Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method.

To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position. Since the orthomosaic images are stitched from a large number of smaller images, there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot. To overcome this, we have developed a three-level hierarchical optimization method. First, the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area. Then, columns of microplots are repositioned, constrained by their expected spacing. Finally, the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation, minimizes spacing variance between microplots, and maximizes each microplot's alignment relative to other microplots in the same row and column. The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials. The algorithm was able to detect 99.7% of microplots for canola and 99% for wheat. The automatically segmented microplots were compared to ground truth segmentations, resulting in an average DSC of 91.2% and 89.6% across all microplots and orthomosaics in the canola and wheat datasets.

Nitrogen Use Efficiency in Parent vs. Hybrid Canola under Varying Nitrogen Availabilities.

Improving nitrogen use efficiency (NUE) is essential for sustainable agriculture, especially in high-N-demanding crops such as canola (Brassica napus). While advancements in above-ground agronomic practices have improved NUE, research on soil and below-ground processes are limited. Plant NUE-and its components, N uptake efficiency (NUpE), and N utilization efficiency (NUtE)-can be further improved by exploring crop variety and soil N cycling. Canola parental genotypes (NAM-0 and NAM-17) and hybrids (H151857 and H151816) were grown on a dark brown chernozem in Saskatchewan, Canada. Soil and plant samples were collected at the 5-6 leaf stage and flowering, and seeds were collected at harvest maturity. Soil N cycling varied with phenotypic stage, with higher potential ammonium oxidation rates at the 5-6 leaf stage and higher urease activity at flowering. Seed N uptake was higher under higher urea-N rates, while the converse was true for NUE metrics. Hybrids had higher yield, seed N uptake, NUtE, and NUE, with higher NUE potentially owing to higher NUtE at flowering, which led to higher yield and seed N allocation. Soil N cycling and soil N concentrations correlated for improved canola NUE, revealing below-ground breeding targets. Future studies should consider multiple root characteristics, including rhizosphere microbial N cycling, root exudates, and root system architecture, to determine the below-ground dynamics of plant NUE.

Phenotyping Flowering in Canola (Brassica napus L.) and Estimating Seed Yield Using an Unmanned Aerial Vehicle-Based Imagery.

Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola (Brassica napus L.) flowers, the bright yellow flowers, indeterminately increase over a protracted period. Flower production of canola plays an important role in yield determination. Yellowness of canola petals may be a critical reflectance signal and a good predictor of pod number and, therefore, seed yield. However, quantifying flowering based on traditional visual scales is subjective, time-consuming, and labor-consuming. Recent developments in phenotyping technologies using Unmanned Aerial Vehicles (UAVs) make it possible to effectively capture crop information and to predict crop yield via imagery. Our objectives were to investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes, including 53 B. napus lines, two Brassica carinata lines, and a Brassica juncea variety, were grown near Saskatoon, SK, Canada from 2016 to 2018 and near Melfort and Scott, SK, Canada in 2017. Aerial imagery with geometric and radiometric corrections was collected through the flowering stage using a UAV mounted with a multispectral camera. We found that the normalized difference yellowness index (NDYI) was a useful vegetation index for representing canola yellowness, which is related to canola flowering intensity during the full flowering stage. However, the flowering pixel number estimated by the thresholding method improved the ability of NDYI to detect yellow flowers with coefficient of determination (R 2) ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel numbers integrated over time covers more growth information and can be a good predictor of pod number and thus, canola yield with R 2 up to 0.42. These results indicate that NDYI-based flowering pixel numbers can perform well in estimating flowering intensity. Integrated flowering intensity extracted from imagery over time can be a potential phenotype associated with canola seed yield.

Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population.

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

An intensive multilocation temporal dataset of fungal and bacterial communities in the root and rhizosphere of Brassica napus.

The plant microbiome has been recently recognized as a plant phenotype to help in the food security of the future population. However, global plant microbiome datasets are insufficient to be used effectively for breeding this new generation of crop plants. We surveyed the diversity and temporal composition of bacterial and fungal communities in the root and rhizosphere of Brassica napus, the world's second largest oilseed crop, weekly in eight diverse lines at one site and every three weeks in sixteen lines, at three sites in 2016 and 2017 in the Canadian Prairies. We sequenced the bacterial 16S ribosomal RNA gene generating a total of 127.7 million reads and the fungal internal transcribed spacer (ITS) region generating 113.4 million reads. 14,944 unique fungal amplicon sequence variants (ASV) were detected, with an average of 43 ASVs per root and 105 ASVs per rhizosphere sample. We detected 10,882 unique bacterial ASVs with an average of 249 ASVs per sample. Temporal, site-to-site, and line-driven variability were key determinants of microbial community structure. This dataset is a valuable resource to systematically extract information on the belowground microbiome of diverse B. napus lines in different environments, at different times in the growing season, in order to adapt effective varieties for sustainable crop production systems.

