Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Global plant genetics research efforts have focused on developing high yielding, stress tolerant, and disease resistant row and field crop varieties that are more efficient in their use of agronomic inputs (water, nutrients, pesticides, etc.). Until recently, a key bottleneck in such research was the lack of high-throughput sensing technologies for effective and rapid evaluation of expressed phenotypes under field conditions for holistic data-driven decision making and variety selection. This review focuses on technological aspects of integrating unmanned aerial vehicles with imaging systems to enhance field phenotyping capabilities. The state-of-the-art of unmanned aerial vehicle technology for various applications including crop emergence, vigor, and characterization of yield potential of row and field crops has been reviewed. The potential of using aerial imaging to evaluate resistance/susceptibility to biotic and abiotic stress for crop breeding and precision production management has been discussed along with future perspectives and developments.Keywords: High-throughput field phenomics, Aerial imaging, Crop breeding, Data minin

Development and characterization of a compensating wheat-Thinopyrum intermedium Robertsonian translocation with Sr44 resistance to stem rust (Ug99)

Citation: Liu, W., Danilova, T. V., Rouse, M. N., Bowden, R. L., Friebe, B., Gill, B. S., & Pumphrey, M. O. (2013). Development and characterization of a compensating wheatThinopyrum intermedium Robertsonian translocation with Sr44 resistance to stem rust (Ug99). Retrieved from http://krex.ksu.eduThe emergence of the highly virulent Ug99 race complex of the stem rust fungus (Puccinia graminis Pers. f. sp. tritici Eriks. & Henn.) threatens wheat (Triticum aestivum L.) production worldwide. One of the effective genes against the Ug99 race complex is Sr44, which was derived from Thinopyrum intermedium (Host) Barkworth & D.R. Dewey and mapped to the short arm of 7J (designated 7J#1S) present in the noncompensating T7DS-7J#1L∙7J#1S translocation. Noncompensating wheat-alien translocations are known to cause genomic duplications and deficiencies leading to poor agronomic performance, precluding their direct use in wheat improvement. The present study was initiated to produce compensating wheat-Th. intermedium Robertsonian translocations (RobTs) with Sr44 resistance. One compensating RobT was identified consisting of the wheat 7DL arm translocated to the Th. intermedium 7J#1S arm resulting in T7DL∙7J#1S. The T7DL∙7J#1S stock was designated as TA5657. The 7DL∙7J#1S stock carries Sr44 and has resistance to the Ug99 race complex. This compensating RobT with Sr44 resistance may be useful in wheat improvement. In addition, we identified an unnamed stem rust resistance gene located on the 7J#1L arm that confers resistance not only to Ug99, but also to race TRTTF, which is virulent to Sr44. However, the action of the second gene can be modified by the presence of suppressors in the recipient wheat cultivars

Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making

Stem rust resistance in Aegilops tauschii germplasm

Citation: Rouse, M.N., Olson, E.L., Gill, B.S., Pumphrey, M.O., & Jin, Y. (2011). Stem rust resistance in Aegilops tauschii germplasm. Retrieved from http://krex.ksu.eduAegilops tauschii Coss., the D genome donor of hexaploid wheat, Triticum aestivum L., has been used extensively for the transfer of agronomically important traits to wheat, including stem rust resistance genes Sr33, Sr45, and Sr46. To identify potentially new stem rust resistance genes in A. tauschii germplasm, we evaluated 456 nonduplicated accessions deposited in the USDA National Small Grains Collection (Aberdeen, ID) and the Wheat Genetic and Genomic Resources Center collection (Kansas State University, Manhattan, KS), with races TTKSK (Ug99), TRTTF, TTTTF, TPMKC, RKQQC, and QTHJC of Puccinia graminis Pers.:Pers. f. sp. tritici Eriks. & E. Henn. Ninety-eight accessions (22%) were identified as resistant to race TTKSK. A broad range of resistant infection types (; to 2+) were found in reaction to race TTKSK. Resistance was significantly associated among most of the races in pairwise comparisons. However, resistance was largely race specific. Only 12 of the accessions resistant to race TTKSK were also resistant to the other five races. Results from this germplasm screening will facilitate further studies on the genetic characterization of accessions with potentially novel sources of resistance to race TTKSK

Virulence Characterization of Wheat Stripe Rust Fungus Puccinia striiformis f. sp. tritici in Ethiopia and Evaluation of Ethiopian Wheat Germplasm for Resistance to Races of the Pathogen from Ethiopia and the United States

Stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most important diseases of wheat in Ethiopia. In total, 97 isolates were recovered from stripe rust samples collected in Ethiopia in 2013 and 2014. These isolates were tested on a set of 18 Yr single-gene differentials for characterization of races and 7 supplementary differentials for additional information of virulence. Of 18 P. striiformis f. sp. tritici races identified, the 5 most predominant races were PSTv-105 (21.7%), PSTv-106 (17.5%), PSTv-107 (11.3%), PSTv-76 (10.3%), and PSTv-41 (6.2%). High frequencies (>40%) were detected for virulence to resistance genes Yr1, Yr2, Yr6, Yr7, Yr8, Yr9, Yr17, Yr25, Yr27, Yr28, Yr31, Yr43, Yr44, YrExp2, and YrA. Low frequencies (<40%) were detected for virulence to Yr10, Yr24, Yr32, YrTr1, Hybrid 46, and Vilmorin 23. None of the isolates were virulent to Yr5, Yr15, YrSP, and YrTye. Among the six collection regions, Arsi Robe and Tiyo had the highest virulence diversities, followed by Bekoji, while Bale and Holeta had the lowest. Evaluation of 178 Ethiopian wheat cultivars and landraces with two of the Ethiopian races and three races from the United States indicated that the Ethiopian races were more virulent on the germplasm than the predominant races of the United States. Thirteen wheat cultivars or landraces that were resistant or moderately resistant to all five tested races should be useful for breeding wheat cultivars with resistance to stripe rust in both countries

