AIMC Topic: Glycine max

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On construction of data preprocessing for real-life SoyLeaf dataset & disease identification using Deep Learning Models.

Computational biology and chemistry
The vast volumes of data are needed to train Deep Learning Models from scratch to identify illnesses in soybean leaves. However, there is still a lack of sufficient high-quality samples. To overcome this problem, we have developed the real-life SoyLe...

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.

Briefings in bioinformatics
Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. A...

Use of in vitro dry matter digestibility and gas production to predict apparent total tract digestibility of total dietary fiber for growing pigs.

Journal of animal science
In vitro DM disappearance (IVDMD) and gas production methods have been developed and used to measure in vivo nutrient digestibility of feed ingredients, but further validation is needed for ingredients containing high concentrations of insoluble fibe...