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Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural

The Giant African snail is the world's largest, destructive and most damaging land pest in agricultural plantations. These pests are sometimes called as the natural enemies of farmers. High devouring capacity of these snails causes heavy damages to vegetation. In this work, various snails are c...

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Main Authors: J., Adeline Sneha, Chakravarthi, Rekha, Khamis, Nor Hisham, D., Joshua Amarnath
Format: Article
Published: Kalpana Corporation 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/93959/
https://www.e-ijep.co.in/41-1-25-33/
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spelling my.utm.939592022-02-28T13:26:57Z http://eprints.utm.my/id/eprint/93959/ Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural J., Adeline Sneha Chakravarthi, Rekha Khamis, Nor Hisham D., Joshua Amarnath TK Electrical engineering. Electronics Nuclear engineering The Giant African snail is the world's largest, destructive and most damaging land pest in agricultural plantations. These pests are sometimes called as the natural enemies of farmers. High devouring capacity of these snails causes heavy damages to vegetation. In this work, various snails are collected and placed in the room temperature. The snails were feed with the consumables, such as cauliflower, tomatoes and soil, indeed the sound frequency is measured. In addition, the various activities also monitored and the respective frequencies are observed. For instance, the movement of a snail, retraction of the head in shell, head movements are recorded from the acoustic room with the recorder. However, monitoring the snail low level sound were challenging for recording. Thus the high precision device is required for recording the signals. The recording has been done for a number of days in order to evade uncertainty. The recorded accurate signals are chosen for the analysis. These signals are analyzed in the time and frequency domain. The statistical features are extracted from the signal. Further, the power spectral density, sound pressure level and equal loudness contour of the signals are calculated. The wide range of frequency of the snail of its various movements is determined. The extracted statistical features from the signals are fed into the neural network for training. Back propagation algorithm is used for classification along with the snail's signal, few noise signals also fed into the neural network to identify the effectiveness of network in the identification of the pest. It gives 99% efficiency in identifying the snail's signal. This work is also compared with capacitance based pest identification. This work has been experimentally validated with low-cost acoustic sensor that wirelessly communicates to the base, to monitor and control snail activity, the work suggested in this paper is a non-destructive form of pest identification which does not harm the crops as well as the environment. Kalpana Corporation 2021-01 Article PeerReviewed J., Adeline Sneha and Chakravarthi, Rekha and Khamis, Nor Hisham and D., Joshua Amarnath (2021) Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural. Indian Journal of Environmental Protection, 41 (1). pp. 25-33. ISSN 0253-7141 https://www.e-ijep.co.in/41-1-25-33/
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
J., Adeline Sneha
Chakravarthi, Rekha
Khamis, Nor Hisham
D., Joshua Amarnath
Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural
description The Giant African snail is the world's largest, destructive and most damaging land pest in agricultural plantations. These pests are sometimes called as the natural enemies of farmers. High devouring capacity of these snails causes heavy damages to vegetation. In this work, various snails are collected and placed in the room temperature. The snails were feed with the consumables, such as cauliflower, tomatoes and soil, indeed the sound frequency is measured. In addition, the various activities also monitored and the respective frequencies are observed. For instance, the movement of a snail, retraction of the head in shell, head movements are recorded from the acoustic room with the recorder. However, monitoring the snail low level sound were challenging for recording. Thus the high precision device is required for recording the signals. The recording has been done for a number of days in order to evade uncertainty. The recorded accurate signals are chosen for the analysis. These signals are analyzed in the time and frequency domain. The statistical features are extracted from the signal. Further, the power spectral density, sound pressure level and equal loudness contour of the signals are calculated. The wide range of frequency of the snail of its various movements is determined. The extracted statistical features from the signals are fed into the neural network for training. Back propagation algorithm is used for classification along with the snail's signal, few noise signals also fed into the neural network to identify the effectiveness of network in the identification of the pest. It gives 99% efficiency in identifying the snail's signal. This work is also compared with capacitance based pest identification. This work has been experimentally validated with low-cost acoustic sensor that wirelessly communicates to the base, to monitor and control snail activity, the work suggested in this paper is a non-destructive form of pest identification which does not harm the crops as well as the environment.
format Article
author J., Adeline Sneha
Chakravarthi, Rekha
Khamis, Nor Hisham
D., Joshua Amarnath
author_facet J., Adeline Sneha
Chakravarthi, Rekha
Khamis, Nor Hisham
D., Joshua Amarnath
author_sort J., Adeline Sneha
title Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural
title_short Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural
title_full Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural
title_fullStr Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural
title_full_unstemmed Identification and control of giant African snail in cauliflower farms by low frequency sound measurement technique and back propagation neural
title_sort identification and control of giant african snail in cauliflower farms by low frequency sound measurement technique and back propagation neural
publisher Kalpana Corporation
publishDate 2021
url http://eprints.utm.my/id/eprint/93959/
https://www.e-ijep.co.in/41-1-25-33/
_version_ 1726791459822108672
score 13.211869

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