Estimation of irrigated oats yield using spectral indices

Coelho, Anderson Prates; Faria, Rogério Teixeira de; Leal, Fábio Tiraboschi; Barbosa, José de Arruda; Dalri, Alexandre Barcellos; Rosalen, David Luciano

doi:10.1016/j.agwat.2019.105700

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Worldwide, researchers are making efforts to apply machine learning (ML) techniques in different databases to estimate crop yield. Most of these databases are composed of spectral band/vegetation indices from crop canopies [8,9], chlorophyll index [9], fusion of chemical and physical soil properties, historical weather conditions and historical crop yield [10]. For example, Syngenta Crop Challenge [11] supplied a database with 2267 experimental corn hybrids planted in different locations across Canada and the United States between 2008 and 2016.…”

Section: Introductionmentioning

confidence: 99%

Soybean Yield Estimation and Its Components: A Linear Regression Approach

Wei

Molin

2020

Agriculture

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Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components (number of grains (NG) and thousand grains weight (TGW)), there is an opportunity to study their relationships. The objective was to explore the relationships between soybean yield and its components, generate equations to estimate yield and evaluate its prediction accuracy. The training dataset was composed of soybean yield and its components’ data from 2010 to 2019. Linear regression models based on NG, TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on-field collected samples. It was found that globally TGW and NG presented weak (r = 0.50) and strong (r = 0.92) linear relationships with yield, respectively. In addition to that, applying the fitted models to the validation dataset, model based on NG presented the highest accuracy, coefficient of determination (R2) of 0.70, mean absolute error (MAE) of 639.99 kg ha−1 and root mean squared error (RMSE) of 726.67 kg ha−1.

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Section: Introductionmentioning

confidence: 99%

Soybean Yield Estimation and Its Components: A Linear Regression Approach

Wei

Molin

2020

Agriculture

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“…Also, other researchers have indicated that NDVI has a stronger correlation when estimating white oat grain yield in comparison to other VI's. 35,36…”

Section: Resultsmentioning

confidence: 99%

“…Also, other researchers have indicated that NDVI has a stronger correlation when estimating white oat grain yield in comparison to other VI's. 35,36 Evaluation of thematic yield maps by kernel density estimation Yield maps using estimated and actual oats grain yield were generated by the statistical pattern analysis method of KDE. The oats grain yield map variation was highlighted as follows: low (red = 0-4.97 t per ha), medium (orange = 4.97-6.18 t per ha), high (yellow = 6.18-7.11 t per ha) and very high (green > 7.62 t per ha) yield areas.…”

Section: Correlation Between Estimated and Actual Oats Grain Yieldmentioning

confidence: 99%