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Record W4412566594 · doi:10.1002/aisy.70071

Artificial Intelligence‐Driven Robotic Sensing System for Noninvasive Crop Health Monitoring and Autonomous Irrigation Management

2025· article· en· W4412566594 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Intelligent Systems · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSmart Agriculture and AI
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsArtificial intelligenceComputer scienceCrop managementIrrigation managementAgricultural engineeringIrrigationSystems engineeringCropEnvironmental scienceEngineeringAgronomyBiology

Abstract

fetched live from OpenAlex

Artificial Intelligence-Driven Robotic Sensing System Woo Soo Kim’s research team at Simon Fraser University has developed an AI-powered, non-invasive sensing robot that directly monitors plant health and water needs from plant leaves using electrical signals from 3D-printed plant sensors. This innovative technology optimizes resource use, enhances crop yields, and advances sustainable indoor farming, paving the way for smart, resource-efficient agriculture globally. More details can be found in article number 2500198.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.435

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)0.0000.000

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 head0.032
GPT teacher head0.275
Teacher spread0.244 · 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