A CNN-RNN Framework for Crop Yield Prediction

Khaki, Saeed; Wang, Lizhi; Archontoulis, Sotirios

doi:10.3389/fpls.2019.01750

Cited by 409 publications

(251 citation statements)

References 46 publications

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“…Optimization performed through the neural network using the feed forward back propagation method uses the supervised learning. However, the application of neural network is used in another way by Gandhi (16) and Khaki (17) for crop yield prediction. It is considered that the algorithm is provided with samples of inputs and outputs.…”

Section: Neural Network C Lassificationmentioning

confidence: 99%

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Supro¹

2020

IJST

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Objectives: This study aims to implement data analytics and machine learning approaches to rice data and to establish association rules on fixed attributes and their correlations for yield prediction of crops. Methods: The data of rice crop is collected from district Larkana as per defined parameters: area, production, yield, temperature, rainfall, humidity and wind speed. The pre-processing operations are applied on prepared dataset to execute data analytics and machine learning algorithms. The processed data are then input into an Apriori algorithm for generating association rules. Neural network model is used to perform optimization on resulted association rules. Findings: Minimum support and confidence values equal to 3 and 80 respectively were set using Apriori algorithm on prepared rice dataset and obtained 88 association rules. Among them, results of 28 associated rules predicted`High Yield Production'. Neural network model is experimented to optimize the predicted yield of district Larkana through which overall network performance of 97.8% is calculated. Previously, rice yield data of Larkana were not statistically and digitally predicted and investigated. Application : Group of effective and well-built association rules of yield prediction are core outcome of this study which will be helpful for researchers, farmers and government officials to improve the productivity of rice crop.

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Section: Neural Network C Lassificationmentioning

confidence: 99%

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Supro¹

2020

IJST

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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

Self Cite

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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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“…González-Camacho et al used probabilistic neural network for genome-based prediction of corn and wheat [13]. Machine learning approaches were also used for predicting performance of crops under different environmental conditions [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach

Khaki¹,

Khalilzadeh²,

Wang³

2020

PLoS ONE

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Experimental corn hybrids are created in plant breeding programs by crossing two parents, so-called inbred and tester, together. Identification of best parent combinations for crossing is challenging since the total number of possible cross combinations of parents is large and it is impractical to test all possible cross combinations due to limited resources of time and budget. In the 2020 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the historical yield performances of around 4% of total cross combinations of 593 inbreds with 496 testers which were planted in 280 locations between 2016 and 2018 and asked participants to predict the yield performance of cross combinations of inbreds and testers that have not been planted based on the historical yield data collected from crossing other inbreds and testers. In this paper, we present a collaborative filtering method which is an ensemble of matrix factorization method and a neural network to solve this problem. Our computational results suggested that the proposed model significantly outperformed other models such as deep factorization machines (DeepFM), generalized matrix factorization (GMF), LASSO, random forest (RF), and neural networks. Presented method and results were produced within the 2020 Syngenta Crop Challenge.

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A CNN-RNN Framework for Crop Yield Prediction

Cited by 409 publications

References 46 publications

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness

Forecasting Corn Yield With Machine Learning Ensembles

Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach

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