Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning

Co-Location of QTL for Vigor and Resistance to Three Diseases in Juglans microcarpa × J. regia Rootstocks

A QTL on chromosome 4D of the Juglans microcarpa × J. regia genome that co-located resistance against Agrobacterium tumefaciens, Phytophthora pini, and Phytophthora cinnamomi disease scores was investigated for additional traits. Phenotypic data for Pratylenchus vulnus counts and tree height were analyzed in this study for the same hybrids previously used to identify this QTL. Using the same GBS genotype data, the same co-located QTL for A. tumefaciens and Phytophthora spp. disease scores were reproduced and the QTL for P. vulnus counts and tree height were co-located with resistance to A. tumefaciens and Phytophthora spp. Moreover, we found GBS genotype data to harbor additional genetic variation unrelated to any of the traits analyzed. Marker-assisted and genomic selection models were created and assessed for their performance in selection. The ability to predict traits using SNP data was strongest with two-year tree height, followed by A. tumefaciens disease score, three-year tree height, Phytophthora spp. disease score, and P. vulnus counts. These results suggest a shared mechanism of action that links disease to tree height. Moreover, deploying these selection models would assist efforts in walnut improvement for rootstock genotypes.

Broad-spectrum resistance to fungal foliar diseases in wheat: recent efforts and achievements

Wheat (Triticum spp.) is one of the most important cereal crops in the world. Several diseases affect wheat production and can cause 20-80% yield loss annually. Out of these diseases, stripe rust, also known as yellow rust (Puccinia striiformis f. sp. tritici), stem rust (Puccinia graminis f. sp. tritici), leaf rust (Puccinia recondita), and powdery mildew (Blumeria graminis f. sp. tritici) are the most important fungal diseases that infect the foliar part of the plant. Many efforts were made to improve wheat resistance to these diseases. Due to the continuous advancement in sequencing methods and genomic tools, genome-wide association study has become available worldwide. This analysis enabled wheat breeders to detect genomic regions controlling the resistance in specific countries. In this review, molecular markers significantly associated with the resistance of the mentioned foliar diseases in the last five years were reviewed. Common markers that control broad-spectrum resistance in different countries were identified. Furthermore, common genes controlling the resistance of more than one of these foliar diseases were identified. The importance of these genes, their functional annotation, and the potential for gene enrichment are discussed. This review will be valuable to wheat breeders in producing genotypes with broad-spectrum resistance by applying genomic selection for the target common markers and associated genes.

Genotypic variation in winter wheat for fusarium foot rot and its biocontrol using Clonostachys rosea

Biological control to manage plant diseases is an environmentally friendly alternative to using chemical pesticides. However, little is known about the role of genetic variation in plants affecting the efficacy of biological control agents (BCAs). The aim of this study was to explore the genetic variation in winter wheat for disease susceptibility to fusarium foot rot caused by Fusarium graminearum and variation in biocontrol efficacy of the fungal BCA Clonostachys rosea to control the disease. In total, 190 winter wheat genotypes were evaluated under controlled conditions in two treatments, i.e. (i) F. graminearum (Fg) and (ii) F. graminearum infection on C. rosea treated seeds (FgCr). Alongside disease severity, plant growth-related traits such as shoot length and root length were also measured. Comparison of genotypes between the two treatments enabled the dissection of genotypic variation for disease resistance and C. rosea efficacy. The study revealed significant variation among plant genotypes for fusarium foot rot susceptibility and other growth traits in treatment Fg. Moreover, significant variation in C. rosea efficacy was also observed in genotype contrasts between the two treatments for all traits. Using a 20K marker array, a genome-wide association study was also performed. We identified a total of 18 significant marker-trait associations for disease resistance and C. rosea efficacy for all the traits. Moreover, the markers associated with disease resistance and C. rosea efficacy were not co-localized, highlighting the independent inheritance of these traits, which can facilitate simultaneous selection for cultivar improvement.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

An integrated feature selection approach to high water stress yield prediction

The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

Identification and estimation of lodging in bread wheat genotypes using machine learning predictive algorithms

BACKGROUND

Lodging or stem bending decreases wheat yield quality and quantity. Thus, the traits reflected in early lodging wheat are helpful for early monitoring to some extent. In order to identify the superior genotypes and compare multiple linear regression (MLR) with support vector regression (SVR), artificial neural network (ANN), and random forest regression (RF) for predicting lodging in Iranian wheat accessions, a total of 228 wheat accessions were cultivated under field conditions in an alpha-lattice experiment, randomized incomplete block design, with two replications in two cropping seasons (2018-2019 and 2019-2020). To measure traits, a total of 20 plants were isolated from each plot and were measured using image processing.

