research-article

Weeds and Crops Classification Using Deep Convolutional Neural Network

Authors:

Jiang Haichen,

Chang Qingrui,

Liu Zheng GuangAuthors Info & Claims

ICCCV '20: Proceedings of the 3rd International Conference on Control and Computer Vision

Pages 40 - 44

https://doi.org/10.1145/3425577.3425585

Published: 23 January 2021 Publication History

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Abstract

Weeding is an effective method to improve crop yields. In precision agriculture, accurate and reliable weed identification methods provide the basis for achieving high-precision weed position control. This paper proposes a field weed and crop classification algorithm based on deep convolutional neural network and uses support vector machine (SVM) to optimize the algorithm. The model using VGG16+SVM for classification tasks has achieved an accuracy rate of 96,4%, which has practical significance for modern agricultural precision weeding.

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Cited By

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Jasphin EJoice C(2024)Weed Classification and Crop Health Monitoring in Microclimatic Conditions Using Thermal Image Analysis and Deep Learning AlgorithmsJournal of Plant Growth Regulation10.1007/s00344-024-11542-1Online publication date: 6-Nov-2024
https://doi.org/10.1007/s00344-024-11542-1
P NS SGanesh MPrakash Yadav GSuneeth TKumar K(2023)Classification of Weeds Detection Control Management Using Artificial and Deep Convolutional Neural Networks2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS57279.2023.10113127(1294-1299)Online publication date: 17-Mar-2023
https://doi.org/10.1109/ICACCS57279.2023.10113127
Damilare SYinka-Banjo CAjayi O(2023)Weed Identification in Plant Seedlings Using Convolutional Neural NetworksEmerging Technologies for Developing Countries10.1007/978-3-031-35883-8_14(206-224)Online publication date: 6-Jul-2023
https://doi.org/10.1007/978-3-031-35883-8_14
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ICCCV '20: Proceedings of the 3rd International Conference on Control and Computer Vision

August 2020

114 pages

ISBN:9781450388023

DOI:10.1145/3425577

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 January 2021

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ICCCV'20: 2020 the 3rd International Conference on Control and Computer Vision

August 23 - 25, 2020

Macau, China

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Jasphin EJoice C(2024)Weed Classification and Crop Health Monitoring in Microclimatic Conditions Using Thermal Image Analysis and Deep Learning AlgorithmsJournal of Plant Growth Regulation10.1007/s00344-024-11542-1Online publication date: 6-Nov-2024
https://doi.org/10.1007/s00344-024-11542-1
P NS SGanesh MPrakash Yadav GSuneeth TKumar K(2023)Classification of Weeds Detection Control Management Using Artificial and Deep Convolutional Neural Networks2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS57279.2023.10113127(1294-1299)Online publication date: 17-Mar-2023
https://doi.org/10.1109/ICACCS57279.2023.10113127
Damilare SYinka-Banjo CAjayi O(2023)Weed Identification in Plant Seedlings Using Convolutional Neural NetworksEmerging Technologies for Developing Countries10.1007/978-3-031-35883-8_14(206-224)Online publication date: 6-Jul-2023
https://doi.org/10.1007/978-3-031-35883-8_14
Elakya RVignesh UValarmathi PChithra NSigappi S(2022)A Novel Approach for Identification of Weeds in Paddy By using Deep Learning TechniquesInternational Journal of Electrical and Electronics Research10.37391/ijeer.10041210:4(832-836)Online publication date: 30-Dec-2022
https://doi.org/10.37391/ijeer.100412

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Classification of Weeds Using Neural Network Algorithms and Image Classifiers

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