A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT

Stas, Michiel; Orshoven, Jos Van; Dong, Qinghan; Heremans, Stien; Zhang, Beier

doi:10.1109/agro-geoinformatics.2016.7577625

Cited by 28 publications

(11 citation statements)

References 22 publications

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“…Climate variables are the primary inputs for the above two approaches. Using more vegetation growth status variables, such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), would obtain better results for yield predictions [2,[21][22][23]. The rapid development of remote sensing technology makes these indexes available both on longer temporal and wider spatial resolutions [24].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

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Section: Introductionmentioning

confidence: 99%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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“…Ensemble learning is proved to be effective as it can reduce bias, variance, or both and is able to better capture the underlying distribution of the data in order to make better predictions, if the base learners are diverse enough ( Dietterich, 2000 ; Pham and Olafsson, 2019a ; Pham and Olafsson, 2019b ; Shahhosseini et al., 2019a ; Shahhosseini et al., 2019b ). The usage of ensemble learning in ecological problems is becoming more widespread; for instance, bagging and specifically random forest ( Vincenzi et al., 2011 ; Mutanga et al., 2012 ; Fukuda et al., 2013 ; Jeong et al., 2016 ), boosting ( De'ath, 2007 ; Heremans et al., 2015 ; Belayneh et al., 2016 ; Stas et al., 2016 ; Sajedi-Hosseini et al., 2018 ), and stacking ( Conţiu and Groza, 2016 ; Cai et al., 2017 ; Shahhosseini et al., 2019a ), are some of the ensemble learning applications in agriculture. Although, there have been studies using some of the ensemble methods in the agriculture domain, to the best of our knowledge, there is no study to compare the effectiveness of ensemble learning for ecological problems, especially when there are temporal and spatial correlations in the data.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Corn Yield With Machine Learning Ensembles

2020

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The emergence of new technologies to synthesize and analyze big data with highperformance computing has increased our capacity to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predictions faster and with higher flexibility compared to simulation crop modeling. However, a single machine learning model can be outperformed by a "committee" of models (machine learning ensembles) that can reduce prediction bias, variance, or both and is able to better capture the underlying distribution of the data. Yet, there are many aspects to be investigated with regard to prediction accuracy, time of the prediction, and scale. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine leaning based framework to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa) considering complete and partial inseason weather knowledge. Several ensemble models are designed using blocked sequential procedure to generate out-of-bag predictions. The forecasts are made in county-level scale and aggregated for agricultural district and state level scales. Results show that the proposed optimized weighted ensemble and the average ensemble are the most precise models with RRMSE of 9.5%. Stacked LASSO makes the least biased predictions (MBE of 53 kg/ha), while other ensemble models also outperformed the base learners in terms of bias. On the contrary, although random k-fold cross-validation is replaced by blocked sequential procedure, it is shown that stacked ensembles perform not as good as weighted ensemble models for time series data sets as they require the data to be non-IID to perform favorably. Comparing our proposed model forecasts with the literature demonstrates the acceptable performance of forecasts made by our proposed ensemble model. Results from the scenario of having partial in-season weather knowledge reveals that decent yield forecasts with RRMSE of 9.2% can be made as early as June 1 st. Moreover, it was shown that the proposed model performed better than individual models and benchmark ensembles at agricultural district and statelevel scales as well as county-level scale. To find the marginal effect of each input feature on the forecasts made by the proposed ensemble model, a methodology is suggested that is the basis for finding feature importance for the ensemble model. The findings

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“…Multiple regression, where predictors are NDVI values from different growth stages of the crop, is one of the methods which can be applied [15,16]. Cumulative NDVI (cNDVI) is based on time series until pre-heading stage or later stages can be used as a predictor of cereal grain yield [17][18][19][20]. Yield prediction can be performed using vegetation condition index (VCI), which is expressed in the percentage and gives an idea of where the observed value is situated between the extreme values (minimum and maximum) during the previous years [21].…”

Section: Introductionmentioning

confidence: 99%

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Panek

Gozdowski

2021

Agronomy

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In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

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A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT

Cited by 28 publications

References 22 publications

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Forecasting Corn Yield With Machine Learning Ensembles

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

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