Predictive Modelling of Farms and Crop Growth through Satellite and Remote Sensing Data Processing using Ensemble Learning Algorithms
Bibliographic record
Abstract
The use of advanced sensing technologies, machine learning, and intelligent task scheduling is crucial for precision agriculture, aiming to enhance decision-making, predict crop yields, and support sustainability. This study introduces the Predictive Modelling of Farms and Crop Growth through Satellite and Remote Sensing Data Processing using the Ensemble Learning Algorithms (PFCSRE) model, which integrates machine learning to analyze leaf nitrogen content, predict chlorophyll levels, and utilize satellite imagery for improved farming outcomes. The model employs Quantum-Driven Mongoose Optimization (QDMO) for tuning, Random Forest (RF) regression, and a virtual machine (VM)-based scheduling method, ensuring real-time, scalable, and reliable performance in smart agriculture. The study covers three large carrot fields in Saudi Arabia over two growing seasons, tracking over 90 sample plots with SPAD meters and GPS-tagged data collection on soil and crops. The Growing Degree Days (GDD) model was used to track growth stages, and the Leaf Chlorophyll Content (LCC) was calculated by combining satellite vegetation indices with SPAD data. The LCC layers helped create productivity zones for predicting crop yields. The PFCSRE model, with training and validation accuracy exceeding 98%, and confirmed classification reliability through ROC curve and AUC analysis, outperforms other baseline models. The regression analysis indicated that mid-growth phases significantly influenced carrot yields, while late-stage chlorophyll content negatively affected output. The PFCSRE model, adaptable to various crops, aids sustainable farming by optimizing resource use. The intelligent scheduling model improved efficiency in cloud-based computation, offering better predictive accuracy and data integration compared to traditional models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".