RESULTS

The lodging score index (LS) had the highest positive correlation with plant height (r = 0.78**), Number of nodes (r = 0.71**), and internode length 1 (r = 0.70**). Genotypes were classified into four groups based on heat map output. The most lodging-resistant genotypes showed a lodging index of zero or close to zero. The findings revealed that the RF algorithm provided a more accurate estimate (R2 = 0.887 and RMSE = 0.091 for training data and R2 = 0.768 and RMSE = 0.124 for testing data) of wheat lodging than the ANN and SVR algorithms, and its robustness was as good as ANN but better than SVR.

CONCLUSION

Overall, it seems that the RF model can provide a helpful predictive and exploratory tool to estimate wheat lodging in the field. This work can contribute to the adoption of managerial approaches for precise and non-destructive monitoring of lodging.

A deep learning model for rapid classification of tea coal disease

BACKGROUND

The common tea tree disease known as "tea coal disease" (Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification.

RESULTS

Both RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging.

CONCLUSIONS

This study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.

To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?

The utilization of high-throughput in-field phenotyping systems presents new opportunities for evaluating crop stress. However, existing studies have primarily focused on individual stresses, overlooking the fact that crops in field conditions frequently encounter multiple stresses, which can display similar symptoms or interfere with the detection of other stress factors. Therefore, this study aimed to investigate the impact of wheat yellow rust on reflectance measurements and nitrogen status assessment. A multi-sensor mobile platform was utilized to capture RGB and multispectral images throughout a 2-year fertilization-fungicide trial. To identify disease-induced damage, the SegVeg approach, which combines a U-NET architecture and a pixel-wise classifier, was applied to RGB images, generating a mask capable of distinguishing between healthy and damaged areas of the leaves. The observed proportion of damage in the images demonstrated similar effectiveness to visual scoring methods in explaining grain yield. Furthermore, the study discovered that the disease not only affected reflectance through leaf damage but also influenced the reflectance of healthy areas by disrupting the overall nitrogen status of the plants. This emphasizes the importance of incorporating disease impact into reflectance-based decision support tools to account for its effects on spectral data. This effect was successfully mitigated by employing the NDRE vegetation index calculated exclusively from the healthy portions of the leaves or by incorporating the proportion of damage into the model. However, these findings also highlight the necessity for further research specifically addressing the challenges presented by multiple stresses in crop phenotyping.

The Keys to Controlling Wheat Rusts: Identification and Deployment of Genetic Resistance

Rust diseases are among the major constraints for wheat production worldwide due to the emergence and spread of highly destructive races of Puccinia. The most common approach to minimize yield losses due to rust is to use cultivars that are genetically resistant. Modern wheat cultivars, landraces, and wild relatives can contain undiscovered resistance genes, which typically encode kinase or nucleotide-binding site leucine rich repeat (NLR) domain containing receptor proteins. Recent research has shown that these genes can provide either resistance in all growth stages (all-stage resistance; ASR) or specially in later growth stages (adult-plant resistance; APR). ASR genes are pathogen and race-specific, meaning can function against selected races of the Puccinia fungus due to the necessity to recognize specific avirulence molecules in the pathogen. APR genes are either pathogen-specific or multipathogen resistant but often race-nonspecific. Prediction of resistance genes through rust infection screening alone remains complex when more than one resistance gene is present. However, breakthroughs during the past half century such as the single-nucleotide polymorphism-based genotyping techniques and resistance gene isolation strategies like mutagenesis, resistance gene enrichment, and sequencing (MutRenSeq), mutagenesis and chromosome sequencing (MutChromSeq), and association genetics combined with RenSeq (AgRenSeq) enables rapid transfer of resistance from source to modern cultivars. There is a strong need for combining multiple genes for better efficacy and longer-lasting resistance. Hence, techniques like gene cassette creation speeds up the gene combination process, but their widespread adoption and commercial use is limited due to their transgenic nature.