Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Societal Impact StatementPulse crops, including dry pea, lentil, and chickpea, are rich sources of protein, low digestible carbohydrates, and micronutrients. With the increasing demand for plant‐based protein with gluten‐free and allergen‐free foods, pulse crops have become of global importance for meeting the nutritional demand of growing populations. Breeding for nutritional quality is becoming a bottleneck for most breeding programs globally due to the cost of these available tools. Therefore, low‐cost, high‐throughput phenotyping tools will be a focus of interest for the selection of elite germplasm for cultivar development and gene identification for pulse cultivar development. This publication explains the emerging and future trends of phenotyping tools that are feasible for pulse breeding and improving nutritional quality.SummaryPrecision agriculture tools based on spectroscopic and imaging techniques now contribute to high‐throughput phenotyping (HTP) pipelines for nutritional and agronomic traits to speed breeding and selection for cultivar development. Fourier transform mid‐infrared (FT‐MIR) spectroscopy has been a reliable HTP tool for macro nutritional traits in pulse crops. Hyperspectral, multispectral, and RGB (red‐green‐blue) imaging with unmanned aerial systems (UAVs) have been developed to measure agronomic traits for cereals, but these techniques have yet to be developed and validated for pulse crops. This review summarizes different phenotyping techniques applied to nutritional and agronomic traits for crop breeding and reviews applications of machine learning tools for optimizing HTP.
Societal Impact StatementPulse crops, including dry pea, lentil, and chickpea, are rich sources of protein, low digestible carbohydrates, and micronutrients. With the increasing demand for plant‐based protein with gluten‐free and allergen‐free foods, pulse crops have become of global importance for meeting the nutritional demand of growing populations. Breeding for nutritional quality is becoming a bottleneck for most breeding programs globally due to the cost of these available tools. Therefore, low‐cost, high‐throughput phenotyping tools will be a focus of interest for the selection of elite germplasm for cultivar development and gene identification for pulse cultivar development. This publication explains the emerging and future trends of phenotyping tools that are feasible for pulse breeding and improving nutritional quality.SummaryPrecision agriculture tools based on spectroscopic and imaging techniques now contribute to high‐throughput phenotyping (HTP) pipelines for nutritional and agronomic traits to speed breeding and selection for cultivar development. Fourier transform mid‐infrared (FT‐MIR) spectroscopy has been a reliable HTP tool for macro nutritional traits in pulse crops. Hyperspectral, multispectral, and RGB (red‐green‐blue) imaging with unmanned aerial systems (UAVs) have been developed to measure agronomic traits for cereals, but these techniques have yet to be developed and validated for pulse crops. This review summarizes different phenotyping techniques applied to nutritional and agronomic traits for crop breeding and reviews applications of machine learning tools for optimizing HTP.
No abstract
Background Drought and salinity stress have been proposed as the main environmental factors threatening food security, as they adversely affect crops' agricultural productivity. As a potential solution, the application of plant growth regulators to enhance drought and salinity tolerance has gained considerable attention. γ-aminobutyric acid (GABA) is a four-carbon non-protein amino acid that accumulates in plants as a response to stressful conditions. This study focused on a comparative assessment of several machine learning (ML) regression models, including radial basis function, generalized regression neural network (GRNN), random forest (RF), and support vector regression (SVR) to develop predictive models for assessing the effect of different concentrations of GABA (0, 10, 20, and 40 mM) on various physio-biochemical traits during periods of drought, salinity, and combined stress conditions. The physio-biochemical traits included antioxidant enzyme activities (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT; and ascorbate peroxidase, APX), protein content, malondialdehyde (MDA) levels, and hydrogen peroxide (H2O2) levels. The non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the superior prediction model. Results The GRNN model outperformed the other ML algorithms and was therefore selected for optimization by NSGA-II. The GRNN-NSGA-II model revealed that treatment with GABA at concentrations of 20.90 mM and 20.54 mM, under combined drought and salinity stress conditions at 20.86 and 20.72 days post-treatment, respectively, could result in the maximum values for protein content (by 0.80 and 0.69), APX activity (by 50.63 and 51.51), SOD activity (by 0.54 and 0.53), POD activity (by 1.53 and 1.72), CAT activity (by 4.42 and 5.66), as well as lower MDA levels (by 0.12 and 0.15) and H2O2 levels (by 0.44 and 0.55), respectively, in the ‘Atabaki’ and ‘Rabab’ cultivars. Conclusions This study demonstrates that the GRNN-NSGA-II model, as an advanced ML algorithm with a strong predictive ability for outcomes in combined stressful environmental conditions, provides valuable insights into the significant factors influencing such multifactorial processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.