Multi-Locus Mixed Model Analysis Of Stem Rust Resistance In Winter Wheat

Genome-wide association mapping is a powerful tool for dissecting the relationship between phenotypes and genetic variants in diverse populations. With the improved cost efficiency of high-throughput genotyping platforms, association mapping is a desirable method of mining populations for favorable alleles that hold value for crop improvement. Stem rust, caused by the fungus f. sp. is a devastating disease that threatens wheat ( L.) production worldwide. Here, we explored the genetic basis of stem rust resistance in a global collection of 1411 hexaploid winter wheat accessions genotyped with 5390 single nucleotide polymorphism markers. To facilitate the development of resistant varieties, we characterized marker–trait associations underlying field resistance to North American races and seedling resistance to the races TTKSK (Ug99), TRTTF, TTTTF, and BCCBC. After evaluating several commonly used linear models, a multi-locus mixed model provided the maximum statistical power and improved the identification of loci with direct breeding application. Ten high-confidence resistance loci were identified, including SNP markers linked to and and at least three newly discovered resistance loci that are strong candidates for introgression into modern cultivars. In the present study, we assessed the power of multi-locus association mapping while providing an in-depth analysis for its practical ability to assist breeders with the introgression of rare alleles into elite varieties

Effect of high-resolution satellite and UAV imagery plot pixel resolution in wheat crop yield prediction

Accurate crop performance assessment and yield prediction in plant breeding programmes can aid decision-making to improve productivity and product quality during crop selection and management. Grain yield is a complex trait, which is a function of the genotype-environment interaction. While using digital remote sensing traits to assess crop performance and predict yield, the characteristics of the sensing tools and approaches can influence prediction performance. In this study, two sensing scales, an unmanned aerial vehicle (UAV) equipped with a ten-band multispectral camera and high-resolution (~0.31 m) WorldView-3 satellite imagery, were used to monitor spring and winter wheat breeding trails in two growing seasons (2020 and 2021). The breeding plots were planted in three different plot sizes (about 1.5 × 5.0 m, 3.0 × 11.0 m, and 4.5 × 11.0 m in spring wheat, and about 1.5 × 3.0 m, 3.0 × 7.3 m, and 4.5 × 7.3 m in winter wheat), with each having 12 varieties and three replications per variety. The spectral and vegetation indices (VI) were extracted from the datasets, and machine learning models for yield prediction (partial least squares regression, least absolute shrinkage selector operator regression, and random forest regression) were evaluated. With multiscale approaches, a moderate to strong correlation of VI data between high-resolution satellite and UAV data (0.42 ≤ r ≤ 0.99, p r ≤ 0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) were also comparable. These findings inform the applications of high-resolution satellite imagery in breeding programmes, considering that the plot size would influence yield prediction accuracies.</p

Biogenic VOCs Emission Profiles Associated with Plant-Pest Interaction for Phenotyping Applications

Pest attacks on plants can substantially change plants’ volatile organic compounds (VOCs) emission profiles. Comparison of VOC emission profiles between non-infected/non-infested and infected/infested plants, as well as resistant and susceptible plant cultivars, may provide cues for a deeper understanding of plant-pest interactions and associated resistance. Furthermore, the identification of biomarkers—specific biogenic VOCs—associated with the resistance can serve as a non-destructive and rapid tool for phenotyping applications. This research aims to compare the VOCs emission profiles under diverse conditions to identify constitutive (also referred to as green VOCs) and induced (resulting from biotic/abiotic stress) VOCs released in potatoes and wheat. In the first study, wild potato Solanum bulbocastanum (accession# 22; SB22) was inoculated with Meloidogyne chitwoodi race 1 (Mc1), and Mc1 pathotype Roza (SB22 is resistant to Mc1 and susceptible to pathotype Roza), and VOCs emission profiles were collected using gas chromatography-flame ionization detection (GC-FID) at different time points. Similarly, in the second study, the VOCs emission profiles of resistant (‘Hollis’) and susceptible (‘Alturas’) wheat cultivars infested with Hessian fly insects were evaluated using the GC-FID system. In both studies, in addition to variable plant responses (susceptibility to pests), control treatments (non-inoculated or non-infested) were used to compare the VOCs emission profiles resulting from differences in stress conditions. The common VOC peaks (constitutive VOCs) between control and infected/infested samples, and unique VOC peaks (induced VOCs) presented only in infected/infested samples were analyzed. In the potato-nematode study, the highest unique peak was found two days after inoculation (DAI) for SB22 inoculated with Mc1 (resistance response). The most common VOC peaks in SB22 inoculated with both Mc1 and Roza were found at 5 and 10 DAI. In the wheat-insect study, only the Hollis showed unique VOC peaks. Interestingly, both cultivars released the same common VOCs between control and infected samples, with only a difference in VOC average peak intensity at 22.4 min retention time where the average intensity was 4.3 times higher in the infested samples of Hollis than infested samples of Alturas. These studies demonstrate the potential of plant VOCs to serve as a rapid phenotyping tool to assess resistance levels in different crops