Home › Entities › academia › Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in `Spania' watermelons using vis/NIR hyperspectral imaging

Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in `Spania' watermelons using vis/NIR hyperspectral imaging

Research article (Postharvest Biology and Technology, 2024) · cited 13× · AI/ML
academia openalex:W4402830979
Press Enter · cited answer in seconds

Development of an unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in `Spania' watermelons using vis/NIR hyperspectral imaging

Summary

Development of an unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in Spania' watermelons using vis/NIR hyperspectral imaging is a scholarly article<sup id="cite-A2" class="cite-ref" title="Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC inSpania' watermelons using vis/NIR hyperspectral imaging is an in">[1].

Key Facts

  • Development of an unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in Spania' watermelons using vis/NIR hyperspectral imaging's instance of is recorded as scholarly article<sup id="cite-C1" class="cite-ref" title="Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC inSpania' watermelons using vis/NIR hyperspectral imaging — instan">[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in `Spania' watermelons using vis/NIR hyperspectral imaging. Retrieved May 24, 2026, from https://4ort.xyz/entity/development-of-a-unified-framework-of-low-rank-approximation-and-deep-neural-networks-for-predicting-the-spatial-variabi
MLA “Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in `Spania' watermelons using vis/NIR hyperspectral imaging.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/development-of-a-unified-framework-of-low-rank-approximation-and-deep-neural-networks-for-predicting-the-spatial-variabi.
BibTeX @misc{4ortxyz_development-of-a-unified-framework-of-low-rank-approximation-and-deep-neural-networks-for-predicting-the-spatial-variabi_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in `Spania' watermelons using vis/NIR hyperspectral imaging}}, year = {2026}, url = {https://4ort.xyz/entity/development-of-a-unified-framework-of-low-rank-approximation-and-deep-neural-networks-for-predicting-the-spatial-variabi}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Development of a unified framework of low-rank approximation and deep neural networks for predicting the spatial variability of SSC in `Spania' watermelons using vis/NIR hyperspectral imaging — https://4ort.xyz/entity/development-of-a-unified-framework-of-low-rank-approximation-and-deep-neural-networks-for-predicting-the-spatial-variabi (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/development-of-a-unified-framework-of-low-rank-approximation-and-deep-neural-networks-for-predicting-the-spatial-variabi · Last refreshed: