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Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop

Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired pro...

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Main Authors: Hamna Waheed, Noureen Zafar, Waseem Akram, Awais Manzoor, Abdullah Gani, Saif ul Islam
Format: Article
Language:English
English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2022
Subjects:
SB1-1110 Plant culture
Online Access:https://eprints.ums.edu.my/id/eprint/33879/3/Deep%20learning%20based%20disease%2C%20pest%20pattern%20and%20nutritional%20deficiency%20detection%20system%20for%20%E2%80%9CZingiberaceae%E2%80%9D%20crop.pdf
https://eprints.ums.edu.my/id/eprint/33879/1/Deep%20Learning%20Based%20Disease%2C%20Pest%20Pattern%20and%20Nutritional%20Deficiency%20Detection%20System%20for%20%E2%80%9CZingiberaceae%E2%80%9D%20Crop%20_ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/33879/
https://www.mdpi.com/2077-0472/12/6/742/htm
https://doi.org/10.3390/agriculture12060742
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spelling my.ums.eprints.338792022-08-19T07:27:13Z https://eprints.ums.edu.my/id/eprint/33879/ Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop Hamna Waheed Noureen Zafar Waseem Akram Awais Manzoor Abdullah Gani Saif ul Islam SB1-1110 Plant culture Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99% for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96%. For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (96%). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets. Multidisciplinary Digital Publishing Institute (MDPI) 2022 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/33879/3/Deep%20learning%20based%20disease%2C%20pest%20pattern%20and%20nutritional%20deficiency%20detection%20system%20for%20%E2%80%9CZingiberaceae%E2%80%9D%20crop.pdf text en https://eprints.ums.edu.my/id/eprint/33879/1/Deep%20Learning%20Based%20Disease%2C%20Pest%20Pattern%20and%20Nutritional%20Deficiency%20Detection%20System%20for%20%E2%80%9CZingiberaceae%E2%80%9D%20Crop%20_ABSTRACT.pdf Hamna Waheed and Noureen Zafar and Waseem Akram and Awais Manzoor and Abdullah Gani and Saif ul Islam (2022) Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop. Agriculture, 12 (742). pp. 1-17. ISSN 2077-0472 https://www.mdpi.com/2077-0472/12/6/742/htm https://doi.org/10.3390/agriculture12060742
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic SB1-1110 Plant culture
spellingShingle SB1-1110 Plant culture
Hamna Waheed
Noureen Zafar
Waseem Akram
Awais Manzoor
Abdullah Gani
Saif ul Islam
Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop
description Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99% for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96%. For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (96%). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.
format Article
author Hamna Waheed
Noureen Zafar
Waseem Akram
Awais Manzoor
Abdullah Gani
Saif ul Islam
author_facet Hamna Waheed
Noureen Zafar
Waseem Akram
Awais Manzoor
Abdullah Gani
Saif ul Islam
author_sort Hamna Waheed
title Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop
title_short Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop
title_full Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop
title_fullStr Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop
title_full_unstemmed Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop
title_sort deep learning based disease, pest pattern and nutritional deficiency detection system for “zingiberaceae” crop
publisher Multidisciplinary Digital Publishing Institute (MDPI)
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/33879/3/Deep%20learning%20based%20disease%2C%20pest%20pattern%20and%20nutritional%20deficiency%20detection%20system%20for%20%E2%80%9CZingiberaceae%E2%80%9D%20crop.pdf
https://eprints.ums.edu.my/id/eprint/33879/1/Deep%20Learning%20Based%20Disease%2C%20Pest%20Pattern%20and%20Nutritional%20Deficiency%20Detection%20System%20for%20%E2%80%9CZingiberaceae%E2%80%9D%20Crop%20_ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/33879/
https://www.mdpi.com/2077-0472/12/6/742/htm
https://doi.org/10.3390/agriculture12060742
_version_ 1760231221022949376
score 13.211869

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