Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70721

An Approach for Weed Detection Using CNNs And Transfer Learning

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Title:An Approach for Weed Detection Using CNNs And Transfer Learning
Authors:Ofori, Martinson
El-Gayar, Omar
Keywords:Analytics and Decision Support for Green IS and Sustainability Applications
convolutional neural network
deep learning
efficientnet
plant seedling
show 4 moreprecision agriculture
smart farming
transfer learning
weed detection
show less
Date Issued:05 Jan 2021
Abstract:To prevent yield losses, it is critical to eliminate competition between food crops and weeds at the onset of plant growth. While uniform spraying of herbicides can be economically and environmentally inefficient, site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. Past research on weed detection in SSWM has used a large deep convolutional neural network (DCNN) for weed detection. These models are, however, computationally expensive and prone to overfitting on smaller datasets. In this paper, we propose an approach to detecting weeds amongst plant seedlings using transfer learning in a small network. Our approach combines the mobile-sized EfficientNet with transfer learning to achieve up to 95.44% classification accuracy on plant seedlings. Due to the robustness of transfer learning methods, this approach would be beneficial in improving both the classification accuracy and generalizability of current weed detection methods.
Pages/Duration:8 pages
URI:http://hdl.handle.net/10125/70721
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.109
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Analytics and Decision Support for Green IS and Sustainability Applications


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