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Wheat Yellow Rust Disease Infection Type Classification Using Texture Features

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed...

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Autores principales: Shafi, Uferah, Mumtaz, Rafia, Haq, Ihsan Ul, Hafeez, Maryam, Iqbal, Naveed, Shaukat, Arslan, Zaidi, Syed Mohammad Hassan, Mahmood, Zahid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747460/
https://www.ncbi.nlm.nih.gov/pubmed/35009689
http://dx.doi.org/10.3390/s22010146
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author Shafi, Uferah
Mumtaz, Rafia
Haq, Ihsan Ul
Hafeez, Maryam
Iqbal, Naveed
Shaukat, Arslan
Zaidi, Syed Mohammad Hassan
Mahmood, Zahid
author_facet Shafi, Uferah
Mumtaz, Rafia
Haq, Ihsan Ul
Hafeez, Maryam
Iqbal, Naveed
Shaukat, Arslan
Zaidi, Syed Mohammad Hassan
Mahmood, Zahid
author_sort Shafi, Uferah
collection PubMed
description Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.
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spelling pubmed-87474602022-01-11 Wheat Yellow Rust Disease Infection Type Classification Using Texture Features Shafi, Uferah Mumtaz, Rafia Haq, Ihsan Ul Hafeez, Maryam Iqbal, Naveed Shaukat, Arslan Zaidi, Syed Mohammad Hassan Mahmood, Zahid Sensors (Basel) Article Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield. MDPI 2021-12-27 /pmc/articles/PMC8747460/ /pubmed/35009689 http://dx.doi.org/10.3390/s22010146 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shafi, Uferah
Mumtaz, Rafia
Haq, Ihsan Ul
Hafeez, Maryam
Iqbal, Naveed
Shaukat, Arslan
Zaidi, Syed Mohammad Hassan
Mahmood, Zahid
Wheat Yellow Rust Disease Infection Type Classification Using Texture Features
title Wheat Yellow Rust Disease Infection Type Classification Using Texture Features
title_full Wheat Yellow Rust Disease Infection Type Classification Using Texture Features
title_fullStr Wheat Yellow Rust Disease Infection Type Classification Using Texture Features
title_full_unstemmed Wheat Yellow Rust Disease Infection Type Classification Using Texture Features
title_short Wheat Yellow Rust Disease Infection Type Classification Using Texture Features
title_sort wheat yellow rust disease infection type classification using texture features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747460/
https://www.ncbi.nlm.nih.gov/pubmed/35009689
http://dx.doi.org/10.3390/s22010146
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