Classification of Crop Tolerance to Heat and Drought—A Deep Convolutional Neural Networks Approach

Khaki, Saeed; Khalilzadeh, Zahra; Wang, Lizhi

doi:10.3390/agronomy9120833

Cited by 30 publications

(18 citation statements)

References 41 publications

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“…Yang et al (2019) investigated the ability of CNN to estimate rice grain yield using remotely sensed images and found that CNN model provided robust yield forecast throughout the ripening stage. Khaki and Khalilzadeh (2019) used deep CNNs to predict corn yield loss across 1,560 locations in the United States and Canada.…”

Section: Introductionmentioning

confidence: 99%

A CNN-RNN Framework for Crop Yield Prediction

2020

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Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN has three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information.(2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.

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Section: Introductionmentioning

confidence: 99%

A CNN-RNN Framework for Crop Yield Prediction

2020

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“…Many studies have approached regression problems, in which the response variable is continuous, with machine learning to solve an ecological problem ( James et al., 2013 ). These studies include but not limited to crop yield predictions ( Drummond et al., 2003 ; Vincenzi et al., 2011 ; González Sánchez et al., 2014 ; Jeong et al., 2016 ; Pantazi et al., 2016 ; Cai et al., 2017 ; Chlingaryan et al., 2018 ; Crane-Droesch, 2018 ; Basso and Liu, 2019 ; Khaki and Wang, 2019 ; Shahhosseini et al., 2019c ; Emirhüseyinoğlu and Ryan, 2020 ; Khaki et al., 2020 ), crop quality ( Hoogenboom et al., 2004 ; Karimi et al., 2008 ; Mutanga et al., 2012 ; Shekoofa et al., 2014 ; Qin et al., 2018 ; Ansarifar and Wang, 2019 ; Khaki et al, 2019 ; Lawes et al., 2019 ; Moeinizade et al, 2019 ), water management ( Mohammadi et al., 2015 ; Feng et al., 2017 ; Mehdizadeh et al., 2017 ), soil management ( Johann et al., 2016 ; Morellos et al., 2016 ; Nahvi et al., 2016 ), and others.…”

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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“…The latter have enjoyed wide applications in various ecological classification problems and predictive modeling (Rumpf et al 2010, Shekoofa et al 2014, Crane-Droesch 2018, Karimzadeh and Olafsson 2019, Pham and Olafsson 2019a, 2019b because of their adeptness to deal with nonlinear relationships, high-order interactions and non-normal data (De'ath and Fabricius 2000). Such methods include regularized regressions (Hoerl and Kennard 1970, Tibshirani 1996, Zou and Hastie 2005, tree-based models (Shekoofa et al 2014), Support Vector Machines (Basak et al 2007, Karimi et al 2008, Neural Networks (Liu et al 2001, Crane-Droesch 2018, Khaki and Khalilzadeh 2019, Khaki and Wang 2019 and others.…”

Section: Introductionmentioning

confidence: 99%

Maize yield and nitrate loss prediction with machine learning algorithms

Shahhosseini

Martinez-Feria

et al. 2019

Environ. Res. Lett.

142

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Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked:(1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10%-40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management.

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Classification of Crop Tolerance to Heat and Drought—A Deep Convolutional Neural Networks Approach

Cited by 30 publications

References 41 publications

A CNN-RNN Framework for Crop Yield Prediction

A CNN-RNN Framework for Crop Yield Prediction

Forecasting Corn Yield With Machine Learning Ensembles

Maize yield and nitrate loss prediction with machine learning algorithms

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