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14 November 2021 Two-dimensional deep regression for early yield prediction of winter wheat
Giorgio Morales, John W. Sheppard
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Proceedings Volume 11914, SPIE Future Sensing Technologies 2021; 119140H (2021) https://doi.org/10.1117/12.2612209
Event: SPIE Future Sensing Technologies, 2021, Online Only
Abstract
Crop yield prediction is one of the tasks of Precision Agriculture that can be automated based on multi-source periodic observations of the fields. We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We present a CNN architecture called Hyper3DNetReg that takes in a multi-channel input image and outputs a two-dimensional raster, where each pixel represents the predicted yield value of the corresponding input pixel. We utilize radar data acquired from the Sentinel-1 satellites, while the on-ground data correspond to a set of six raster features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), and aspect. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present experiments over four fields of winter wheat and show that our proposed methodology yields better results than five compared methods, including multiple linear regression, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.
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Giorgio Morales and John W. Sheppard "Two-dimensional deep regression for early yield prediction of winter wheat", Proc. SPIE 11914, SPIE Future Sensing Technologies 2021, 119140H (14 November 2021); https://doi.org/10.1117/12.2612209
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KEYWORDS
Data modeling
Data acquisition
Neural networks
Satellite imaging
Synthetic aperture radar
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