Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function

Gyamerah, Samuel Asante; Ngare, Philip; Ikpe, Dennis

doi:10.1016/j.agrformet.2019.107808

Cited by 41 publications

(21 citation statements)

References 25 publications

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“…In contrast, QRF considers the response variable's spread of values at each node and infer estimates for conditional quantiles, prediction intervals, or other statistics from the distribution (Dobarco et al,2019;Meinshausen, 2006;Vaysse and Lagacherie, 2017). If there are extreme values in the samples applying the sample mean in the leaf node may result in biasness (Gyamerah et al, 2020), therefore the median value was used for point prediction in the QRF model to enhance the accuracy of the prediction.…”

Section: Mapping Model: Quantile Regression Forestmentioning

confidence: 99%

Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches

Zare

Abtahi²,

Shamsi³

et al. 2021

CATENA

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Mapping Model: Quantile Regression Forestmentioning

confidence: 99%

Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches

Zare

Abtahi²,

Shamsi³

et al. 2021

CATENA

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“…The random forest (RF) model has been applied successfully to predict agricultural information, for example, soil temperature [41], soil moisture content [42,43], and crop yield [44,45]. Usually, a RF model consists of numerous decision trees.…”

Section: Introductionmentioning

confidence: 99%

Simulating Canopy Temperature Using a Random Forest Model to Calculate the Crop Water Stress Index of Chinese Brassica

et al. 2021

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The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

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“…Sakamoto [30] estimated the spatial distribution of United States corn and soybeans yield effectively through the use of the RFR model. Gyamerah et al [31] forecasted groundnut and millet yield based on quantile random forest and Epanechnikov kernel function successfully. Nevertheless, there is a gap in knowledge of whether the application of the RFR algorithm in sugarcane AFW prediction is also feasible.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Tang

et al. 2020

Remote Sensing

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Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R2 = 0.96, RMSE = 1.27 kg m−2) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R2 = 0.97, RMSE = 1.33 kg m−2). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production.

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Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function

Cited by 41 publications

References 25 publications

Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches

Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches

Simulating Canopy Temperature Using a Random Forest Model to Calculate the Crop Water Stress Index of Chinese Brassica

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

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