Artificial Intelligence‐Driven Robotic Sensing System for Noninvasive Crop Health Monitoring and Autonomous Irrigation Management
Bibliographic record
Abstract
This study introduces an artificial intelligence (AI)‐driven robotic system utilizing a 3D‐printed electrophysiological (EP) sensor for noninvasive, real‐time monitoring of plant health signals across different irrigation levels, highlighting the crucial role of these technologies in enhancing smart agriculture and sustainability. The sensing system consists of a mobile robot with a 3D EP sensor and portable Faraday cage for data acquisition, using an AI‐powered convolution neural network to analyze EP data in greenhouses and categorize irrigation levels to optimize water usage for scalable agricultural management. The findings reveal that the 3D EP sensor displays lower and more stable contact resistance (2.10 ± 0.52 MΩ) compared to flat thin‐film sensors (2.96 ± 1.45 MΩ), ensuring high electrical reliability due to effective contact with hairy tomato leaves. The 3D EP sensor's high sensitivity (signal resolution of 0.0122 mV) detects subtle EP signal changes linked to irrigation levels, aiding water optimization and crop yield enhancement. For the first time, this study employs scalogram images for detailed analysis of plant EP signals, achieving a classification accuracy of 86.91%, comparable to red, gren, and blue image‐based methods (86.37%). This system is a reliable tool for long‐term monitoring in smart farming and provides insights into plant signal dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".