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Rice disease identification through leaf image and IoT based smart rice field monitoring system

Rice disease identification in early-stage, proper medication in case of disease affection and managing irrigation at the appropriate time is the most consequential phenomena to increase the production level of the rice. In this paper, a novel technique to diagnose the rice diseases and smart medica...

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Main Authors: Islam, Md Nahidul, Ahmed, Fahim, Ahammed, Md Tanvir, Rashid, Mamunur, Bari, Bifta Sama
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/42293/1/Rice%20disease%20identification%20through%20leaf%20image.pdf
http://umpir.ump.edu.my/id/eprint/42293/2/Rice%20disease%20identification%20through%20leaf%20image%20and%20IoT%20based%20smart%20rice%20field%20monitoring%20system_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42293/
https://doi.org/10.1007/978-981-19-2095-0_45
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spelling my.ump.umpir.422932024-10-30T04:27:27Z http://umpir.ump.edu.my/id/eprint/42293/ Rice disease identification through leaf image and IoT based smart rice field monitoring system Islam, Md Nahidul Ahmed, Fahim Ahammed, Md Tanvir Rashid, Mamunur Bari, Bifta Sama T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Rice disease identification in early-stage, proper medication in case of disease affection and managing irrigation at the appropriate time is the most consequential phenomena to increase the production level of the rice. In this paper, a novel technique to diagnose the rice diseases and smart medication prescription system have been proposed. Furthermore, the internet of things (IoT) based smart rice field monitoring system has also been proposed. To identify rice diseases, the leaf image dataset (consists of healthy and three different diseases) has been analyzed through the convolutional neural network (CNN). The obtained rice disease diagnosis accuracy of the proposed system was 98.7%. In a real-time system, the leaf image data has been collected remotely using Raspberry Pi and the data has been sent to a server to be tested by a trained CNN model. Some sensors including soil moisture sensor, pressure sensor, humidity sensor, and temperature sensor have been implanted in the targeted field which aims to record the current scenario of the rice field and send the sensors data to the server. On a web page, proper medications have been displayed if any rice disease identified. Moreover, the user may monitor his field remotely which facilitates irrigation in opportune time. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42293/1/Rice%20disease%20identification%20through%20leaf%20image.pdf pdf en http://umpir.ump.edu.my/id/eprint/42293/2/Rice%20disease%20identification%20through%20leaf%20image%20and%20IoT%20based%20smart%20rice%20field%20monitoring%20system_ABS.pdf Islam, Md Nahidul and Ahmed, Fahim and Ahammed, Md Tanvir and Rashid, Mamunur and Bari, Bifta Sama (2022) Rice disease identification through leaf image and IoT based smart rice field monitoring system. In: Lecture Notes in Electrical Engineering. Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021 , 20 September 2021 , Gambang. pp. 529-539., 900. ISSN 1876-1100 ISBN 978-981192094-3 (Published) https://doi.org/10.1007/978-981-19-2095-0_45
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Islam, Md Nahidul
Ahmed, Fahim
Ahammed, Md Tanvir
Rashid, Mamunur
Bari, Bifta Sama
Rice disease identification through leaf image and IoT based smart rice field monitoring system
description Rice disease identification in early-stage, proper medication in case of disease affection and managing irrigation at the appropriate time is the most consequential phenomena to increase the production level of the rice. In this paper, a novel technique to diagnose the rice diseases and smart medication prescription system have been proposed. Furthermore, the internet of things (IoT) based smart rice field monitoring system has also been proposed. To identify rice diseases, the leaf image dataset (consists of healthy and three different diseases) has been analyzed through the convolutional neural network (CNN). The obtained rice disease diagnosis accuracy of the proposed system was 98.7%. In a real-time system, the leaf image data has been collected remotely using Raspberry Pi and the data has been sent to a server to be tested by a trained CNN model. Some sensors including soil moisture sensor, pressure sensor, humidity sensor, and temperature sensor have been implanted in the targeted field which aims to record the current scenario of the rice field and send the sensors data to the server. On a web page, proper medications have been displayed if any rice disease identified. Moreover, the user may monitor his field remotely which facilitates irrigation in opportune time.
format Conference or Workshop Item
author Islam, Md Nahidul
Ahmed, Fahim
Ahammed, Md Tanvir
Rashid, Mamunur
Bari, Bifta Sama
author_facet Islam, Md Nahidul
Ahmed, Fahim
Ahammed, Md Tanvir
Rashid, Mamunur
Bari, Bifta Sama
author_sort Islam, Md Nahidul
title Rice disease identification through leaf image and IoT based smart rice field monitoring system
title_short Rice disease identification through leaf image and IoT based smart rice field monitoring system
title_full Rice disease identification through leaf image and IoT based smart rice field monitoring system
title_fullStr Rice disease identification through leaf image and IoT based smart rice field monitoring system
title_full_unstemmed Rice disease identification through leaf image and IoT based smart rice field monitoring system
title_sort rice disease identification through leaf image and iot based smart rice field monitoring system
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/42293/1/Rice%20disease%20identification%20through%20leaf%20image.pdf
http://umpir.ump.edu.my/id/eprint/42293/2/Rice%20disease%20identification%20through%20leaf%20image%20and%20IoT%20based%20smart%20rice%20field%20monitoring%20system_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42293/
https://doi.org/10.1007/978-981-19-2095-0_45
_version_ 1822924721033838592
score 13.232414

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