Genomic selection (GS) is a promising approach exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. In plant breeding it provides opportunities to increase genetic gain of complex traits per unit time and cost. The cost benefit balance was an important consideration for GS to work in crop plants. Availability of genome-wide high-throughput, cost effective and flexible markers, having low ascertainment bias, suitable for large population size as well for both model and non-model crop species with or without the reference genome sequence was the most important factor for its successful and effective implementation in crop species. These factors were the major limitations to marker systems viz., SSR and array-based, and was unimaginable before the availability of next-generation sequencing (NGS) technologies which have provided novel SNP genotyping platforms especially the genotyping by sequencing (GBS). These marker technologies have changed the entire scenario of marker applications and made the use of GS a routine work for crop improvement in both model and non-model crop species. The NGS based genotyping have increased genomic-estimated breeding value (GEBV) prediction accuracies over other established marker platform in cereals and other crop species, and made dream of GS true in crop breeding. But to harness the true benefits from GS, these marker technologies will be combined with high-throughput phenotyping (HTP) for achieving the valuable genetic gain from complex traits. Moreover, the continuous decline in sequencing cost will make the WGS feasible and cost effective for GS in near future. Till that time matures the targeted sequencing seems to be more cost-effective option for large scale marker discovery and GS, particularly in case of large and un-decoded genomes.

Over the past few decades, heavy metal contamination in soil and water has increased due to anthropogenic activities. The higher exposure of crop plants to heavy metal stress reduces growth and yield, and affect the sustainability of agricultural production. In this regard, the use of silicon (Si) supplementation offers a promising prospect since numerous studies have reported the beneficial role of Si in mitigating stresses imposed by biotic as well as abiotic factors including heavy metal stress. The fundamental mechanisms involved in the Si-mediated heavy metal stress tolerance include reduction of metal ions in soil substrate, co-precipitation of toxic metals, metal-transport related gene regulation, chelation, stimulation of antioxidants, compartmentation of metal ions, and structural alterations in plants. Exogenous application of Si has been well documented to increase heavy metal tolerance in numerous plant species. The beneficial effects of Si are particularly evident in plants able to accumulate high levels of Si. Consequently, to enhance metal tolerance in plants, the inherent genetic potential for Si uptake should be improved. In the present review, we have discussed the potential role and mechanisms involved in the Si-mediated alleviation of metal toxicity as well as different approaches for enhancing Si-derived benefits in crop plants.

Cadmium (Cd) is a trace element causing severe toxicity symptoms in plants, besides posing hazardous fitness issue due to its buildup in the human body through food chain. Nanoparticles (NPs) are recently employed as a novel strategy to directly ameliorate the Cd stress and acted as nano-fertilizers. The intend of the current study was to explore the effects of zinc oxide nanoparticles (ZnO-NPs; 50 mg/L) on plant growth, photosynthetic activity, elemental status and antioxidant activity in Oryza sativa (rice) under Cd (0.8 mM) stress. To this end, the rice plants are treated by Cd stress at 15 days after sowing (DAS), and the treatment was given directly into the soil. Supply of ZnO-NPs as foliar spray was given for five consecutive days from 30 to 35 DAS, and sampling was done at 45 DAS. However, rice plants supplemented with ZnO-NPs under the Cd toxicity revealed significantly increased shoot length (SL; 34.0%), root fresh weight (RFW; 30.0%), shoot dry weight (SDW; 23.07%), and root dry weight (RDW; 12.24%). Moreover, the ZnO-NPs supplement has also positive effects on photosynthesis related parameters, SPAD value (40%), chloroplast structure, and qualitatively high fluorescence observed by confocal microscopy even under Cd stress. ZnO-NPs also substantially prevented the increases of hydrogen peroxide (H₂O₂) and malondialdehyde (MDA) triggered by Cd. Physiological and biochemical analysis showed that ZnO-NPs increased enzymatic activities of superoxide dismutase (SOD; 59%), catalase (CAT; 52%), and proline (17%) that metabolize reactive oxygen species (ROS); these increases coincided with the changes observed in the H₂O₂ and MDA accumulation after ZnO-NPs application. In conclusion, ZnO-NPs application to foliage has great efficiency to improve biomass, photosynthesis, protein, antioxidant enzymes activity, mineral nutrient contents and reducing Cd levels in rice. This can be attributed mainly from reduced oxidative damage resulted due to the ZnO-NPs application.

Silicon (Si), a beneficial element for plants, is known for its prophylactic effect under stress conditions.

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.