Research on Tomato Quality Prediction Models Based on the Coupling of Environmental Factors and Appearance Phenotypes.

Longwei Liang, Zhaoyuan Wang, Kaige Liu, Jing Xu, Changhong Li, Huiying Liu, Ming Diao

Abstract read

In one paragraph

Article in Plants (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Longwei LiangCollege of Agriculture, Shihezi University, Shihezi 832003, China.ORCID 0009-0007-0098-6235

Zhaoyuan WangCollege of Agriculture, Shihezi University, Shihezi 832003, China.ORCID 0009-0006-5903-5250

Kaige LiuCollege of Agriculture, Shihezi University, Shihezi 832003, China.ORCID 0000-0002-0863-9533

Jing XuCollege of Agriculture, Shihezi University, Shihezi 832003, China.

Changhong LiCollege of Agriculture, Shihezi University, Shihezi 832003, China.

Huiying LiuCollege of Agriculture, Shihezi University, Shihezi 832003, China.

Ming DiaoCollege of Agriculture, Shihezi University, Shihezi 832003, China.ORCID 0009-0002-1926-0315

Funding

the Earmarked Fund for the Xinjiang Agricultural Research System XJARS-07the Seventh Division - Shihezi University Science and Technology Innovation Special Project QS2023012

6 · The paper itself

Abstract

This study addresses the limitations of current non-destructive techniques for assessing tomato quality, such as their high cost, strong dependence on spectroscopic instruments, and difficulty in dynamic monitoring. The study proposes an integrated tomato quality prediction model that combines a Long Short-Term Memory (LSTM)-based environmental predictor, a Gated Recurrent Unit with attention mechanism (GRU-AT) for dynamic maturity prediction, and a Deep Neural Network (DNN)-based quality evaluation module. The LSTM model demonstrated high accuracy in environmental prediction (R

Indexed as

appearance phenotypesdeep neural networkenvironmental factorsgated recurrent unitlong short-term memorytomato quality prediction

Identifiers

PMID41375280

DOI10.3390/plants14233569

PMCPMC12693907

What Socratic holds

Textmetadata

LicenceCC BY

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 Socratic graph.