GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley

Residual network improves the prediction accuracy of genomic selection

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review

Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.

Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches

Sugarcane smut and Pachymetra root rots are two serious diseases of sugarcane, with susceptible infected crops losing over 30% of yield. A heritable component to both diseases has been demonstrated, suggesting selection could improve disease resistance. Genomic selection could accelerate gains even further, enabling early selection of resistant seedlings for breeding and clonal propagation. In this study we evaluated four types of algorithms for genomic predictions of clonal performance for disease resistance. These algorithms were: Genomic best linear unbiased prediction (GBLUP), including extensions to model dominance and epistasis, Bayesian methods including BayesC and BayesR, Machine learning methods including random forest, multilayer perceptron (MLP), modified convolutional neural network (CNN) and attention networks designed to capture epistasis across the genome-wide markers. Simple hybrid methods, that first used BayesR/GWAS to identify a subset of 1000 markers with moderate to large marginal additive effects, then used attention networks to derive predictions from these effects and their interactions, were also developed and evaluated. The hypothesis for this approach was that using a subset of markers more likely to have an effect would enable better estimation of interaction effects than when there were an extremely large number of possible interactions, especially with our limited data set size. To evaluate the methods, we applied both random five-fold cross-validation and a structured PCA based cross-validation that separated 4702 sugarcane clones (that had disease phenotypes and genotyped for 26k genome wide SNP markers) by genomic relationship. The Bayesian methods (BayesR and BayesC) gave the highest accuracy of prediction, followed closely by hybrid methods with attention networks. The hybrid methods with attention networks gave the lowest variation in accuracy of prediction across validation folds (and lowest MSE), which may be a criteria worth considering in practical breeding programs. This suggests that hybrid methods incorporating the attention mechanism could be useful for genomic prediction of clonal performance, particularly where non-additive effects may be important.

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits

Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such, genomic prediction in sugarcane presents an interesting case for machine learning (ML) methods, which are purportedly able to deal with high levels of complexity in prediction. Here, we investigated deep learning (DL) neural networks, including multilayer networks (MLP) and convolution neural networks (CNN), and an ensemble machine learning approach, random forest (RF), for genomic prediction in sugarcane. The data set used was 2912 sugarcane clones, scored for 26,086 genome wide single nucleotide polymorphism markers, with final assessment trial data for total cane harvested (TCH), commercial cane sugar (CCS), and fiber content (Fiber). The clones in the latest trial (2017) were used as a validation set. We compared prediction accuracy of these methods to genomic best linear unbiased prediction (GBLUP) extended to include dominance and epistatic effects. The prediction accuracies from GBLUP models were up to 0.37 for TCH, 0.43 for CCS, and 0.48 for Fiber, while the optimized ML models had prediction accuracies of 0.35 for TCH, 0.38 for CCS, and 0.48 for Fiber. Both RF and DL neural network models have comparable predictive ability with the additive GBLUP model but are less accurate than the extended GBLUP model.

Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses

Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage

Proper grain storage plays a critical role in maintaining food quality. Among a variety of grains, wheat has emerged as one of the most important grain reserves globally due to its short growing period, high yield, and storage resistance. To improve the quality assessment of wheat during storage, this study collected and analyzed monitoring data from more than 20 regions in China, including information on storage environmental parameters and changes in wheat pesticide residue concentrations. Based on these factors, an Autoformer-based model was developed to predict the changes in wheat pesticide residue concentrations during storage. A comprehensive wheat quality assessment index Q was set for the predicted and true values of pesticide residue concentrations, then combined with the K-means++ algorithm to assess the quality of wheat during storage. The results of the study demonstrate that the Autoformer model achieved the optimal prediction results and the smallest error values. The mean absolute error (MAE) and the other four error values are 0.11017, 0.01358, 0.04681, 0.11654, and 0.13005. The findings offer technical assistance and a scientific foundation for enhancing the quality of stored wheat.

Crop genomic selection with deep learning and environmental data: A survey

Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.

Genetic relationships and low diversity among the tea-oil Camellia species in Sect. Oleifera, a bulk woody oil crop in China

Tea-oil Camellia is one of the four woody oil crops in the world and has high ecological, economic and medicinal values. However, there are great differences in the classification and merging of tea-oil Camellia Sect. Oleifera species, which brings difficulties to the innovative utilization and production of tea-oil Camellia resources. Here, ISSR, SRAP and chloroplast sequence markers were analyzed in 18 populations of tea-oil Camellia Sect. Oleifera species to explore their phylogenetic relationships and genetic diversity. The results showed that their genetic diversity were low, with mean H and π values of 0.16 and 0.00140, respectively. There was high among-population genetic differentiation, with ISSR and SRAP markers showing an Fst of 0.38 and a high Nm of 1.77 and cpDNA markers showing an Fst of 0.65 and a low Nm of 0.27. The C. gauchowensis, C. vietnamensis and Hainan Island populations formed a single group, showing the closest relationships, and supported being the same species for them with the unifying name C. drupifera and classifying the resources on Hainan Island as C. drupifera. The tea-oil Camellia resources of Hainan Island should be classified as a special ecological type or variety of C. drupifera. However, cpDNA marker-based STRUCTURE analysis showed that the genetic components of the C. osmantha population formed an independent, homozygous cluster; hence, C. osmantha should be a new species in Sect. Oleifera. The C. oleifera var. monosperma and C. oleifera populations clustered into two distinct clades, and the C. oleifera var. monosperma populations formed an independent cluster, accounting for more than 99.00% of its genetic composition; however, the C. oleifera populations contained multiple different cluster components, indicating that C. oleifera var. monosperma significantly differs from C. oleifera and should be considered the independent species C. meiocarpa. Haplotype analysis revealed no rapid expansion in the tested populations, and the haplotypes of C. oleifera, C. meiocarpa and C. osmantha evolved from those of C. drupifera. Our results support the phylogenetic classification of Camellia subgenera, which is highly significant for breeding and production in tea-oil Camellia.