (3) the image analysis with an object-based (OBIA) algorithm and the calculation of vegetation indices (VIs), (4) the statistical analysis of spectral data and bioethanol-related variables to predict a UAV-based ranking of cereal accessions in terms of theoretical ethanol yield. The UAV-based system captured the high variability observed in the field trials over time. Three VIs created with visible wavebands and four VIs that incorporated the near-infrared (NIR) waveband were studied, obtaining that the NIR-based VIs were the best at estimating the crop biomass, while the visible-based VIs were suitable for estimating crop sugar release. The temporal factor was very helpful in achieving better estimations. The results that were obtained from single dates [i.e., temporal scenario 1 (TS-1)] were always less accurate for estimating the sugar release than those obtained in TS-2 (i.e., averaging the values of each VI obtained during plant anthesis) and less accurate for estimating the crop biomass and theoretical ethanol yield than those obtained in TS-3 (i.e., averaging the values of each VI obtained during full crop development). The highest correlation to theoretical ethanol yield was obtained with the normalized difference vegetation index (R2 = 0.66), which allowed to rank the cereal accessions in terms of potential for bioethanol production.
Highlights
There is a renewed interest in biomass recovery for energy consumption because biomass is a renewable and carbon neutral source of energy (Perea-Moreno et al, 2019)
The early anthesis date of some accessions could be due to a short duration of their vegetative stage (Jamieson et al, 1998), which might have produced a smaller number of leaf primordia and resulted in a lower sink capacity and a decrease in biomass accumulation during the pre-anthesis period (Giunta et al, 1999)
This was partially observed at the level of cereal species, in which the average dates of anthesis and the total biomass for x Triticosecale were significantly lower in comparison to the average values for other three species that were studied
Summary
There is a renewed interest in biomass recovery for energy consumption because biomass is a renewable and carbon neutral source of energy (Perea-Moreno et al, 2019). Two types of biofuels can be distinguished according to the different feedstock types. The first generation liquid biofuel is produced from cereals, sugar crops, and oilseeds, and the second-generation liquid biofuel is produced from lignocellulosic feedstock (Mittal and Decker, 2013; Kang et al, 2014). Secondgeneration biofuel is a more sustainable option because it is not in direct competition with the food supply and, it does not increase food prices. It produces lower greenhouse gas emissions and better water and land uses (Sims et al, 2010). The process of biofuel production could be improved in terms of productivity, efficiency and cost reduction by using two powerful tools, classical breeding and biotechnology, and by analyzing the genotype–phenotype relationships in both cases
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