Assessing accuracy of barley yield forecasting with integration of climate variables and support vector regression

Parviz, Laleh

doi:10.17951/c.2018.73.1.19-30

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…The model performance results from this study are similar to those from the literature [5,14,[52][53][54]: (a) the performance of the SVR-based model was better than the traditional linear model (i.e., linear regression model), and (b) the models must frequently be retrained to enhance the prediction performance when the range of the test dataset exceeds the training dataset, especially if the dataset is a data stream or time series. Therefore, the MBO/SVR model can be successfully and suitably applied to agricultural output prediction issues because it demonstrates the best performance, especially for predicting long-term agricultural output without retraining the prediction model.…”

Section: Discussionsupporting

confidence: 75%

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

et al. 2022

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Recently, annual agricultural data have been highly volatile as a result of climate change and national economic trends. Therefore, such data might not be enough to develop good agricultural policies for stabilizing agricultural output. A good agricultural output prediction model to assist agricultural policymaking has thus become essential. However, the highly volatile data would affect the prediction model’s performance. For this reason, this study proposes a marriage in honey bees optimization/support vector regression (MBO/SVR) model to minimize the effects of highly volatile data (outliers) and enhance prediction accuracy. We verified the performance of the MBO/SVR model by using the annual total agricultural output collected from the official Agricultural Statistics Yearbook of the Council of Agriculture, Taiwan. Taiwan’s annual total agricultural output integrates agricultural, livestock and poultry, fishery, and forest products. The results indicated that the MBO/SVR model had a lower mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), and relative root mean squared error (r-RMSE) than those of the models it was compared to. Furthermore, the MBO/SVR model predicted long-term agricultural output more accurately and achieved higher directional symmetry (DS) than the other models. Accordingly, the MBO/SVR model is a robust, high-prediction-accuracy model for predicting long-term agricultural output to assist agricultural policymaking.

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Section: Discussionsupporting

confidence: 75%

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

et al. 2022

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“…In our study the percentage deviation of estimated yield by observed yield done at tillering, flowering and grain filling stage by the SVR model was between 1.03 to 17.54 %. Parviz (2018) observed that the minimum correlation coefficient between the observed and simulated yield for barley was found in the Gilan province using a support vector machine. In our study LASSO modal performed well for yield prediction done at tillering, flowering and grain filling stages.…”

Section: Percentage Deviation Of Predicted Yield Done At Different St...mentioning

confidence: 98%

Multistage wheat yield prediction using hybrid machine learning techniques

et al. 2022

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Wheat being highly affected by the weather, adverse weather drastically reduces the wheat yield. Model was developed for multi stage wheat yield prediction by stepwise multi linear regression (SMLR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO) and hybrid machine learning LASSO-SVR and SMLR-SVR techniques. Wheat yield data and weather parameter for generating thermal and weather indices during different growth stage for more than 30 years were collected for study area. Analysis was carried out by fixing 70 % of the data for calibration and remaining 30 % dataset for validation in R software. Results showed that LASSO performed best having nRMSE value between 1.22 % at grain filling stage for IARI, New Delhi to 8.36 % for Hisar at flowering stage. The model performance of SVR is increased if a hybrid model in combination with LASSO and SMLR is applied. The hybrid model LASSO-SVR has shown more improvement than SVR model compared with SMLR-SVR.

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“…Parviz [23] used two methods, SVR and MLR, to predict barley yield in three Iranian provinces with arid, semi-arid, and humid climates. The results of this study showed that SVR is a better method for making a prediction for the considered 36-year period.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Sharifi [23] investigated prediction of barley yield in Iran, which is one of the rainfed crops that can be grown in the region, NDVI, EVI, temperature and evaporation indicators were considered. Although, other factors such as soil moisture and rainfall can be considered in the growth of rain-fed barley.…”

Section: Review Of Literaturementioning

confidence: 99%

Forecasting Rainfed Agricultural Production in Arid and Semi-Arid Lands Using Learning Machine Methods: A Case Study

et al. 2021

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With the rising demand for food products and the direct impact of climate change on food production in many parts of the world, recent years have seen growing interest in the subject of food security and the role of rainfed farming in this area. Machine learning methods can be used to predict crop yield based on a combination of remote sensing data and data collected by ground weather stations. This paper argues that forecasting drylands farming yield can be reliable for management purpose under uncertain conditions using machine learning methods and remote sensing data and determines which indicators are most important in predicting the yield of chickpea. In this study, the yield of rainfed chickpea farms in 11 top chickpea producing counties in Kermanshah province, Iran, was predicted using three machine learning methods, namely support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN). To improve prediction accuracy, for each county, remote sensing data were overlaid by the satellite images of rainfed farms with a suitable slope and altitude for rainfed farming. An integrated database was created by combining weather data, remote sensing data, and chickpea yield statistics. The methods were evaluated using the leave-one-out cross-validation (LOOCV) technique and compared in terms of multiple measures. Given the sensitivity of rainfed chickpea yield to the time of data, the predictions were made in two scenarios: (1) using the averages of the data of all growing months, and (2) using the data of a combination of months. The results showed that RF provides more accurate yield predictions than other methods. The predictions of this method were 7–8% different from the statistics reported by the Statistical Center and the Ministry of Agriculture of Iran. It was found that for pre-harvest prediction of rainfed chickpea yield, using the data of the March–April period (the averages of two months) offers the best result in terms of the correlation coefficient for the relationship between the yield and the predictor indices.

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Assessing accuracy of barley yield forecasting with integration of climate variables and support vector regression

Cited by 5 publications

References 27 publications

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

Multistage wheat yield prediction using hybrid machine learning techniques

Forecasting Rainfed Agricultural Production in Arid and Semi-Arid Lands Using Learning Machine Methods: A Case Study

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