Agrobot: Agricultural Robot using IoT and Machine Learning (ML)
Bibliographic record
Abstract
In today's world agriculture plays a very important role in the manufacturing of textiles, clothes, production of surplus number of crops that is food, which is essential for the everyday livelihood of mankind. On the other hand, there are a lot of challenges in the agricultural industries such as unpredictable natural disasters such as droughts, famines, floods etc., which can incur huge loss for the agriculture industries as well as the countries which have an agrarian society not only will it be affected by natural disasters but can also be affected by the diseases, which have the potential to destroy crops. The primary objective is to assist farmers and agriculture industries to thrive so that there can be less occurrence of food shortages by efficiently increasing the production of crops tenfold, analyzes the soil fertility for better plant growth, helps prevent plant epidemic by analyzing which fertilizer is better suitable for the protection of the plant to analyze the weather and the plants that is suitable to grow in the particular weather condition and predict the occurrence of natural disasters such as droughts and floods for early prevention from the crops by using the ATMEGA controller.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".