Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Correa, Agenor Martinho; Teodoro, Paulo Eduardo; Gonçalves, Manoel Carlos; Barroso, Laís Mayara Azevedo; Nascimento, Moysés; Santos, Adriano dos; Torres, Francisco Eduardo

doi:10.4238/gmr.15028230

Cited by 10 publications

(8 citation statements)

References 19 publications

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“…As for the phenotypic stability, there was similarity of 81.82% in the classification of genotypes, probably because in the ANN the stability is based on the concept of Finlay and Wilkinson (1963), which differs from the Eberhart and Russel (1966) methodology, which considers stability as invariance and no predictability. Similar results were obtained by Nascimento et al (2013), Teodoro et al (2015) and Correa et al (2016), who verified agreement over 80% between Eberhart and Russell (1966) and ANNs for the adaptability and phenotypic stability of alfalfa, cowpea and common bean genotypes, respectively. Due to the high concordance rates between the evaluated methodologies, ANNs can be considered an effective alternative to measure the adaptability and phenotypic stability of genotypes in breeding programs.…”

Section: Practical Classificationsupporting

confidence: 85%

Artificial neural networks classify cotton genotypes for fiber length

Carvalho¹,

Teodoro

Barroso

et al. 2018

Crop Breed. Appl. Biotechnol.

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Fiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater length fibers. The aim of this study was to evaluate the adaptability and stability of the fibers length of cotton genotypes for recommendation to the Midwest and Northeast, using artificial neural networks (ANNs) and Eberhart and Russell method. Seven trials were carried out in the states of Ceará, Rio Grande do Norte, Goiás and Mato Grosso do Sul. Experimental design was a randomized block with four replications. Data were submitted to analysis of adaptability and stability through the Eberhart & Russell and ANNs methodologies. Based on these methods, the genotypes BRS Aroeira, CNPA CNPA 2009 42 and CNPA 2009 27 has better performance in unfavorable, general and favorable environment, respectively, for having fiber length above the overall mean of environments and high phenotypic stability.

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Section: Practical Classificationsupporting

confidence: 85%

Artificial neural networks classify cotton genotypes for fiber length

Carvalho¹,

Teodoro

Barroso

et al. 2018

Crop Breed. Appl. Biotechnol.

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“…The deep learning framework was based on convolutional neural networks (CNNs), which were used to predict the quantitative traits from SNPs and achieved more accurate results. Similarly, artificial neural networks (ANNs) have been employed for GS-based prediction modeling in common bean [ 235 ]. The genotype Aporé, which was studied using ANNs, was recommended for use in unfavorable environments because of its grain yield and high phenotypic stability even under unfavorable conditions.…”

Section: Smart Farming: Artificial Intelligence (Ai)-based Pulse Breeding For Climate Resiliencementioning

confidence: 99%

Fab Advances in Fabaceae for Abiotic Stress Resilience: From ‘Omics’ to Artificial Intelligence

Singh

Chaudhary

Singh

et al. 2021

IJMS

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Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. ‘Omics’-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel ‘omics’ approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics—which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation—has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

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“…The ANNs have been used for plant breeding, including use in the investigation of genotype x environment interactions. Correa et al (2016) have developed an ANN method as an effective alternative to measure the adaptability and phenotypic stability of genotypes in breeding programs in Common bean (Phaseolus vulgaris L.). In this regard, the simulated genotypes are used to train and validate neural networks.…”

Section: Crop Management Systemsmentioning

confidence: 99%

Role of artificial intelligence in achieving global food security: a promising technology for future

et al. 2020

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Rapid growth of population, diminishing natural resources, climate change, shrinking agricultural lands and unstable markets are making the global food systems rather insecure. Therefore, modern agriculture and food systems should be more productive in terms of output, efficient in operation, resilient to climate change and sustainable for the future generations. As a result, the need of a technological transformation is greater than ever before. Being a recent advancement in computer sciences, Artificial Intelligence (AI) has the capacity to address the challenges of this new paradigm. Hence, understanding the importance and applicability of AI in agriculture and food sector could be vital in the journey towards achieving global food security. This review focuses on the AI applications in relation to four pillars of food security (food availability, food accessibility, food utilization and stability) as defined by FAO, in detail. The AI technologies are being applied worldwide in all four pillars of food security even though it has been one of the slower adopted technologies compared to the rest. Nevertheless, it warrants exploring the capabilities of AI and their current impact on the food systems. It is eminent that AI technology has a key role to play in the future agriculture sector. The worldwide AI in agriculture market is expected to reach USD 2,075 million by 2024. Present article reveals how AI technologies could benefit global agriculture and food sector, and examines the ways by which AI can address the prominent issues in Sri Lankan agriculture sector such as labor scarcity, misuse of agrochemicals and inefficient food value chains. Though there are still many challenges and gaps to be addressed at research, policy, administrative and farmer levels, the immense potential of this novel technology should be exploited fast in the journey towards global food security.

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Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Cited by 10 publications

References 19 publications

Artificial neural networks classify cotton genotypes for fiber length

Artificial neural networks classify cotton genotypes for fiber length

Fab Advances in Fabaceae for Abiotic Stress Resilience: From ‘Omics’ to Artificial Intelligence

Role of artificial intelligence in achieving global food security: a promising technology for future

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