Summary: Homoeologous exchanges (HEs) have been shown to generate novel gene combinations and phenotypes in a range of polyploid species. Gene presence/absence variation (PAV) is also a major contributor to genetic diversity. In this study, we show that there is an association between these two events, particularly in recent Brassica napus synthetic accessions, and that these represent a novel source of genetic diversity, which can be captured for the improvement of this important crop species. By assembling the pangenome of B. napus, we show that 38% of the genes display PAV behaviour, with some of these variable genes predicted to be involved in important agronomic traits including flowering time, disease resistance, acyl lipid metabolism and glucosinolate metabolism. This study is a first and provides a detailed characterization of the association between HEs and PAVs in B. napus at the pangenome level.

Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners.

Summary: Methods based on single nucleotide polymorphism (SNP), copy number variation (CNV) and presence/absence variation (PAV) discovery provide a valuable resource to study gene structure and evolution. However, as a result of these structural variations, a single reference genome is unable to cover the entire gene content of a species. Therefore, pangenomics analysis is needed to ensure that the genomic diversity within a species is fully represented. Brassica napus is one of the most important oilseed crops in the world and exhibits variability in its resistance genes across different cultivars. Here, we characterized resistance gene distribution across 50 B. napus lines. We identified a total of 1749 resistance gene analogs (RGAs), of which 996 are core and 753 are variable, 368 of which are not present in the reference genome (cv. Darmor‐bzh). In addition, a total of 15 318 SNPs were predicted within 1030 of the RGAs. The results showed that core R‐genes harbour more SNPs than variable genes. More nucleotide binding site‐leucine‐rich repeat (NBS‐LRR) genes were located in clusters than as singletons, with variable genes more likely to be found in clusters. We identified 106 RGA candidates linked to blackleg resistance quantitative trait locus (QTL). This study provides a better understanding of resistance genes to target for genomics‐based improvement and improved disease resistance.

This research was conducted in order to evaluation the salinity stress effect on growth parameters and stem anatomical changes of soybean grown under controlled conditions. Soybean seeds were surface sterilized and then sown into plastic pots filled up with perlite and vermiculite. Seeds were irrigated with Broughton and Dilworth solution daily. At full folded cotyledons stage (5 day after sowing), salinity stress was induced by adding NaCl into nutrition solution with final concentration of 0, 25, 50 and 100 mM. Thirty days after sowing plants were harvested and growth parameters and anatomical changes were evaluated. The results showed that, salinity stress was significantly decreased shoot and root weight either fresh weight or dry weight, in addition, total plant weight, plant height and leaf number were decreased due to salinity stress. Interestingly, leaf area was not affected by salinity stress. Stem microscopic study demonstrated that, salinity stress significantly increased cutin mass and trichome density on epidermal cells. On the other hand, cortex thickness was decreased because of salinity stress while xylem thickness had upward increase when soybean plants were grown under salinity stress especially high level of salinity. Additionally, there were changed in xylem formation and arrangement in stressed plants.