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      A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique

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          Abstract

          Farmers require accurate yield estimates, since they are key to predicting the volume of stock needed at supermarkets and to organizing harvesting operations. In many cases, the yield is visually estimated by the crop producer, but this approach is not accurate or time efficient. This study presents a rapid sensing and yield estimation scheme using off-the-shelf aerial imagery and deep learning. A Region-Convolutional Neural Network was trained to detect and count the number of apple fruit on individual trees located on the orthomosaic built from images taken by the unmanned aerial vehicle (UAV). The results obtained with the proposed approach were compared with apple counts made in situ by an agrotechnician, and an R 2 value of 0.86 was acquired (MAE: 10.35 and RMSE: 13.56). As only parts of the tree fruits were visible in the top-view images, linear regression was used to estimate the number of total apples on each tree. An R 2 value of 0.80 (MAE: 128.56 and RMSE: 130.56) was obtained. With the number of fruits detected and tree coordinates two shapefile using Python script in Google Colab were generated. With the previous information two yield maps were displayed: one with information per tree and another with information per tree row. We are confident that these results will help to maximize the crop producers' outputs via optimized orchard management.

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          Deep learning in agriculture: A survey

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            Recent advances in convolutional neural networks

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              Deep Learning for Computer Vision: A Brief Review

              Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/884712
                URI : https://loop.frontiersin.org/people/922654
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                15 July 2020
                2020
                : 11
                : 1086
                Affiliations
                [1] 1 Área de Ingeniería Agroforestal, Dpto. de Ingeniería Aeroespacial y Mecánica de Fluidos, Universidad de Sevilla , Sevilla, Spain
                [2] 2 Information Technology Group, Wageningen University & Research , Wageningen, Netherlands
                Author notes

                Edited by: Spyros Fountas, Agricultural University of Athens, Greece

                Reviewed by: Gambella Filippo, University of Sassari, Italy; Jordi Llorens Calveras, Universitat de Lleida, Spain

                *Correspondence: Manuel Pérez-Ruiz, manuelperez@ 123456us.es

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2020.01086
                7378326
                32765566
                b818ef52-d126-4ccd-99fc-839a0a0aced3
                Copyright © 2020 Apolo-Apolo, Pérez-Ruiz, Martínez-Guanter and Valente

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 March 2020
                : 01 July 2020
                Page count
                Figures: 13, Tables: 2, Equations: 6, References: 65, Pages: 15, Words: 7692
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                deep learning,apple,yield map,google colab,photogrammetry,fruit
                Plant science & Botany
                deep learning, apple, yield map, google colab, photogrammetry, fruit

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