Handheld hyperspectral imaging dataset of annual sowthistle and little mallow under abiotic stress for machine learning.

Elijah Brunnengraeber, Tyler Cassidy, Dylan Woodbridge, Arun D Jani, Arun K Sharma

Abstract read

In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges.Foods (Basel, Switzerland) · 2026
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Elijah BrunnengraeberDepartment of Biology, Agriculture, and Chemistry, California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, USA.

Tyler CassidyDepartment of Biology, Agriculture, and Chemistry, California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, USA.

Dylan WoodbridgeDepartment of Biology, Agriculture, and Chemistry, California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, USA.

Arun D JaniDepartment of Biology, Agriculture, and Chemistry, California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, USA.

Arun K SharmaDepartment of Biology, Agriculture, and Chemistry, California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning has become an increasingly important tool for overcoming agricultural challenges by enabling efficient and consistent classification of crop-related data. Training such supervised models requires high quality labeled datasets. This work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species on California's Central Coast: annual sowthistle (

Indexed as

Abiotic stressDatasetHyperspectral imagingPlant healthPlant identificationWeeds

Identifiers

PMID42239812

DOI10.1016/j.dib.2026.112858

PMCPMC13226935

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.