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Sensors 2018, 18(5), 1611;

A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network

Department of Biosystems Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 29 Bahman Boulevard, Tabriz 5166616471, Iran
Department of Engineering—Signal Processing, Aarhus University, DK-8000 Aarhus C, Denmark
Author to whom correspondence should be addressed.
Received: 28 February 2018 / Revised: 27 April 2018 / Accepted: 15 May 2018 / Published: 18 May 2018
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Determining the individual location of a plant, besides evaluating sowing performance, would make subsequent treatment for each plant across a field possible. In this study, a system for locating cereal plant stem emerging points (PSEPs) has been developed. In total, 5719 images were gathered from several cereal fields. In 212 of these images, the PSEPs of the cereal plants were marked manually and used to train a fully-convolutional neural network. In the training process, a cost function was made, which incorporates predefined penalty regions and PSEPs. The penalty regions were defined based on fault prediction of the trained model without penalty region assignment. By adding penalty regions to the training, the network’s ability to precisely locate emergence points of the cereal plants was enhanced significantly. A coefficient of determination of about 87 percent between the predicted PSEP number of each image and the manually marked one implies the ability of the system to count PSEPs. With regard to the obtained results, it was concluded that the developed model can give a reliable clue about the quality of PSEPs’ distribution and the performance of seed drills in fields. View Full-Text
Keywords: cereal; plants distribution; sowing performance cereal; plants distribution; sowing performance

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Karimi, H.; Skovsen, S.; Dyrmann, M.; Nyholm Jørgensen, R. A Novel Locating System for Cereal Plant Stem Emerging Points’ Detection Using a Convolutional Neural Network. Sensors 2018, 18, 1611.

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