Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields

Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields

Crop yield prediction prior to harvest is important for crop income and insurance projections, and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and predictor variables, especially at the field scale. In this...

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Bibliographic Details
Main Authors: Angela Kross, Evelyn Znoj, Daihany Callegari, Gurpreet Kaur, Mark Sunohara, David R. Lapen, Heather McNairn
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Remote Sensing
Subjects:
corn
soybean
yield prediction
remote sensing
vegetation indices
artificial neural network
Online Access:https://www.mdpi.com/2072-4292/12/14/2230

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