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Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops. Machine learning has emerged as a promising tool for crop improvement. Here, the authors review transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency of orphan crops breeding.

Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.

Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. The main objective of this study was to assess if the inclusion of historical weather data, maize growth captured through imagery, and important phenotypic traits would improve the predictive power of an established multimodal deep learning model. Evaluation of the model performance when training from scratch showed its ability to accurately predict ~89% of hybrids with high-yield potential and demonstrated enhanced explanatory power compared with previously published models. Shapley Additive explanations (SHAP) analysis indicated the top influential features include plant density, hybrid placement in the field, date to anthesis, parental line, temperature, humidity, and solar radiation. Including weather historical data was important for model performance, significantly enhancing the predictive and explanatory power of the model. For future research, the use of the model can move beyond maize yield prediction by fine-tuning the model on other crop data, serving as a potential decision-making tool for crop breeders to determine high-performing individuals from diverse crop types.

Weeds pose a significant threat to agricultural production, leading to substantial yield losses and increased herbicide usage, with severe economic and environmental implications. This paper uses deep learning to explore a novel approach via targeted segmentation mapping of crop plants rather than weeds, focusing on canola (Brassica napus) as the target crop. Multiple deep learning architectures (ResNet-18, ResNet-34, and VGG-16) were trained for the pixel-wise segmentation of canola plants in the presence of other plant species, assuming all non-canola plants are weeds. Three distinct datasets (T1_miling, T2_miling, and YC) containing 3799 images of canola plants in varying field conditions alongside other plant species were collected with handheld devices at 1.5 m. The top performing model, ResNet-34, achieved an average precision of 0.84, a recall of 0.87, a Jaccard index (IoU) of 0.77, and a Macro F1 score of 0.85, with some variations between datasets. This approach offers increased feature variety for model learning, making it applicable to the identification of a wide range of weed species growing among canola plants, without the need for separate weed datasets. Furthermore, it highlights the importance of accounting for the growth stage and positioning of plants in field conditions when developing weed detection models. The study contributes to the growing field of precision agriculture and offers a promising alternative strategy for weed detection in diverse field environments, with implications for the development of innovative weed control techniques.

The global demand for oilseeds is increasing along with the human population. The family of Brassicaceae crops are no exception, typically harvested as a valuable source of oil, rich in beneficial molecules important for human health. The global capacity for improving Brassica yield has steadily risen over the last 50 years, with the major crop Brassica napus (rapeseed, canola) production increasing to ~72 Gt in 2020. In contrast, the production of Brassica mustard crops has fluctuated, rarely improving in farming efficiency. The drastic increase in global yield of B. napus is largely due to the demand for a stable source of cooking oil. Furthermore, with the adoption of highly efficient farming techniques, yield enhancement programs, breeding programs, the integration of high-throughput phenotyping technology and establishing the underlying genetics, B. napus yields have increased by >450 fold since 1978. Yield stability has been improved with new management strategies targeting diseases and pests, as well as by understanding the complex interaction of environment, phenotype and genotype. This review assesses the global yield and yield stability of agriculturally important oilseed Brassica species and discusses how contemporary farming and genetic techniques have driven improvements.

Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value < 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality.

The surge in high‐throughput technologies has empowered the acquisition of vast genomic datasets, prompting the search for genetic markers and biomarkers relevant to complex traits. However, grappling with the inherent complexities of high dimensionality and sparsity within these datasets poses formidable hurdles. The immense number of features and their potential redundancy demand efficient strategies for extracting pertinent information and identifying significant markers. Feature selection is important in large genomic data as it helps in enhancing interpretability and computational efficiency. This study focuses on addressing these challenges through a comprehensive investigation into genomic feature selection methodologies, employing a rich soybean (Glycine max L. Merr.) dataset comprising 966 lines with over 5.5 million single nucleotide polymorphisms. Emphasizing the “small n large p” dilemma prevalent in contemporary genomic studies, we compared the efficacy of traditional genome‐wide association studies (GWAS) with two prominent machine learning tools, random forest and extreme gradient boosting, in pinpointing predictive features. Utilizing the expansive soybean dataset, we assessed the performance of these methodologies in selecting features that optimize predictive modeling for various phenotypes. By constructing predictive models based on the selected features, we ascertain the comparative prediction accuracies, thereby illuminating the strengths and limitations of these feature selection methodologies in the realm of genomic data analysis. Core Ideas High‐throughput genomic technologies produce vast datasets that reduce the efficiency of trait association. Genomic feature selection can increase the accuracy and computational efficiency of trait association. Our comparison of feature selection approaches can help readers optimize feature selection and trait association. Plain Language Summary Modern genetic research relies on vast amounts of data obtained through advanced technologies. This flood of information makes it difficult to find the important genetic markers linked to complex traits. Our study addresses this challenge by exploring different ways to sift through the data to improve the association of genetic markers with traits. We focused on soybean, using a dataset with millions of genetic markers from nearly a thousand different soybean lines. We looked at traditional methods such as genome‐wide association studies alongside newer machine learning techniques. By comparing these methods, we aimed to see which ones are best at picking out the most important genetic features for predicting traits. Our findings shed light on the strengths and weaknesses of different feature selection methods in genetic research. This can help researchers choose the best approach for their studies, ultimately improving our understanding of genetic traits.