An intensive multilocation temporal dataset of fungal communities in the root and rhizosphere of Brassica napus.

The plant microbiome has been recently recognized as a plant phenotype to help in the food security of the future population. However, global plant microbiome datasets are insufficient to be used effectively for breeding this new generation of crop plants. We surveyed the diversity and temporal composition of fungal communities in the root and rhizosphere of Brassica napus, the world's second largest oilseed crop, weekly in eight diverse lines at one site and every three weeks in sixteen lines, at three sites in 2016 and 2017 in the Canadian Prairies. 14,944 unique amplicon sequence variants (ASV) were detected based on the internal transcribed spacer region, with an average of 43 ASVs per root and 105 ASVs per rhizosphere sample. Temporal, site-to-site, and line-driven variability were key determinants of fungal community structure. This dataset is a valuable resource to systematically extract information on the belowground microbiome of diverse B. napus lines in different environments, at different times in the growing season, in order to adapt effective varieties for sustainable crop production systems.

SoilCam: A Fully Automated Minirhizotron using Multispectral Imaging for Root Activity Monitoring.

A minirhizotron is an in situ root imaging system that captures components of root system architecture dynamics over time. Commercial minirhizotrons are expensive, limited to white-light imaging, and often need human intervention. The implementation of a minirhizotron needs to be low cost, automated, and customizable to be effective and widely adopted. We present a newly designed root imaging system called SoilCam that addresses the above mentioned limitations. The imaging system is multi-modal, i.e., it supports both conventional white-light and multispectral imaging, with fully automated operations for long-term in-situ monitoring using wireless control and access. The system is capable of taking 360° images covering the entire area surrounding the tube. The image sensor can be customized depending on the spectral imaging requirements. The maximum achievable image quality of the system is 8 MP (Mega Pixel)/picture, which is equivalent to a 2500 DPI (dots per inch) image resolution. The length of time in the field can be extended with a rechargeable battery and solar panel connectivity. Offline image-processing software, with several image enhancement algorithms to eliminate motion blur and geometric distortion and to reconstruct the 360° panoramic view, is also presented. The system is tested in the field by imaging canola roots to show the performance advantages over commercial systems.

Core and Differentially Abundant Bacterial Taxa in the Rhizosphere of Field Grown Brassica napus Genotypes: Implications for Canola Breeding.

Modifying the rhizosphere microbiome through targeted plant breeding is key to harnessing positive plant-microbial interrelationships in cropping agroecosystems. Here, we examine the composition of rhizosphere bacterial communities of diverse Brassica napus genotypes to identify: (1) taxa that preferentially associate with genotypes, (2) core bacterial microbiota associated with B. napus, (3) heritable alpha diversity measures at flowering and whole growing season, and (4) correlation between microbial and plant genetic distance among canola genotypes at different growth stages. Our aim is to identify and describe signature microbiota with potential positive benefits that could be integrated in B. napus breeding and management strategies. Rhizosphere soils of 16 diverse genotypes sampled weekly over a 10-week period at single location as well as at three time points at two additional locations were analyzed using 16S rRNA gene amplicon sequencing. The B. napus rhizosphere microbiome was characterized by diverse bacterial communities with 32 named bacterial phyla. The most abundant phyla were Proteobacteria, Actinobacteria, and Acidobacteria. Overall microbial and plant genetic distances were highly correlated (R = 0.65). Alpha diversity heritability estimates were between 0.16 and 0.41 when evaluated across growth stage and between 0.24 and 0.59 at flowering. Compared with a reference B. napus genotype, a total of 81 genera were significantly more abundant and 71 were significantly less abundant in at least one B. napus genotype out of the total 558 bacterial genera. Most differentially abundant genera were Proteobacteria and Actinobacteria followed by Bacteroidetes and Firmicutes. Here, we also show that B. napus genotypes select an overall core bacterial microbiome with growth-stage-related patterns as to how taxa joined the core membership. In addition, we report that sets of B. napus core taxa were consistent across our three sites and 2 years. Both differential abundance and core analysis implicate numerous bacteria that have been reported to have beneficial effects on plant growth including disease suppression, antifungal properties, and plant growth promotion. Using a multi-site year, temporally intensive field sampling approach, we showed that small plant genetic differences cause predictable changes in canola microbiome and are potential target for direct and indirect selection within breeding programs.

