Ensemble Machine Learning Methods to Estimate the Sugarcane Yield Based on Remote Sensing Information
Bibliographic record
Abstract
The purpose of this study is to investigate the computing capabilities of machine learning algorithms and remotely sensed signals to extract the agricultural information.Many techniques and models have been developed to extract information from the remotely sensed observations, but it remains an exigent problem due to the accuracy, reliability and timeliness parameters.Sugarcane yield estimation based on the temporal profile of multispectral Landsat-8 data has been explored in the proposed work.An initial attempt has been made in this study to select important parameters to be used as input to the machine learning method.Mean Decrease Accuracy and Mean Decrease Gini measures of random forest algorithm have been used to select the important parameters for predictive modelling.The results of the study revealed that Green Normalized Vegetation Index, Normalized Difference Vegetation Index and Land Surface Water Index performed best among other indices.Bands B2, B3, B6 and B7 of Landsat-8 recorded as top scorers.The proposed work focused on ensemble machine learning methods to optimize the correlation of historical crop yield values with spectral information.The Random Forest method exhibits a significant performance (RMSE= 1.51 t/ha and R 2 = 0.94) as compared with other methods such as Classification and Regression Tree, Support Vector Regression and K-Nearest Neighbor.The proposed model based on random forest algorithm is best among all the scenarios and growth stages, whereas model based on classification and regression tree performs worst in all the cases.The proposed study indicates that the numerical value of a single spectral parameter and single-date data is not sufficient for the reliable yield estimation because it is difficult to discriminate some of the crops due to similar phenology in a particular growth period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".