A Neural Network for Setting Target Corn Yields

Liu, J.; Goering, Carroll E.; Tian, Lei

doi:10.13031/2013.6097

Cited by 65 publications

(16 citation statements)

References 5 publications

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“…Fukuda et al (2013) also used random forest for predicting mango fruit yields in response to water supply under different irrigation regimes, and found that random forest was applicable for mango yield prediction with a specific focus on water management. Liu et al (2001) applied artificial neural networks to approximate a nonlinear function to relate the corn yield to input variables such as weather, soil, and management practices. Ransom et al (2019) evaluated machine learning methods for corn nitrogen recommendation tools using soil and weather information.…”

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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“…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.

132

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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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“…However, there are variations between fields and years. Many studies used ANN to predict yields [18], [14]. For example, in a study on setting target corn yields, the root mean square error (RMSE) was 20% [18].…”

Section: Alternative Methodsmentioning

confidence: 99%

“…Many studies used ANN to predict yields [18], [14]. For example, in a study on setting target corn yields, the root mean square error (RMSE) was 20% [18]. In a comparison of the efficiency of multiple linear regression models and ANN models to predict the soybean yields in Maryland, the ANN model performed better, with an r 2 and RMSE of 0.81 and 214 compared with 0.46 and 312 for the linear regression model [14].…”

Section: Alternative Methodsmentioning

confidence: 99%

Self-Organising and Self-Learning Model for Soybean Yield Prediction

Alghamdi

Angelov

Giménez

et al. 2019

2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)

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Machine learning has arisen with advanced data analytics. Many factors influence crop yield, such as soil, amount of water, climate, and genotype. Determining factors that significantly influence yield prediction and identify the most appropriate predictive methods are important in yield management. It is critical to consider and study the combination of different crop factors and their impact on the yield. The objectives of this paper are: (1) to use advanced data analytic techniques to precisely predict the soybean crop yields, (2) to identify the most influential features that impact soybean predictions, (3) to illustrate the ability of Fuzzy Rule-Based (FRB) subsystems , which are self-organizing, self-learning, and data-driven, by using the recently developed Autonomous Learning Multiple-Model First-order (ALMMo-1) system, and (4) to compare the performance with other well-known methods. The ALMMo-1 system is a transparent model, which stakeholders can easily read and interpret. The model is a datadriven and composed of prototypes selected from the actual data. Many factors affect the yield, and data clouds can be formed in the feature/data space based on the data density. The data cloud is the key to the IF part of FRB subsystems , while the THEN part (the consequences of the IF condition) illustrates the yield prediction in the form of a linear regression model, which consists of the yield features or factors. In addition, the model can determine the most influential features of the yield prediction online. The model shows an excellent prediction accuracy with a Root Mean Square Error (RMSE) of 0.0883, and Non-Dimensional Error Index (NDEI) of 0.0611, which is competitive with state-of-the-art methods.

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A Neural Network for Setting Target Corn Yields

Cited by 65 publications

References 5 publications

A CNN-RNN Framework for Crop Yield Prediction

A CNN-RNN Framework for Crop Yield Prediction

Maize yield and nitrate loss prediction with machine learning algorithms

Self-Organising and Self-Learning Model for Soybean Yield Prediction

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