Explainable AI-driven interpretation of environmental drivers of tomato fruit expansion in smart greenhouses using IoT sensing.

Yunlong Li, Tianle Wang, Haoyuan Zu, Shufeng Liu, Linlu Zu

Abstract read

In one paragraph

Article in Scientific reports, 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

5 authors.

Yunlong LiIntelligent Manufacturing College, Tianjin Sino-German University of Applied Sciences, Tianjin, 300350, China.

Tianle WangCollege of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China.

Haoyuan ZuCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018, China.

Shufeng LiuCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018, China.

Linlu ZuCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018, China. zulinlu@sdau.edu.cn.

Funding

The Science and Technology smes innovation ability improvement project of Shandong Province 2022TSGC2047

6 · The paper itself

Abstract

Tomato fruit expansion is a key physiological process that determines fruit size, marketability, and yield, yet its quantitative and threshold-based response to microclimatic factors in smart greenhouses has been insufficiently studied. This study develops an IoT-driven sensing framework combined with explainable artificial intelligence (XAI) to interpret the environmental drivers of fruit expansion. A robust environmental monitoring system continuously captured key factors including air and soil temperature, humidity, light intensity, CO

Indexed as

Artificial IntelligenceEnvironmental MonitoringFruitSolanum lycopersicumAgricultureInternet of ThingsMachine LearningSoilTemperatureSoilExplainable AIIoT sensingSHAP analysisSustainabilityTomato growth

Identifiers

PMID41258186

DOI10.1038/s41598-025-24800-3

PMCPMC12630824

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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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.