Impact of Polymer-Coated Urea Application Timing on Corn Yield in an IoT-based Smart Farming Application
Bibliographic record
Abstract
The population of the world is increasing exponentially each year with a large population base. Agricultural fields are facing the pressure of dealing with food insufficiency, whereas the challenges of limited resources of arable land and fresh water on the earth should be taken into account at the same time. Smart farming was born at the right time to cope with the problem and has become one of the most powerful approaches to reducing the ecological footprint of farming and improving agricultural yield. The four most important variables that impact crop yield are soil productivity, the accessibility of water, climate, and pests or diseases. This thesis emphasizes the application of chemical fertilizers to corn and disregards the impact of water, pests, and disease for the moment. In this study, three scenarios are explored deeper one by one. The only factor that varies among the three scenarios is the nitrogen amount available to the plant. Fertilizers have outstanding performance in improving the yield and quality of plants in agricultural fields, and this is the emphasis of this thesis. Compared with the fertilizer properties and characteristics of frequently used commercial fertilizers, polymer-coated urea was selected as the fertilizer in this study because the feature of nitrogen can be released into the soil slowly and in a controlled manner. Scenario 1 created an ideal condition where unlimited nitrogen was provided to the corn. Scenario 2 assumed that a fixed amount of polymer-coated urea was applied at the beginning of the sowing season only. Scenario 3 figured out an optimal yield by separating the fertilizer application at the beginning and in the middle of the growing days with the same amounts of fertilizer used in Scenario 2. The model was performed based on historical data from Oklahoma and Ottawa using IoT sensors. The simulation model generated with Python figured out that approximately the end of June to the start of July is the best time to apply the remaining fertilizer, assuming that the sowing stage starts on May 1. The percentage of polymer-coated urea applied initially was found to usually be around 10% in the tested regions. The model was used to predict the yield in Ottawa using from 40.94 g/(m^2) in Scenario 2 to 55.43 g/(m^2) in Scenario 3, achieving an outstanding increasing rate of 35.38%.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".