A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015

Kim, Nari; Ha, Kyung‐Ja; Park, No-Wook; Cho, Jaeil; Hong, Sungwook; Lee, Yang-Won

doi:10.3390/ijgi8050240

Cited by 97 publications

(66 citation statements)

References 55 publications

(110 reference statements)

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“…(2016) could predict US corn yield with 30 years of data using random forest with the prediction RRMSE of 16.7%; while Crane-Droesch (2018) could achieve out-of-bag USDA corn prediction error of 13.4% using semiparametric neural network with a data set comprised of the information for years 1979–2016. Kim et al. (2019) designed a model which predicted cross-validation out-of-bag samples with a RRMSE of 7.9% ( Table 3 ).…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

Forecasting Corn Yield With Machine Learning Ensembles

2020

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“…You et al (2017) applied CNNs and RNNs to predict soybean yield based on a sequence of remotely sensed images. Kim et al (2019) developed a deep neural network model for crop yield prediction using optimized input variables from satellite products and meteorological datasets between 2006 and 2015. Wang et al (2018) designed a deep learning framework to predict soybean crop yields in Argentina and they also achieved satisfactory results with a transfer learning approach to predict Brazil soybean harvests with a smaller amount of data.…”

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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“…The task is then to find a functional form for that can correctly predict new cases using SVM [7] [15] [22] [25] [26] [31]. This can be achieved by training the SVM model on a sample set , i.e., training set, a process that involves, like classification ( see above ), the sequential optimization of an error function.…”

Section: Algorithm -Methodsmentioning

confidence: 99%

“…study used the dataset [22] of crop yield of 25 districts of Tamil Nadu and US region (provided by Michigan State University, USA). Many factors have an impact on the crop yield .…”

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confidence: 99%

Artificial Intelligence and Agriculture 5. 0

Murugesan¹,

Kidambi²,

G³

et al. 2019

IJRTE

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Abstract : Use of Artificial Intelligence and Robotics in agriculture is called as Agriculture 5.0 Disruptive technology should help in solving the social needs . Rogers suggest to develop human centric " Ubiquitous Computing " solution , for specific domain (agriculture production). Crop yield prediction (CYP) is vital to address the ever growing demand of food requirements of burgeoning world population and to prevent starvation . Artificial Intelligence can offer effective and practical solution for the problem. Machine Learning ( ML ) and Deep learning ( DL) have been evaluated. Machine Learning models ( using python , R , Seaborn ) have been experimented in this paper. Data of crop yield is used for model evaluation , which includes horticultural product (Banana) , cash crop ( Sugarcane), food crop (Rice) , for kharif , rabi season ( Dataset of Tamil Nadu and US region); Future research could combine remote sensing data and machine learning to predict the yield ( using google earth engine). Better accuracy in crop prediction is possible when vital data like soil moisture content ( ground level, root level and extreme ends of the field), 14 micro nutrients of soil is made available , for many seasons.

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A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015

Cited by 97 publications

References 55 publications

Forecasting Corn Yield With Machine Learning Ensembles

Forecasting Corn Yield With Machine Learning Ensembles

A CNN-RNN Framework for Crop Yield Prediction

Artificial Intelligence and Agriculture 5. 0

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