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Sorghum genotypes vary in their response to higher sowing density, but the traits explaining these variations are unknown. In the present study, a 3D-imaging based approach identified the phenotypic traits responsible for the genetic variation in sorghum's response to high sowing density. Twenty sorghum genotypes, some varying in their response to density, were grown and 3D-images were collected weekly between weeks 4–6. From these images, 80 phenotypic traits, including 33 architectural and 47 multispectral, were extracted. The within-genotype means of these 80 traits, and two indicators of the sowing density response (Biomass ratio (Br) and Transpiration ratio (Tr)), measured in a previous study with 13 common genotypes, were used in a Spearman correlation analysis. Seventeen and four traits were strongly correlated with Br and Tr, respectively. The majority of these traits, predominantly architectural, strongly suggest that, under high sowing density, a fuller light interception, having more leaf area in the lower canopy, lead to a larger Br, while more vertically aligned leaves favour larger Tr values, which related to higher water use efficiency in another study. Furthermore, a Principal Component Analysis (PCA) indicated traits contributing to better photosynthesis could be used to estimate Br. Similarly, a combination of traits relating to leaf angle were good indicators of the genetic variation in Tr values. These results provide insights about the strategies some sorghum genotypes have developed to thrive under higher sowing density and that could be used as biomarkers for the breeding of density-resistant cultivars.

Abstract Background Bananas (Musa spp.) are an important crop worldwide. Most modern cultivars resulted from a complex polyploidization history that comprised three whole genome duplications (WGDs) shaping the haploid Musa genome, followed by inter- and intra-specific crosses between Musa acuminata and M. balbisiana (A and B genome, respectively). Unresolved hybridizations finally led to banana diversification into several autotriploid (AAA) and allotriploid cultivars (AAB and ABB). Using transcriptomic data, we investigated the impact of the genome structure on gene expression patterns in roots of 12 different triploid genotypes covering AAA, AAB and ABB subgenome constitutions. Results We demonstrate that (i) there are different genome structures, (ii) expression patterns go beyond the predicted genomic groups, and (iii) the proportion of the B genome influences the gene expression. The presence of the B genome is associated with a higher expression of genes involved in flavonoid biosynthesis, fatty acid metabolism, amino sugar and nucleotide sugar metabolism and oxidative phosphorylation. There are cultivar-specific chromosome regions with biased B:A gene expression ratios that demonstrate homoeologous exchanges (HE) between A and B sub-genomes. In two cultivars, aneuploidy was detected. We identified 3674 genes with a different expression level between allotriploid and autotriploid with ~ 57% having recently duplicated copies (paralogous). We propose a Paralog Inclusive Expression (PIE) analysis that appears to be suitable for genomes still in a downsizing and fractionation process following whole genome duplications. Our approach allows highlighting the genes with a maximum likelihood to affect the plant phenotype. Conclusions This study on banana is a good case to investigate the effects of alloploidy in crops. We conclude that allopolyploidy triggered changes in the genome structure of a crop and it clearly influences the gene.

This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with PlantEye (R) laser scanners. Firstly, we segmented the crop canopies from the background in 3D space using the Region Growing Segmentation algorithm. Then, Convolutional Neural Network (CNN) based ML algorithms were fine-tuned for plant counting. Application of the CNN-based (Convolutional Neural Network) processing architecture was possible only after we reduced the dimensionality of the data to 2D. This allowed for the identification of individual plants and their counting with an accuracy of 93.18% and 92.87% for mung bean and chickpea plants, respectively. These steps were connected to the phenotyping pipeline, which can now replace manual counting operations that are inefficient, costly, and error-prone. The use of CNN in this study was innovatively solved with dimensionality reduction, addition of height information as color, and consequent application of a 2D CNN-based approach. We found there to be a wide gap in the use of ML on 3D information. This gap will have to be addressed, especially for more complex plant feature extractions, which we intend to implement through further research.

Abstract The fate of doubled genes, from allopolyploid or autopolyploid origin, is controlled at multiple levels, resulting in the modern day cultivars. We studied the root growth of 3 different triploid banana cultivars under control and osmotic stress conditions. The root growth of the allopolyploid ABB cultivar was 42% higher under control and 61% higher under osmotic stress. By integrating transcriptomics and proteomics, we studied the gene expression of all 3 cultivars, resulting in 2,749 identified root proteins. 383 gene loci displayed genotype specific differential expression whereof 252 showed at least one Single Amino Acid Polymorphism (SAAP). In the ABB cultivar, allele expressions supposedly follow a 1/3 and 2/3 pattern for respectively the A and the B allele. Using transcriptome read alignment to assess the homeoallelic contribution we found that 63% of the allele specific genes deviated from this expectation. 32 gene loci even did not express the A allele. The identified ABB allele- specific proteins correlate well with the observed growth phenotype as they are enriched in energy related functions such as ATP metabolic processes, nicotinamide nucleotide metabolic processes, and glycolysis.

Water deficit is one of the world’s major constraints in agriculture and will aggravate in the future. Banana (Musa spp.) is an important crop that needs vast amounts of water for optimal production. The International Transit Center of Bioversity International holds the world’s biggest collection of banana biodiversity (>1,500 accessions). The long-term aim of this research is to evaluate the potential within this collection for climate smart agricultural usage. Therefore, we developed a phenotyping setup under controlled environmental conditions and we selected 32 representatives of the Musa biodiversity (29 cultivars and 3 wild relatives) for evaluation. The best performing genotypes accumulated six to seven times more biomass than the least performing. Eight genotypes (five ABB, one AAB, and two AAA) invest under osmotic stress significantly more in root growth than in leaf growth. We predict therefore that these genotypes have potential for high productivity under rain fed conditions with a short dry season. To gain more insight in the transpiration physiology, we gravimetrically monitored individual plant transpiration over the diurnal period. All analyzed genotypes showed a marked reduction in transpiration rate in the afternoon. Moreover, the timing of this onset, as well as its impact on total transpiration, was genotype dependent. This phenomenon was more pronounced in 13 genotypes (eight ABB, two AAB, two AA, one BB). Banana is a crop originating from the humid tropics and has developed a strong root pressure to maintain an efficient water and nutrient transport even under saturated relative humidity conditions. Therefore, we hypothesize that the diurnal transpiration decline contributes to a higher water use efficiency without compromising the nutrient transport. Of the eight genotypes that had the best growth under osmotic stress, all analyzed ABB cultivars have a lower maximal transpiration rate, keep this maximal transpiration for a shorter time and therefore consume less water per day. We conclude that lab models are very useful to study the biodiversity and to identify different traits that contribute to a better drought tolerance/avoidance. We encourage researchers investigating other crops to start exploring their collections.