7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery

      letter

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq. Experiments were conducted in the Brazilian Cerrado with 110 genotypes with three replications, totaling 330 plots. Two regression models based on Convolutional Neural Networks (CNNs) named AlexNet and ResNet18 were evaluated, and compared to VGGNet—adopted in previous work in the same thematic for other grass species. The predictions returned by the models reached a correlation of 0.88 and a mean absolute error of 12.98% using AlexNet considering pre-training and data augmentation. This proposal may contribute to forage biomass estimation in breeding populations and livestock areas, as well as to reduce the labor in the field.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: not found
          • Article: not found

          Python for Scientific Computing

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Deep learning in agriculture: A survey

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Very Deep Convolutional Networks for Large-Scale Image Recognition

              , (2014)
              In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
                Bookmark

                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                26 August 2020
                September 2020
                : 20
                : 17
                : 4802
                Affiliations
                [1 ]Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070900, MS, Brazil; wellingtonvcastro@ 123456gmail.com (W.C.); caio.polidoro@ 123456aluno.ufms.br (C.P.); lucas.rodrigues@ 123456ifms.edu.br (L.R.); edsontm@ 123456facom.ufms.br (E.M.)
                [2 ]Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070900, MS, Brazil; wesley.goncalves@ 123456ufms.br (W.G.); eloiseambiental@ 123456gmail.com (E.S.)
                [3 ]Faculty of Engineering, Architecture and Urbanism, University of Western São Paulo, Presidente Prudente 19067175, SP, Brazil; lucasosco@ 123456unoeste.br
                [4 ]Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande 79106550, MS, Brazil; mateus.santos@ 123456embrapa.br (M.S.); liana.jank@ 123456embrapa.br (L.J.); sanzio.barrios@ 123456embrapa.br (S.B.); cbdovalle@ 123456gmail.com (C.V.); rosangela.simeao@ 123456embrapa.br (R.S.); camilo.carromeu@ 123456embrapa.br (C.C.)
                [5 ]Embrapa Instrumentation, São Carlos 13560970, SP, Brazil; lucio.jorge@ 123456embrapa.br
                Author notes
                [* ]Correspondence: jose.marcato@ 123456ufms.br
                Author information
                https://orcid.org/0000-0002-2847-2240
                https://orcid.org/0000-0002-9096-6866
                https://orcid.org/0000-0002-8212-0828
                https://orcid.org/0000-0002-0258-536X
                https://orcid.org/0000-0002-8815-6653
                https://orcid.org/0000-0003-4704-066X
                https://orcid.org/0000-0001-5324-906X
                https://orcid.org/0000-0001-9436-3678
                https://orcid.org/0000-0002-5490-4959
                https://orcid.org/0000-0003-3054-5127
                https://orcid.org/0000-0001-8351-846X
                https://orcid.org/0000-0001-8341-3203
                https://orcid.org/0000-0002-4471-0886
                Article
                sensors-20-04802
                10.3390/s20174802
                7506807
                32858803
                9f2a3277-fcaa-47bf-a21d-737b98bfab3f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 July 2020
                : 12 August 2020
                Categories
                Letter

                Biomedical engineering
                convolutional neural network,biomass yield,data augmentation,phenotyping

                Comments

                Comment on this article