Multi-Focus Fusion Technique on Low-Cost Camera Images for Canola Phenotyping.

To meet the high demand for supporting and accelerating progress in the breeding of novel traits, plant scientists and breeders have to measure a large number of plants and their characteristics accurately. Imaging methodologies are being deployed to acquire data for quantitative studies of complex traits. Images are not always good quality, in particular, they are obtained from the field. Image fusion techniques can be helpful for plant breeders with more comfortable access plant characteristics by improving the definition and resolution of color images. In this work, the multi-focus images were loaded and then the similarity of visual saliency, gradient, and color distortion were measured to obtain weight maps. The maps were refined by a modified guided filter before the images were reconstructed. Canola images were obtained by a custom built mobile platform for field phenotyping and were used for testing in public databases. The proposed method was also tested against the five common image fusion methods in terms of quality and speed. Experimental results show good re-constructed images subjectively and objectively performed by the proposed technique. The findings contribute to a new multi-focus image fusion that exhibits a competitive performance and outperforms some other state-of-the-art methods based on the visual saliency maps and gradient domain fast guided filter. The proposed fusing technique can be extended to other fields, such as remote sensing and medical image fusion applications.

Magnitude Differences in Bioactive Compounds, Chemical Functional Groups, Fatty Acid Profiles, Nutrient Degradation and Digestion, Molecular Structure, and Metabolic Characteristics of Protein in Newly Developed Yellow-Seeded and Black-Seeded Canola Lines.

Recently, new lines of yellow-seeded (CS-Y) and black-seeded canola (CS-B) have been developed with chemical and structural alteration through modern breeding technology. However, no systematic study was found on the bioactive compounds, chemical functional groups, fatty acid profiles, inherent structure, nutrient degradation and absorption, or metabolic characteristics between the newly developed yellow- and black-seeded canola lines. This study aimed to systematically characterize chemical, structural, and nutritional features in these canola lines. The parameters accessed include bioactive compounds and antinutrition factors, chemical functional groups, detailed chemical and nutrient profiles, energy value, nutrient fractions, protein structure, degradation kinetics, intestinal digestion, true intestinal protein supply, and feed milk value. The results showed that the CS-Y line was lower (P ≤ 0.05) in neutral detergent fiber (122 vs 154 g/kg DM), acid detergent fiber (61 vs 99 g/kg DM), lignin (58 vs 77 g/kg DM), nonprotein nitrogen (56 vs 68 g/kg DM), and acid detergent insoluble protein (11 vs 35 g/kg DM) than the CS-B line. There was no difference in fatty acid profiles except C20:1 eicosenoic acid content (omega-9) which was in lower in the CS-Y line (P < 0.05) compared to the CS-B line. The glucosinolate compounds differed (P < 0.05) in terms of 4-pentenyl, phenylethyl, 3-CH3-indolyl, and 3-butenyl glucosinolates (2.9 vs 1.0 µmol/g) between the CS-Y and CS-B lines. For bioactive compounds, total polyphenols tended to be different (6.3 vs 7.2 g/kg DM), but there were no differences in erucic acid and condensed tannins with averages of 0.3 and 3.1 g/kg DM, respectively. When protein was portioned into five subfractions, significant differences were found in PA, PB1 (65 vs 79 g/kg CP), PB2, and PC fractions (10 vs 33 g/kg CP), indicating protein degradation and supply to small intestine differed between two new lines. In terms of protein structure spectral profile, there were no significant differences in functional groups of amides I and II, α helix, and ß-sheet structure as well as their ratio between the two new lines, indicating no difference in protein structure makeup and conformation between the two lines. In terms of energy values, there were significant differences in total digestible nutrient (TDN; 149 vs 133 g/kg DM), metabolizable energy (ME; 58 vs 52 MJ/kg DM), and net energy for lactation (NEL; 42 vs 37 MJ/kg DM) between CS-Y and CS-B lines. For in situ rumen degradation kinetics, the two lines differed in soluble fraction (S; 284 vs 341 g/kg CP), potential degradation fraction (D; 672 vs 590 g/kg CP), and effective degraded organic matter (EDOM; 710 vs 684 g/kg OM), but no difference in degradation rate. CS-Y had higher digestibility of rumen bypass protein in the intestine than CS-B (566 vs 446 g/kg of RUP, P < 0.05). Modeling nutrient supply results showed that microbial protein synthesis (MCP; 148 vs 171 g/kg DM) and rumen protein degraded balance (DPB; 108 vs 127 g/kg DM) were lower in the CS-Y line, but there were no differences in total truly digested protein in small intestine (DVE) and feed milk value (FMV) between the two lines. In conclusion, the new yellow line had different nutritional, chemical, and structural features compared to the black line. CS-Y provided better nutrient utilization and availability.

