TY - JOUR
T1 - Wheat Yellow Rust Disease Infection Type Classification Using Texture Features
AU - Shafi, Uferah
AU - Mumtaz, Rafia
AU - Haq, Ihsan Ul
AU - Hafeez, Maryam
AU - Iqbal, Naveed
AU - Shaukat, Arslan
AU - Zaidi, Syed Mohammad Hassan
AU - Mahmood, Zahid
N1 - Funding Information:
Funding: This research receives funding from National Centre of Artificial Intelligence (NCAI), NUST, Islamabad, Pakistan.
Funding Information:
Acknowledgments: The research study is supported by the National Center for Artificial Intelligence (NCAI), NUST, Islamabad in collaboration with NARC, Islamabad, Pakistan. Research and development of this study were conducted in IoT Lab, NUST-SEECS, Islamabad, Pakistan.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
KW - Feature extraction
KW - GLCM features
KW - Local binary pattern (LBP)
KW - Machine learning
KW - Texture analysis
KW - Wheat yellow rust disease
UR - http://www.scopus.com/inward/record.url?scp=85121755806&partnerID=8YFLogxK
U2 - 10.3390/s22010146
DO - 10.3390/s22010146
M3 - Article
AN - SCOPUS:85121755806
VL - 22
JO - Sensors
JF - Sensors
SN - 1424-3210
IS - 1
M1 - 146
ER -