INVESTIGATION ON THE IMPACT OF IoT AND MACHINE LEARNING IN URBAN PLANNING AND STRATEGIES FOR SUSTAINABLE COMMUNITY DEVELOPMENT
Bibliographic record
Abstract
Abstract The present study is intended to model rice field to optimize the water management practices using Artificial Intelligence techniques to predict more Realtime effective rain and irrigation water requirements for rice. Results of the study indicate that the merit of predictive analytics for improving irrigation efficiency, and also combine predictions of irrigation demand using Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) for various ML models. These models use data coming from temperature, humidity, and water level sensor that are deployed in paddy agriculture fields to forecast the crop irrigation needs. Taking all the factors into account, the results indicate that both the ANN and LSTM models can predict accurately with high percentage of precision, recall and F1 scores for water needs. In addition to this property, the ML-based systems are highly scalable and flexible in nature which allows it to be widely used in various crops and regions of farming. However, this research certainly underscores the need for data-driven advances to tackle some of the global challenges in agriculture around climate change and water shortage. Using the technology, we can interpret data that will deliver actionable insights around water conservation to increase efficiency and sustainability for farmers. It will be important to build on these systems, over time, with ongoing research and innovation, to continue advancing these systems and to rapidly respond to new threats, while also to increase the utility of digital approaches to food security informatics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".