Multi-stream CNN for spatial resource allocation: A crop management application

Alexandre Barbosa, Thiago Marinho, Nicolas Federico Martin, Naira Hovakimyan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Modeling the spatial structure of crop inputs is of great importance for accurate yield prediction. It is a fundamental step towards optimizing the spatial allocation of resources such as seed and fertilizer. We propose two distinct architectures of Multi-Stream Convolutional Neural Network (MSCNN) - Late Fusion (LF) and Early Fusion (EF) - to model yield response to seed and nutrient management. A study presents a comparison between proposed models with conventional 2D and 3D CNN architectures, and existing agronomy methods. The dataset used to train and test the models is constructed using on-farm experiment data from nine cornfields across the US together with multispectral satellite images. Results show that the MSCNN-LF achieved a 20% reduction of the prediction's mean squared error value when compared to a 3D CNN, and a 26% reduction when compared to a 2D CNN. An optimization algorithm uses the MSCNN-LF model's gradient to change the manageable inputs variables in a way the expected profit is maximized subject to resource constraints. It is shown that an increase of up to 5.2% on expected crop yield return is obtained when compared to usual management practices.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PublisherIEEE Computer Society
Pages258-266
Number of pages9
ISBN (Electronic)9781728193601
DOIs
StatePublished - Jun 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 - Virtual, Online, United States
Duration: Jun 14 2020Jun 19 2020

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2020-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
CountryUnited States
CityVirtual, Online
Period6/14/206/19/20

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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