Smarticulture: Novel Farming Technique to Achieve Automation in Farming and Higher Crop Yield with Aid of Artificial Intelligence
Bibliographic record
Abstract
Abstract— Smart agriculture is a new concept, Because IoT sensors can provide information about agriculture fields and then act on it based on user input. This paper proposes a smart agriculture system which include of drone seeding, fertilisers spraying, soil sampling, farm condition monitoring includes temperature, level of water, moisture monitoring and even any movement happens in the field which may ruin the agricultural field ,through the application of IoT drones and sensors for farms and also crop rotation farms. Drone will collect farm data such as pH value, soil humidity from a ground-based sensor that will be pre-plugged in the farm by the farmer. Because of its capacity to connect over a long distance, Zigbee is used to connect the ground sensor and the drone. The acquired data will subsequently be transmitted through LoRa to a gateway, where it will be stored for data storage and analysis in the cloud. We believe that the proposed application will make farming operations more automated and increase crop output rates. Keywords— IoT, Agriculture, Automation, Zigbee, LoRa, Smart Agriculture, Blockchain, Data Management.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".