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2018 | OriginalPaper | Buchkapitel

Region-Based Convolutional Networks for End-to-End Detection of Agricultural Mushrooms

verfasst von : Alexander J. Olpin, Rozita Dara, Deborah Stacey, Mohamed Kashkoush

Erschienen in: Image and Signal Processing

Verlag: Springer International Publishing

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Abstract

Conventional image processing techniques have been applied to the field of agricultural machine vision for the purposes of identifying crops for quality control, weed detection, automated spraying and harvesting. With the recent advancements in computational hardware Region-based Convolutional Networks have met with varying levels of success in the area of object detection and classification. In this study we found that a Region-based Convolutional Neural Network was able to achieve a 92% accuracy rating while a Region-based Fully Convolutional Network was able to achieve an 87% accuracy rating in the area of object detection operating on a newly create agricultural mushroom dataset.

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Vorheriges Kapitel Adaptive Batch Extraction for Hyperspectral Image Classification Based on Convolutional Neural Network

Nächstes Kapitel Time Series Classification with Shallow Learning Shepard Interpolation Neural Networks

Food and Agriculture Organization of the United Nations. http://www.fao.org

World Population. http://www.worldometers.info/

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Titel: Region-Based Convolutional Networks for End-to-End Detection of Agricultural Mushrooms
verfasst von: Alexander J. Olpin
Rozita Dara
Deborah Stacey
Mohamed Kashkoush
Verlag: Springer International Publishing
Buch: Image and Signal Processing
Print ISBN: 978-3-319-94210-0

Electronic ISBN: 978-3-319-94211-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-94211-7_35

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