Sistem Pemantauan Kesuburan Tanaman Pohon Durian menggunakan Internet Of Things (IOT)
Bibliographic record
Abstract
Durian is one of the leading fruit commodities with high economic value in various tropical regions, including Indonesia. However, the durian cultivation process often faces challenges related to unstable environmental conditions, such as temperature fluctuations, soil moisture, and nutritional deficiencies which can affect the level of plant fertility and the quality of the fruit produced. Therefore, an effective and efficient monitoring system is needed to optimize durian plant care. This research aims to develop a fertility monitoring system for durian trees using Internet of Things (IoT) technology which can help farmers manage the environmental conditions of plants in real-time. The designed system uses various sensors, such as soil moisture sensors, air temperature and humidity sensors, light intensity sensors, and soil nutrient sensors, to collect relevant environmental data. The data obtained from these sensors is then processed by a microcontroller and sent via the IoT network to a cloud-based storage platform. The trial results show that this system can monitor environmental conditions with high accuracy and provide appropriate maintenance recommendations, thereby increasing efficiency in managing durian plants.
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 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".