The timing of flowering in soybean [Glycine max (L.) Merr.], a key legume crop, is influenced by many factors, including daylight length or photoperiodic sensitivity, that affect crop yield, productivity, and geographical adaptation. Despite its importance, a comprehensive understanding of the local linkage landscape and allelic diversity within regions of the genome influencing flowering and contributing to phenotypic variation in subpopulations has been limited. This study addresses these gaps by conducting an in‐depth trait association and linkage analysis coupled with local haplotyping using advanced bioinformatics tools, including crosshap, to characterize genomic variation using a pangenome dataset representing 915 domesticated and wild‐type individuals. The association analysis identified eight significant loci on seven chromosomes. Moving beyond traditional association analysis, local haplotyping of targeted regions on chromosomes 6 and 20 identified distinct haplotype structures, variation patterns, and genomic candidates influencing flowering in subpopulations. These results suggest the action of a network of genomic candidates influencing flowering time and an untapped reservoir of genomic variation for this trait in wild germplasm. Notably, GlymaLee.20G147200 on chromosome 20 was identified as a candidate gene that may cause delayed flowering in soybean, potentially through histone modifications of floral repressor loci as seen in Arabidopsis thaliana (L.) Heynh. These findings support future functional validation of haplotype‐based alleles for marker‐assisted breeding and genomic selection to enhance latitude adaptability of soybean without compromising yield. Core Ideas Flowering in soybean is influenced by many factors, including photoperiod, that affect yield and latitude adaptability. We conducted association tests and local haplotyping to identify genomic regions influencing flowering using 915 soybean lines. We found distinct haplotype structures potentially influencing flowering on chromosomes 6 and 20. These findings support future functional validation of haplotype‐based alleles for marker‐assisted breeding and genomic selection. Plain Language Summary This study focuses on understanding the genetic factors that cause some soybean varieties to flower early while others flower later, a trait that influences their adaptation to different latitudes. We investigated the patterns of DNA sequence variation in cultivated and wildtype soybean using advanced bioinformatics tools, focusing on combinations of genetic variation that are inherited together. Our results show the presence of distinct DNA sequences in wild and cultivated soybeans and their influence on flowering time. Understanding these genetic variations helps in breeding varieties that can be grown in different latitudes with optimal flowering time.

Flowering time is an important developmental stage in plants, influenced by multiple genes and environmental factors. Understanding its genetic basis and interaction with the environment facilitates the development of improved varieties adapted to different environments. Conventional Genome-Wide Association Studies (GWAS) have been widely used to associate genetic markers with heritable traits, but they do not inherently capture interactions among single nucleotide polymorphisms (SNPs) or between SNPs and the environment. Machine Learning (ML) approaches can model these interactions and improve trait prediction even in the presence of noise and missing data. In this study, multi-environment lentil (Lens culinaris Medik.) data were analysed using GWAS and two widely used ML models, Random Forest and XGBoost, to identify genetic markers associated with flowering time. Model interpretability was enhanced using Explainable AI (XAI) techniques, including SHapley Additive exPlanations. GWAS identified eight significant loci across chromosomes one, two, five and seven, with the most significant SNP located at Chr2_530433205, while ML approaches identified nine markers on chromosomes one, two, three, five and seven, with the most significant SNP at Chr7_523220088. The majority of the identified markers were linked to candidate genes for flowering, while ML also identified potential epistasis. These findings highlight ML as a powerful complementary tool to GWAS for trait association.

Soybean (Glycine max) is a legume species of significant economic and nutritional value. The yield of soybean continues to increase with the breeding of improved varieties, and this is likely to continue with the application of advanced genetic and genomic approaches for breeding. Genome technologies continue to advance rapidly, with an increasing number of high-quality genome assemblies becoming available. With accumulating data from marker arrays and whole-genome resequencing, studying variations between individuals and populations is becoming increasingly accessible. Furthermore, the recent development of soybean pangenomes has highlighted the significant structural variation between individuals, together with knowledge of what has been selected for or lost during domestication and breeding, information that can be applied for the breeding of improved cultivars. Because of this, resources such as genome assemblies, SNP datasets, pangenomes and associated databases are becoming increasingly important for research underlying soybean crop improvement.