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A Tactile Method for Rice Plant Recognition Based on Machine Learning
Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice leaves and the interference of weeds. The objective...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570840/ https://www.ncbi.nlm.nih.gov/pubmed/32916874 http://dx.doi.org/10.3390/s20185135 |
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author | Chen, Xueshen Mao, Yuanyang Ma, Xu Qi, Long |
author_facet | Chen, Xueshen Mao, Yuanyang Ma, Xu Qi, Long |
author_sort | Chen, Xueshen |
collection | PubMed |
description | Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice leaves and the interference of weeds. The objective of this study was to develop a novel rice plant recognition sensor based on a tactile method which acquires tactile information through physical touch. The tactile sensor would be mounted on the paddy field weeder to provide identification information for the actuator. First, a flexible gasbag filled with air was developed, where vibration features produced by tactile and sliding feedback were acquired when this apparatus touched rice plants or weeds, allowing the subtle vibration data with identification features to be reflected through the voltage value of an air-pressured sensor mounted inside the gasbag. Second, voltage data were preprocessed by three algorithms to optimize recognition features, including dimensional feature, dimensionless feature, and fractal dimension. The three types of features were used to train and test a neural network classifier. To maximize classification accuracy, an optimum set of features (b (variance), f (kurtosis), h (waveform factor), l (box dimension), and m (Hurst exponent)) were selected using a genetic algorithm. Finally, the feature-optimized classifier was trained, and the actual performances of the sensor at different contact positions were tested. Experimental results showed that the recognition rates of the end, middle, and root of the sensor were 90.67%, 98%, and 96% respectively. A tactile-based method with intelligence could produce high accuracy for rice plant recognition, as demonstrated in this study. |
format | Online Article Text |
id | pubmed-7570840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75708402020-10-28 A Tactile Method for Rice Plant Recognition Based on Machine Learning Chen, Xueshen Mao, Yuanyang Ma, Xu Qi, Long Sensors (Basel) Article Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice leaves and the interference of weeds. The objective of this study was to develop a novel rice plant recognition sensor based on a tactile method which acquires tactile information through physical touch. The tactile sensor would be mounted on the paddy field weeder to provide identification information for the actuator. First, a flexible gasbag filled with air was developed, where vibration features produced by tactile and sliding feedback were acquired when this apparatus touched rice plants or weeds, allowing the subtle vibration data with identification features to be reflected through the voltage value of an air-pressured sensor mounted inside the gasbag. Second, voltage data were preprocessed by three algorithms to optimize recognition features, including dimensional feature, dimensionless feature, and fractal dimension. The three types of features were used to train and test a neural network classifier. To maximize classification accuracy, an optimum set of features (b (variance), f (kurtosis), h (waveform factor), l (box dimension), and m (Hurst exponent)) were selected using a genetic algorithm. Finally, the feature-optimized classifier was trained, and the actual performances of the sensor at different contact positions were tested. Experimental results showed that the recognition rates of the end, middle, and root of the sensor were 90.67%, 98%, and 96% respectively. A tactile-based method with intelligence could produce high accuracy for rice plant recognition, as demonstrated in this study. MDPI 2020-09-09 /pmc/articles/PMC7570840/ /pubmed/32916874 http://dx.doi.org/10.3390/s20185135 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Xueshen Mao, Yuanyang Ma, Xu Qi, Long A Tactile Method for Rice Plant Recognition Based on Machine Learning |
title | A Tactile Method for Rice Plant Recognition Based on Machine Learning |
title_full | A Tactile Method for Rice Plant Recognition Based on Machine Learning |
title_fullStr | A Tactile Method for Rice Plant Recognition Based on Machine Learning |
title_full_unstemmed | A Tactile Method for Rice Plant Recognition Based on Machine Learning |
title_short | A Tactile Method for Rice Plant Recognition Based on Machine Learning |
title_sort | tactile method for rice plant recognition based on machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570840/ https://www.ncbi.nlm.nih.gov/pubmed/32916874 http://dx.doi.org/10.3390/s20185135 |
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