An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network

Aminu Musa; Mohamed Hamada; Farouq Muhammad Aliyu; Mohammed Hassan

doi:10.1109/MCSoC51149.2021.00058

An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network

Aminu Musa, Mohamed Hamada, Farouq Muhammad Aliyu, Mohammed Hassan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Scopus citations

Abstract

Recently, researchers proposed automation of hydroponic systems to improve efficiency and minimize manpower requirements. Thus increasing profit and farm produce. However, a fully automated hydroponic system should be able to identify cases such as plant diseases, lack of nutrients, and inadequate water supply. Failure to detect these issues can lead to damage of crops and loss of capital. This paper presents an Internet of Things-based machine learning system for plant disease detection using Deep Convolutional Neural Network (DCNN). The model was trained on a data set of 54, 309 instances containing 38 different classes of plant disease. The images were retrieved from a plant village database. The system achieved an Accuracy of 98.0% and AUC precision score of 88.0%.

Original language	English
Title of host publication	Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	345-351
Number of pages	7
ISBN (Electronic)	9781665438605
DOIs	https://doi.org/10.1109/MCSoC51149.2021.00058
State	Published - 2021
Externally published	Yes
Event	14th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021 - Singapore, Singapore Duration: 20 Dec 2021 → 23 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021

Conference

Conference	14th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021
Country/Territory	Singapore
City	Singapore
Period	20/12/21 → 23/12/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Convolutional neural network
Hydroponic
Internet of Things
Raspberry pi

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/MCSoC51149.2021.00058

Cite this

Musa, A., Hamada, M., Aliyu, F. M., & Hassan, M. (2021). An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network. In Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021 (pp. 345-351). (Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MCSoC51149.2021.00058

Musa, Aminu ; Hamada, Mohamed ; Aliyu, Farouq Muhammad et al. / An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network. Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 345-351 (Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021).

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title = "An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network",

abstract = "Recently, researchers proposed automation of hydroponic systems to improve efficiency and minimize manpower requirements. Thus increasing profit and farm produce. However, a fully automated hydroponic system should be able to identify cases such as plant diseases, lack of nutrients, and inadequate water supply. Failure to detect these issues can lead to damage of crops and loss of capital. This paper presents an Internet of Things-based machine learning system for plant disease detection using Deep Convolutional Neural Network (DCNN). The model was trained on a data set of 54, 309 instances containing 38 different classes of plant disease. The images were retrieved from a plant village database. The system achieved an Accuracy of 98.0% and AUC precision score of 88.0%.",

keywords = "Convolutional neural network, Hydroponic, Internet of Things, Raspberry pi",

author = "Aminu Musa and Mohamed Hamada and Aliyu, {Farouq Muhammad} and Mohammed Hassan",

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Musa, A, Hamada, M, Aliyu, FM & Hassan, M 2021, An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network. in Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021. Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 345-351, 14th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021, Singapore, Singapore, 20/12/21. https://doi.org/10.1109/MCSoC51149.2021.00058

An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network. / Musa, Aminu; Hamada, Mohamed; Aliyu, Farouq Muhammad et al.
Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 345-351 (Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hamada, Mohamed

AU - Aliyu, Farouq Muhammad

AU - Hassan, Mohammed

PY - 2021

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AB - Recently, researchers proposed automation of hydroponic systems to improve efficiency and minimize manpower requirements. Thus increasing profit and farm produce. However, a fully automated hydroponic system should be able to identify cases such as plant diseases, lack of nutrients, and inadequate water supply. Failure to detect these issues can lead to damage of crops and loss of capital. This paper presents an Internet of Things-based machine learning system for plant disease detection using Deep Convolutional Neural Network (DCNN). The model was trained on a data set of 54, 309 instances containing 38 different classes of plant disease. The images were retrieved from a plant village database. The system achieved an Accuracy of 98.0% and AUC precision score of 88.0%.

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Musa A, Hamada M, Aliyu FM, Hassan M. An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network. In Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 345-351. (Proceedings - 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2021). doi: 10.1109/MCSoC51149.2021.00058

An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this