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Record W2975103748 · doi:10.18280/ria.330208

Apple Binocular Visual Identification and Positioning System

2019· article· en· W2975103748 on OpenAlexvenueno aff
Li Liu, Xin Qiao, Xindong Shi, Yong Wang, Yinggang Shi

Bibliographic record

VenueRevue d intelligence artificielle · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsnot available
FundersFundamental Research Funds for the Central Universities
KeywordsComputer visionArtificial intelligenceComputer sciencePreprocessorBinocular visionBinocular disparityIdentification (biology)StereopsisSubtractionMachine visionPositioning systemMathematics

Abstract

fetched live from OpenAlex

In order to realize the autonomous recognition and location of apple, an apple recognition and location system based on LabVIEW software, IMAQ Vision kit and binocular Vision was designed in the work. The system identified apples on the trees by background subtraction based on difference of surface color, object identification based on circle and binocular stereo measurement of the apples on trees. The results showed that the system has accomplished image acquisition, preprocessing, recognition and depth recovery, realizing the positioning of apple on LabVIEW. The system can be transplanted into the fully automatic picking system to pick apples more accurately and quickly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.225
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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