Genetic mapping of legume orthologs reveals high conservation of synteny between lentil species and the sequenced genomes of Medicago and chickpea.

Lentil (Lens culinaris Medik.) is a global food crop with increasing importance for food security in south Asia and other regions. Lens ervoides, a wild relative of cultivated lentil, is an important source of agronomic trait variation. Lens is a member of the galegoid clade of the Papilionoideae family, which includes other important dietary legumes such as chickpea (Cicer arietinum) and pea (Pisum sativum), and the sequenced model legume Medicago truncatula. Understanding the genetic structure of Lens spp. in relation to more fully sequenced legumes would allow leveraging of genomic resources. A set of 1107 TOG-based amplicons were identified in L. ervoides and a subset thereof used to design SNP markers for mapping. A map of L. ervoides consisting of 377 SNP markers spread across seven linkage groups was developed using a GoldenGate genotyping array and single SNP marker assays. Comparison with maps of M. truncatula and L. culinaris documented considerable shared synteny and led to the identification of a few major translocations and a major inversion that distinguish Lens from M. truncatula, as well as a translocation that distinguishes L. culinaris from L. ervoides. The identification of chromosome-level differences among Lens spp. will aid in the understanding of introgression of genes from L. ervoides into cultivated L. culinaris, furthering genetic research and breeding applications in lentil.

Explore protein molecular structure in endosperm tissues in newly developed black and yellow type canola seeds by using synchrotron-based Fourier transform infrared microspectroscopy.

This study was conducted to characterize the protein molecular structure in endosperm tissues in newly developed black and yellow-type canola seeds by using synchrotron-based Fourier transform infrared microspectroscopy. The results showed that the yellow canola seeds contained relatively lower (P<0.05) percentage of ß-sheet and amide I and amide II area compared to the black-type canola seed. This might be an indication that the protein value of the yellow canola seeds as food or feed is different from that of the black canola seeds. The multivariate molecular spectral analyses (AHCA, PCA) showed that there were not significant molecular structural differences in the protein amide I and amide II fingerprint region (ca. 1720-1480 cm(-1)) between the yellow and the black-type of canola seed. It can be concluded that both the yellow and the black-seeded canola contain the same proteins but in different ratios.

The Effect of Plant Age on Resistance to Colletotrichum truncatum in Lens culinaris.

Anthracnose, caused by Colletotrichum truncatum, is a major disease of lentil (Lens culinaris) on the Canadian prairies. Age-dependent resistance to the pathogen was elucidated on (i) the partially resistant line 'CDC Redberry' compared with both resistant (LR59-81) and susceptible ('Eston') lines at different ages across the life cycle of the plant, and (ii) a range of lines with varying resistance at key ages, using both races of the pathogen (races Ct0 and Ct1). CDC Redberry appears moderately susceptible when inoculated at a young age and is more resistant when inoculated 19 to 26 days after planting. The highest disease levels were observed on the oldest plants studied (inoculated at podding stage, 47 days after planting). This pattern of age-dependent resistance in CDC Redberry was the same after inoculation with either race of the pathogen and indicated that the best differentiation occurs when plants are inoculated at the podding stage. 'CDC Robin' showed consistent separation between races at all ages but the most substantial differences were observed on plants inoculated at 47 days of age. Resistant lines LR59-81 and VIR 421 were relatively unaffected by age or by race of C. truncatum compared with partially resistant and susceptible lines. Overall, results suggest that differentiation between resistant and susceptible lines is optimal using older, adult-phased plants.