Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

BACKGROUND: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. RESULTS: This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2 ), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). CONCLUSION: According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

AraDiv: a dataset of functional traits and leaf hyperspectral reflectance of Arabidopsis thaliana.

Data from functional trait databases have been increasingly used to address questions related to plant diversity and trait-environment relationships. However, such databases provide intraspecific data that combine individual records obtained from distinct populations at different sites and, hence, environmental conditions. This prevents distinguishing sources of variation (e.g., genetic-based variation vs. phenotypic plasticity), a necessary condition to test for adaptive processes and other determinants of plant phenotypic diversity. Consequently, individual traits measured under common growing conditions and encompassing within-species variation across the occupied geographic range have the potential to leverage trait databases with valuable data for functional and evolutionary ecology. Here, we recorded 16 functional traits and leaf hyperspectral reflectance (NIRS) data for 721 widely distributed Arabidopsis thaliana natural accessions grown in a common garden experiment. These data records, together with meteorological variables obtained during the experiment, were assembled to create the AraDiv dataset. AraDiv is a comprehensive dataset of A. thaliana's intraspecific variability that can be explored to address questions at the interface of genetics and ecology.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

Into the range: a latitudinal gradient or a center-margins differentiation of ecological strategies in Arabidopsis thaliana?

BACKGROUND AND AIMS: Determining within-species large-scale variation in phenotypic traits is central to elucidate the drivers of species' ranges. Intraspecific comparisons offer the opportunity to understand how trade-offs and biogeographical history constrain adaptation to contrasted environmental conditions. Here we test whether functional traits, ecological strategies from the CSR scheme and phenotypic plasticity in response to abiotic stress vary along a latitudinal or a center- margins gradient within the native range of Arabidopsis thaliana. METHODS: We experimentally examined the phenotypic outcomes of plant adaptation at the center and margins of its geographic range using 30 accessions from southern, central and northern Europe. We characterized the variation of traits related to stress tolerance, resource use, colonization ability, CSR strategy scores, survival and fecundity in response to high temperature (34 °C) or frost (- 6 °C), combined with a water deficit treatment. KEY RESULTS: We found evidence for both a latitudinal and a center-margins differentiation for the traits under scrutiny. Age at maturity, leaf dry matter content, specific leaf area and leaf nitrogen content varied along a latitudinal gradient. Northern accessions presented a greater survival to stress than central and southern accessions. Leaf area, C-scores, R-scores and fruit number followed a center-margins differentiation. Central accessions displayed a higher phenotypic plasticity than northern and southern accessions for most studied traits. CONCLUSIONS: Traits related to an acquisitive/conservative resource-use trade-off followed a latitudinal gradient. Traits associated with a competition/colonization trade-off differentiated along the historic colonization of the distribution range and then followed a center-margins differentiation. Our findings pinpoint the need to consider the joint effect of evolutionary history and environmental factors when examining phenotypic variation across the distribution range of a species.

GECKO is a genetic algorithm to classify and explore high throughput sequencing data.

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data.

Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping.

BACKGROUND AND AIMS: Plant modelling can efficiently support ideotype conception, particularly in multi-criteria selection contexts. This is the case for biomass sorghum, implying the need to consider traits related to biomass production and quality. This study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction. METHODS: Three Ecomeristem model versions were compared to evaluate whether tillering cessation and mortality were source (access to light) or sink (age-based hierarchical access to C supply) driven. They were tested using a field data set considering two biomass sorghum genotypes at two planting densities. An additional data set comparing eight genotypes was used to validate the best approach for its ability to predict the genotypic and environmental control of biomass production. A sensitivity analysis was performed to explore the impact of key genotypic parameters and define optimal parameter combinations depending on planting density and targeted production (sugar and fibre). KEY RESULTS: The sink-driven control of tillering cessation and mortality was the most accurate, and represented the phenotypic variability of studied sorghum genotypes in terms of biomass production and partitioning between structural and non-structural carbohydrates. Model sensitivity analysis revealed that light conversion efficiency and stem diameter are key traits to target for improving sorghum biomass within existing genetic diversity. Tillering contribution to biomass production appeared highly genotype and environment dependent, making it a challenging trait for designing ideotypes. CONCLUSIONS: By modelling tiller growth and mortality as sink-driven processes, Ecomeristem could predict and explore the genotypic and environmental variability of biomass sorghum production. Its application to larger sorghum genetic diversity considering water deficit regulations and its coupling to a genetic model will make it a powerful tool to assist ideotyping for current and future climatic scenario.