A Robust Automated Image-Based Phenotyping Method for Rapid Vegetative Screening of Wheat Germplasm for Nitrogen Use Efficiency

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.

Identifying genomic regions determining shoot and root traits related to nitrogen uptake efficiency in a multiparent advanced generation intercross (MAGIC) winter wheat population in a high-throughput phenotyping facility

In the context of a continuously increasing human population that needs to be fed, with environmental protection in mind, nitrogen use efficiency (NUE) improvement is becoming very important. To understand the natural variation of traits linked to nitrogen uptake efficiency (UPE), one component of NUE, the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting nitrogen (N) levels in a high-throughput phenotyping facility for six weeks. Three biomass-related, three root-related, and two reflectance-related traits were measured weekly under each treatment. Subsequently, the population was genetically analysed using a total of 13,060 polymorphic haplotypes and singular SNPs for a genome-wide association study (GWAS). In total, we detected 543 quantitative trait loci (QTL) across all time points and traits, which were pooled into 42 stable QTL (sQTL; present in at least three of the six weeks). Besides Rht-B1 and Rht-D1, candidate genes playing a role in gibberellic acid-regulated growth and nitrate transporter genes from the NPF gene family, like NRT 1.1, were linked to sQTL. Two novel sQTL on chromosomes 5 A and 6D showed pleiotropic effects on several traits. The high number of N-specific sQTL indicates that selection for UPE is useful specifically under N-limited conditions.

Developing future heat-resilient vegetable crops

Climate change seriously impacts global agriculture, with rising temperatures directly affecting the yield. Vegetables are an essential part of daily human consumption and thus have importance among all agricultural crops. The human population is increasing daily, so there is a need for alternative ways which can be helpful in maximizing the harvestable yield of vegetables. The increase in temperature directly affects the plants' biochemical and molecular processes; having a significant impact on quality and yield. Breeding for climate-resilient crops with good yields takes a long time and lots of breeding efforts. However, with the advent of new omics technologies, such as genomics, transcriptomics, proteomics, and metabolomics, the efficiency and efficacy of unearthing information on pathways associated with high-temperature stress resilience has improved in many of the vegetable crops. Besides omics, the use of genomics-assisted breeding and new breeding approaches such as gene editing and speed breeding allow creation of modern vegetable cultivars that are more resilient to high temperatures. Collectively, these approaches will shorten the time to create and release novel vegetable varieties to meet growing demands for productivity and quality. This review discusses the effects of heat stress on vegetables and highlights recent research with a focus on how omics and genome editing can produce temperature-resilient vegetables more efficiently and faster.

Crop germplasm: Current challenges, physiological-molecular perspective, and advance strategies towards development of climate-resilient crops

Germplasm is a long-term resource management mission and investment for civilization. An estimated ∼7.4 million accessions are held in 1750 plant germplasm centres around the world; yet, only 2% of these assets have been utilized as plant genetic resources (PGRs). According to recent studies, the current food yield trajectory will be insufficient to feed the world's population in 2050. Additionally, possible negative effects in terms of crop failure because of climate change are already being experienced across the world. Therefore, it is necessary to reconciliation of research advancement and innovation of practices for further exploration of the potential of crop germplasm especially for the complex traits associated with yield such as water- and nitrogen use efficiency. In this review, we tried to address current challenges, research gaps, physiological and molecular aspects of two broad spectrum complex traits such as water- and nitrogen-use efficiency, and advanced integrated strategies that could provide a platform for combined stress management for climate-smart crop development. Additionally, recent development in technologies that are directly related to germplasm characterization was highlighted for further molecular utilization towards the development of elite varieties.

Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery

Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R², root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R², RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R², RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.

Genetic Engineering and Genome Editing for Improving Nitrogen Use Efficiency in Plants

Low nitrogen availability is one of the main limiting factors for plant growth and development, and high doses of N fertilizers are necessary to achieve high yields in agriculture. However, most N is not used by plants and pollutes the environment. This situation can be improved by enhancing the nitrogen use efficiency (NUE) in plants. NUE is a complex trait driven by multiple interactions between genetic and environmental factors, and its improvement requires a fundamental understanding of the key steps in plant N metabolism—uptake, assimilation, and remobilization. This review summarizes two decades of research into bioengineering modification of N metabolism to increase the biomass accumulation and yield in crops. The expression of structural and regulatory genes was most often altered using overexpression strategies, although RNAi and genome editing techniques were also used. Particular attention was paid to woody plants, which have great economic importance, play a crucial role in the ecosystems and have fundamental differences from herbaceous species. The review also considers the issue of unintended effects of transgenic plants with modified N metabolism, e.g., early flowering—a research topic which is currently receiving little attention. The future prospects of improving NUE in crops, essential for the development of sustainable agriculture, using various approaches and in the context of global climate change, are discussed.

