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Identification of Harvesting Year of Barley Seeds Using Near-Infrared Hyperspectral Imaging Combined with Convolutional Neural Network Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Plant Leaf Disease Segmentation Using Compressed UNet Architecture

Authors : Mohit Agarwal, Suneet Kr. Gupta, K. K. Biswas

Published in: Trends and Applications in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

In proposed work, a compressed version of UNet has been developed using Differential Evolution for segmenting the diseased regions in leaf images. The compressed model has been evaluated on potato late blight leaf images from PlantVillage dataset. The compressed model needs only 6.8% of space needed by original UNet architecture, and the inference time for disease classification is twice as fast without loss in performance metric of mean Intersection over Union (IoU).

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previous chapter Identification of Harvesting Year of Barley Seeds Using Near-Infrared Hyperspectral Imaging Combined with Convolutional Neural Network
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Appendix
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Metadata
Title
Plant Leaf Disease Segmentation Using Compressed UNet Architecture
Authors
Mohit Agarwal
Suneet Kr. Gupta
K. K. Biswas
Copyright Year
2021
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-75014-5

Electronic ISBN: 978-3-030-75015-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-75015-2_2

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