AIMC Topic: Plant Breeding

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Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data.

Journal of plant physiology
Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the n...

Morphological traits of drought tolerant horse gram germplasm: classification through machine learning.

Journal of the science of food and agriculture
BACKGROUND: Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) is an underutilized pulse crop with good drought resistance traits. It is a rich source of protein. Conventional breeding methods for high yielding and abiotic stress tolerant germplasm are...

New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.

G3 (Bethesda, Md.)
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes...

Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Heredity
Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available fo...

A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.

G3 (Bethesda, Md.)
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic ba...

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.

GigaScience
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent a...

DeepSort: deep convolutional networks for sorting haploid maize seeds.

BMC bioinformatics
BACKGROUND: Maize is a leading crop in the modern agricultural industry that accounts for more than 40% grain production worldwide. THe double haploid technique that uses fewer breeding generations for generating a maize line has accelerated the pace...

A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Key message New fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper presents a new heuristic method for phasing and imputation of genomic data in diploid plant species. Ou...

Image set for deep learning: field images of maize annotated with disease symptoms.

BMC research notes
OBJECTIVES: Automated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers' fields. However, it is difficult for a simple algorithm to distinguish between the target disease and...

Cotton genotypes selection through artificial neural networks.

Genetics and molecular research : GMR
Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explore...