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dc.contributor.authorMorales, Giorgio
dc.contributor.authorSheppard, John W.
dc.date.accessioned2022-03-30T17:23:40Z
dc.date.available2022-03-30T17:23:40Z
dc.date.issued2021-11
dc.identifier.citationMorales, Giorgio, and John W. Sheppard. “Two-Dimensional Deep Regression for Early Yield Prediction of Winter Wheat.” Edited by Christopher R. Valenta, Joseph A. Shaw, and Masafumi Kimata. SPIE Future Sensing Technologies 2021 (November 14, 2021). doi:10.1117/12.2612209.en_US
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/16717
dc.description.abstractCrop 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.en_US
dc.language.isoen_USen_US
dc.rights© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).en_US
dc.titleTwo-dimensional deep regression for early yield prediction of winter wheaten_US
dc.typeArticleen_US
mus.citation.conferenceSPIE FUTURE SENSING TECHNOLOGIES 15-20 November 2021 Online Only, Japanen_US
mus.citation.journaltitleSPIE Future Sensing Technologiesen_US
mus.identifier.doi10.1117/12.2612209en_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.departmentComputer Science.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage4en_US


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