Effect of Irrigation Dose Based on Vegetation Indices Calculation Using Multispectral Sensor
Bibliographic record
Abstract
The agricultural sector is vital for the Greek economy, the Greek producers and consumers. The new era in agriculture leads scientists to find new more effective ways to estimate plant growth and biomass production and to increase water use efficiency. Given the above, a research project was organized at the Laboratory of Agricultural Hydraulics, University of Thessaly. For the first time in the climatic conditions of Central Greece, the project aims to assess another method to estimate the growth and biomass production of corn, based on multispectral photos and vegetation indices using a low cost multispectral camera and drone. Three surface drip irrigation treatments in three replications were organized and a randomized complete block design was used. The crop water needs were calculated according to the daily evapotranspiration using the Penman-Monteith procedure as it was presented by the Food Agricultural Organisation. The amount of water in each treatment was equal to A) 100% of the daily evapotranspiration (ETo), B) 75% of the ETo, and C) 50% of the ETo. Vegetation indices were calculated based on multispectral photos taken from a drone and the Simple and Multiple Regression analysis were used to estimate the maize growth and biomass production in Greek conditions. Different equations were formed to estimate the maize growth and biomass production and vegetation indices were used as independent variables. The results showed that vegetation indices can be used in the agriculture process to estimate maize growth and biomass production. In this paper the results of two consecutive years, 2018 and 2019 are presented.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".