Evidence map›Paper›PMID 42865998›Full record

ReviewFrontiers in plant science2026

Recent advancements on innovative technologies and applications for non-destructive monitoring of tomato quality.

Yirui Huang, Jiahao Gu, Mingyu Yang, Weifeng Zhang, Xuan Liu, Zhanmin Zhao, Xi Yang

Abstract readReview
In one paragraph

Review in Frontiers in plant science, 2026. 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.

Yirui HuangCollege of Information Engineering, Hebei Province Key Laboratory of Intelligent Sensing and Data Processing for Geoenvironment, Hebei Key Laboratory of Optoelectronic Information and Geo-detection Technology, Intelligent Sensor Network Engineering Research Center of Hebei Province, Shijiazhuang, China.
Jiahao GuMedical Business Department, Frist Affiliated Hospital of Hebei Medical University, Shijiazhuang, China.
Mingyu YangCollege of Information Engineering, Hebei Province Key Laboratory of Intelligent Sensing and Data Processing for Geoenvironment, Hebei Key Laboratory of Optoelectronic Information and Geo-detection Technology, Intelligent Sensor Network Engineering Research Center of Hebei Province, Shijiazhuang, China.
Weifeng ZhangCollege of Information Engineering, Hebei Province Key Laboratory of Intelligent Sensing and Data Processing for Geoenvironment, Hebei Key Laboratory of Optoelectronic Information and Geo-detection Technology, Intelligent Sensor Network Engineering Research Center of Hebei Province, Shijiazhuang, China.
Xuan LiuCollege of Future Information Technology, Shijiazhuang University, Shijiazhuang, China.
Zhanmin ZhaoCollege of Information Engineering, Hebei Province Key Laboratory of Intelligent Sensing and Data Processing for Geoenvironment, Hebei Key Laboratory of Optoelectronic Information and Geo-detection Technology, Intelligent Sensor Network Engineering Research Center of Hebei Province, Shijiazhuang, China.
Xi YangCollege of Information Engineering, Hebei Province Key Laboratory of Intelligent Sensing and Data Processing for Geoenvironment, Hebei Key Laboratory of Optoelectronic Information and Geo-detection Technology, Intelligent Sensor Network Engineering Research Center of Hebei Province, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tomato quality assessment involves multiple external and internal attributes. Conventional reference methods remain important for accurate characterization but are often destructive, time-consuming, or unsuitable for repeated and online measurements. This review critically examines recent advances in nondestructive sensing technologies for tomato quality evaluation and organizes them according to sensing mechanism. Electromagnetic and optical techniques are assessed for their ability to characterize surface, compositional, spectral, and physiological information, while mechanical and tactile sensing approaches are discussed in relation to firmnes. Electrical and emerging sensors further extend quality assessment toward bioelectrical properties, volatile profiles, and other physicochemical changes. Particular attention is given to information fusion and multimodal sensing, with emphasis on the distinction between homogeneous spectral fusion and heterogeneous integration of physically complementary modalities. Future research should therefore prioritize complementary sensor selection, independent cross-condition validation, calibration transfer, interpretable modeling, system miniaturization, and standardized implementation in sorting, storage, and postharvest monitoring. In general, the transition from isolated laboratory models to transferable and operational quality-monitoring systems remains a central challenge for nondestructive tomato sensing.

Indexed as

applicationsdata fusion (DF)multimodelnon-destructive techniquestomato quality

Identifiers

PMID42865998
PMCPMC13655698

What OpenQuestion holds

Textmetadata
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.