Rapid Phenotyping Adult Plant Resistance to Stem Rust in Wheat Grown under Controlled Conditions

SpeedFlower: a comprehensive speed breeding protocol for indica and japonica rice

To increase rice yields and feed billions of people, it is essential to enhance genetic gains. However, the development of new varieties is hindered by longer generation times and seasonal constraints. To address these limitations, a speed breeding facility has been established and a robust speed breeding protocol, SpeedFlower is developed that allows growing 4-5 generations of indica and/or japonica rice in a year. Our findings reveal that a high red-to-blue (2R > 1B) spectrum ratio, followed by green, yellow and far-red (FR) light, along with a 24-h long day (LD) photoperiod for the initial 15 days of the vegetative phase, facilitated early flowering. This is further enhanced by 10-h short day (SD) photoperiod in the later stage and day and night temperatures of 32/30 °C, along with 65% humidity facilitated early flowering ranging from 52 to 60 days at high light intensity (800 μmol m-2 s-1). Additionally, the use of prematurely harvested seeds and gibberellic acid treatment reduced the maturity duration by 50%. Further, SpeedFlower was validated on a diverse subset of 198 rice accessions from 3K RGP panel encompassing all 12 distinct groups of Oryza sativa L. classes. Our results confirmed that using SpeedFlower one generation can be achieved within 58-71 days resulting in 5.1-6.3 generations per year across the 12 sub-groups. This breakthrough enables us to enhance genetic gain, which could feed half of the world's population dependent on rice.

Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Strategies for accelerating genetic gains in crop plants: special focus on speed breeding

Feeding 10 billion people sustainably by 2050 in the era of slow genetic progress has spurred urgent calls to bring more crops per unit time. Over the last century, crop physiologists and breeders have been trying to alter plant biology to investigate and intervene in developmental processes under controlled chambers. Accelerating the breeding cycle via "speed breeding" was the outcome of these experiments. Speed breeding accelerates the genetic gain via phenome and genome-assisted trait introgression, re-domestication, and plant variety registration. Furthermore, early varietal release through speed breeding offers incremental benefits over conventional methods. However, a lack of resources and species-specific protocols encumber the technological implementation, which can be alleviated by reallocating funds to establish speed breeding units. This review discusses the limitations of conventional breeding methods and various alternative strategies to accelerate the breeding process. It also discusses the intervention at various developmental stages to reduce the generation time and global impacts of speed breeding protocols developed so far. Low-cost, field-based speed breeding protocol developed by Punjab Agricultural University, Ludhiana, Punjab, India to harvest at least three generations of wheat in a year without demanding the expensive greenhouses or growth chambers is also discussed.

Speed breeding in growth chambers and glasshouses for crop breeding and model plant research

'Speed breeding' (SB) shortens the breeding cycle and accelerates crop research through rapid generation advancement. SB can be carried out in numerous ways, one of which involves extending the duration of plants' daily exposure to light, combined with early seed harvest, to cycle quickly from seed to seed, thereby reducing the generation times for some long-day (LD) or day-neutral crops. In this protocol, we present glasshouse and growth chamber-based SB approaches with supporting data from experimentation with several crops. We describe the conditions that promote the rapid growth of bread wheat, durum wheat, barley, oat, various Brassica species, chickpea, pea, grass pea, quinoa and Brachypodium distachyon. Points of flexibility within the protocols are highlighted, including how plant density can be increased to efficiently scale up plant numbers for single-seed descent (SSD). In addition, instructions are provided on how to perform SB on a small scale in a benchtop growth cabinet, enabling optimization of parameters at a low cost.

Unlocking new alleles for leaf rust resistance in the Vavilov wheat collection

Thirteen potentially new leaf rust resistance loci were identified in a Vavilov wheat diversity panel. We demonstrated the potential of allele stacking to strengthen resistance against this important pathogen. Leaf rust (LR) caused by Puccinia triticina is an important disease of wheat (Triticum aestivum L.), and the deployment of genetically resistant cultivars is the most viable strategy to minimise yield losses. In this study, we evaluated a diversity panel of 295 bread wheat accessions from the N. I. Vavilov Institute of Plant Genetic Resources (St Petersburg, Russia) for LR resistance and performed genome-wide association studies (GWAS) using 10,748 polymorphic DArT-seq markers. The diversity panel was evaluated at seedling and adult plant growth stages using three P. triticina pathotypes prevalent in Australia. GWAS was applied to 11 phenotypic data sets which identified a total of 52 significant marker-trait associations representing 31 quantitative trait loci (QTL). Among them, 29 QTL were associated with adult plant resistance (APR). Of the 31 QTL, 13 were considered potentially new loci, whereas 4 co-located with previously catalogued Lr genes and 14 aligned to regions reported in other GWAS and genomic prediction studies. One seedling LR resistance QTL located on chromosome 3A showed pronounced levels of linkage disequilibrium among markers (r ² = 0.7), suggested a high allelic fixation. Subsequent haplotype analysis for this region found seven haplotype variants, of which two were strongly associated with LR resistance at seedling stage. Similarly, analysis of an APR QTL on chromosome 7B revealed 22 variants, of which 4 were associated with resistance at the adult plant stage. Furthermore, most of the tested lines in the diversity panel carried 10 or more combined resistance-associated marker alleles, highlighting the potential of allele stacking for long-lasting resistance.