Corn Yield Prediction with Ensemble CNN-DNN

05/29/2021
by   Mohsen Shahhosseini, et al.
0

We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The developed data set is a combination of management, environment, and historical corn yields from 1980-2019. Two scenarios for ensemble creation are considered: homogenous and heterogeneous ensembles. In homogenous ensembles, the base CNN-DNN models are all the same, but they are generated with a bagging procedure to ensure they exhibit a certain level of diversity. Heterogenous ensembles are created from different base CNN-DNN models which share the same architecture but have different levels of depth. Three types of ensemble creation methods were used to create several ensembles for either of the scenarios: Basic Ensemble Method (BEM), Generalized Ensemble Method (GEM), and stacked generalized ensembles. Results indicated that both designed ensemble types (heterogenous and homogenous) outperform the ensembles created from five individual ML models (linear regression, LASSO, random forest, XGBoost, and LightGBM). Furthermore, by introducing improvements over the heterogeneous ensembles, the homogenous ensembles provide the most accurate yield predictions across US Corn Belt states. This model could make 2019 yield predictions with a root mean square error of 866 kg/ha, equivalent to 8.5 relative root mean square, and could successfully explain about 77 spatio-temporal variation in the corn grain yields. The significant predictive power of this model can be leveraged for designing a reliable tool for corn yield prediction which will, in turn, assist agronomic decision-makers.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 16

page 17

05/05/2018

Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework

Heterogeneous ensembles built from the predictions of a wide variety and...
02/21/2018

Pooling homogeneous ensembles to build heterogeneous ensembles

In ensemble methods, the outputs of a collection of diverse classifiers ...
05/23/2019

Hierarchical Multimodel Ensemble Estimates of Soil Water Retention with Global Coverage

A correct quantification of mass and energy exchange processes among lan...
11/20/2019

A CNN-RNN Framework for Crop Yield Prediction

Crop yield prediction is extremely challenging due to its dependence on ...
08/14/2019

Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms

Pre-season prediction of crop production outcomes such as grain yields a...
07/24/2017

Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation

This paper presents a novel ensemble framework to extract highly discrim...
04/06/2021

Enhancing the Diversity of Predictions Combination by Negative Correlation Learning

Predictions combination, as a combination model approach with adjustment...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.