Evidence map›Paper›PMID 41019612›Full record

ArticleCurrent research in food science2025

Explainable AI and mobile imaging for non-destructive avocado ripeness and internal quality assessment to reduce food waste.

In-Hwan Lee, Zhengao Li, Luyao Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current research in food science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

In-Hwan LeeDepartment of Food Science and Technology, College of Agricultural Sciences, Oregon State University, Corvallis, OR, 97331, USA.
Zhengao LiDepartment of Computer Science, College of Arts and Sciences, Florida State University, Tallahassee, FL, 32306, USA.
Luyao MaDepartment of Food Science and Technology, College of Agricultural Sciences, Oregon State University, Corvallis, OR, 97331, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food waste is a global challenge, primarily driven by inaccurate prediction of food quality and remaining shelf life. Approximately 30% of food waste occurs at retail and household levels, where traditional assessment methods such as subjective visual inspection or destructive instrumental analysis are inefficient. In this study, we integrated smartphone imaging and deep learning to non-destructively predict avocado firmness and internal quality, aiming to support smarter consumption and distribution decisions. Avocados were selected as a representative model food due to their high market value and high waste rate (∼40%). Over an eight-day storage period at room temperature, a dataset of 1400 avocado images was collected using a smartphone. Firmness was measured using a texture analyzer and served as the ripeness metric and ground truth for model training. To predict ripeness, a convolutional neural network residual regression (CNN ResNetR) model achieved the highest accuracy (R

Indexed as

Explainable artificial intelligenceFood qualityMachine learningShelf lifeSmartphone imaging

Identifiers

PMID41019612
PMCPMC12476098

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.