Quality Detection for Dragon Fruit Based on the End-of-Arm Spectral Sensor of the Harvesting Robot.

Zongxiu Bai, Qiu Xu, Kairan Lou, Bin Zhang

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 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

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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

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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

4 authors.

Zongxiu BaiSchool of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China.

Qiu XuSchool of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China.

Kairan LouSchool of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China.

Bin ZhangSchool of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China.

Funding

China Postdoctoral Science Foundation 2025M782462Hainan Province Postdoctoral Research Project Fundation 2025-104

6 · The paper itself

Abstract

Carrying out quality grading detection on the harvested dragon fruit is an important step in the dragon fruit industry. To reduce the high costs and damage rates caused by this process, an online spectral sensor and a weighing sensor embedded at the end effector of the dragon fruit-picking robot were designed to detect the sugar content, hardness and weight of the dragon fruits in real time during the picking process, thereby achieving the quality classification of the dragon fruits. After collecting the spectral data of dragon fruit, typical linear and nonlinear machine learning methods were used to establish prediction models for SSC-edge, SSC-center and hardness of dragon fruit. The results showed that PLSR models were selected as optimal models for prediction sugar content and hardness, and

Indexed as

detectiondragon fruitsharvesting robotmachine learningspectral sensor

Identifiers

PMID42279730

DOI10.3390/foods15111944

PMCPMC13256535

What OpenQuestion holds

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LicenceCC BY

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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.