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      Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)

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          Abstract

          The production of banana—one of the highly consumed fruits—is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In this paper, we propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted less than 78.8% recall for our sample images of a commercial banana farm in Thailand. To improve this result, we use three image processing methods—Linear Contrast Stretch, Synthetic Color Transform and Triangular Greenness Index—to enhance the vegetative properties of orthomosaic, generating multiple variants of orthomosaic. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples seen on each image variants, to produce multiple results of detection on our region of interest. 96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters, of same farm. Further discussion on results obtained from combination of multiple altitude variants are also discussed later in the research, in an attempt to find better altitude combination for data collection from UAV for the detection of banana plants. The results showed that merging the detection results of 40 and 50 meter dataset could detect the plants missed by each other, increasing recall upto 99%.

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          Extreme learning machine: Theory and applications

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            Deep learning in agriculture: A survey

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              Using Deep Learning for Image-Based Plant Disease Detection

              Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2019
                17 October 2019
                : 14
                : 10
                : e0223906
                Affiliations
                [001] School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Pathum Thani, Thailand
                University of Maryland at College Park, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5331-9897
                Article
                PONE-D-19-10466
                10.1371/journal.pone.0223906
                6797093
                31622450
                3840e1fa-e52e-43de-ab70-17efecc7b8c1
                © 2019 Neupane et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 May 2019
                : 1 October 2019
                Page count
                Figures: 12, Tables: 8, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100005790, Thammasat University;
                Award ID: TU Research Fund under Research Scholar, Contract No. 25/2561
                Award Recipient :
                This study was financially supported by Thammasat University Research Fund under the TU Research Scholar, Contract No. 25/2561.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Fruits
                Bananas
                Research and Analysis Methods
                Imaging Techniques
                Engineering and Technology
                Signal Processing
                Image Processing
                Biology and Life Sciences
                Plant Science
                Plant Anatomy
                Leaves
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Trees
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Grasses
                Biology and Life Sciences
                Agriculture
                Farms
                Custom metadata
                Data available from the Figshare Repository (DOIs: 10.6084/m9.figshare.7981547), and the URL is: https://figshare.com/s/62e391492b1be99515b4.

                Uncategorized
                Uncategorized

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