An intelligent plant growth monitoring system based on ESP32 and IoT resource explorer platform
Bibliographic record
Abstract
Plant growth status monitoring is an important link in the process of plant cultivation. The traditional monitoring methods mainly rely on human labor. This paper focuses on the use of soil moisture sensors and light sensors to detect the plant growth status, and studies the way that can view the plant growth information in real time through the mobile phone program to achieve the purpose of intelligent detection. Considering the characteristics of convenient network transmission and large-capacity multi-channel, ESP32 is used as the main board for development, and a multi-sensor fusion perception device based on ESP32 is designed, which integrates the functions of data detection, storage, and transmission. At the same time, based on the Tencent Cloud IoT resource explorer platform, a set of programs for receiving sensor information is developed with a completed the panel design. Therefore, the purpose of data exchange between the microcontroller and Tencent Cloud is achieved, which makes it possible to display various plant growth status information on the mobile phone in real time, saving a lot of labor costs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".