High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance

Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.

Digital Phenotyping to Delineate Salinity Response in Safflower Genotypes

Salinity is a major contributing factor to the degradation of arable land, and reductions in crop growth and yield. To overcome these limitations, the breeding of crop varieties with improved salt tolerance is needed. This requires effective and high-throughput phenotyping to optimize germplasm enhancement. Safflower (Carthamus tinctorius L.), is an underappreciated but highly versatile oilseed crop, capable of growing in saline and arid environments. To develop an effective and rapid phenotyping protocol to differentiate salt responses in safflower genotypes, experiments were conducted in the automated imaging facility at Plant Phenomics Victoria, Horsham, focussing on digital phenotyping at early vegetative growth. The initial experiment, at 0, 125, 250, and 350 mM sodium chloride (NaCl), showed that 250 mM NaCl was optimum to differentiate salt sensitive and tolerant genotypes. Phenotyping of a diverse set of 200 safflower genotypes using the developed protocol defined four classes of salt tolerance or sensitivity, based on biomass and ion accumulation. Salt tolerance in safflower was dependent on the exclusion of Na⁺ from shoot tissue and the maintenance of K⁺ uptake. Salinity response identified in glasshouse experiments showed some consistency with the performance of representatively selected genotypes tested under sodic field conditions. Overall, our results suggest that digital phenotyping can be an effective high-throughput approach in identifying candidate genotypes for salt tolerance in safflower.

Spectroscopic Estimation of N Concentration in Wheat Organs for Assessing N Remobilization Under Different Irrigation Regimes

Nitrogen (N) remobilization is a critical process that provides substantial N to winter wheat grains for improving yield productivity. Here, the remobilization of N from anthesis to maturity in two wheat cultivars under three irrigation regimes was measured and its relationship to organ N concentration was examined. Based on spectral data of organ powder samples, partial least squares regression (PLSR) models were calibrated to estimate N concentration (N _mass) and validated against laboratory-based measurements. Although spectral reflectance could accurately estimate N _mass, the PLSR-based N _mass-spectra predictive model was found to be organ-specific, organs at the top canopy (chaff and top three leaves) received the best predictions (R ² > 0.88). In addition, N remobilization efficiency (NRE) in the top two leaves and top third internode was highly correlated with its corresponding N concentration change (ΔN _mass) with an R ² of 0.90. ΔN _mass of the top first internode (TIN1) explained 78% variation of the whole-plant NRE. This study provides a proof of concept for estimating N concentration and assessing N remobilization using hyperspectral data of individual organs, which offers a non-chemical and low-cost approach to screen germplasms for an optimal NRE in drought-resistance breeding.

Rapid Screening of Nitrogen Use Efficiency in Perennial Ryegrass (Lolium perenne L.) Using Automated Image-Based Phenotyping

Perennial ryegrass (Lolium perenne L.) is a dominant species in temperate Australian pastures. Currently, nitrogenous fertilizers are used to support herbage production for pasture and fodder. Increasing the nitrogen use efficiency (NUE) of pasture grasses could decrease the amount of fertilizer application and reduce nitrogen (N) leaching into the environment. NUE, defined as units of dry matter production per unit of supplied nitrogen, is a complex trait in which genomic selection may provide a promising strategy in breeding. Our objective was to develop a rapid, high-throughput screening method to enable genomic selection for y -60NUE in perennial ryegrass. NUE of 76 genotypes of perennial ryegrass from a breeding population were screened in a greenhouse using an automated image-based phenomics platform under low (0.5 mM) and moderate (5 mM) N levels over 3 consecutive harvests. Significant (p < 0.05) genotype, treatment, and genotype by treatment interactions for dry matter yield and NUE were observed. NUE under low and moderate N treatments was significantly correlated. Of the seven plant architecture features directly extracted from image analysis and four secondarily derived measures, mean projected plant area (MPPA) from the two side view images had the highest correlation with dry matter yield (r = 0.94). Automated digital image-based phenotyping enables temporal plant growth responses to N to be measured efficiently and non-destructively. The method developed in this study would be suitable for screening large populations of perennial ryegrass growth in response to N for genomic selection purposes.

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.

Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm

Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.