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Article

AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

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Department of Computer Engineering, Networks Jouf University, Sakaka 72388, Saudi Arabia
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Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
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Department of Information Sciences, University of Education Lahore, Lahore 41000, Pakistan
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Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Department of Computer Science, College of Computers and Information Technology, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia
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Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editor: Chen Chen
Sensors 2021, 21(11), 3830; https://doi.org/10.3390/s21113830
Received: 25 April 2021 / Revised: 23 May 2021 / Accepted: 24 May 2021 / Published: 1 June 2021
Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions. View Full-Text
Keywords: guava fruit diseases; feature extraction; machine learning; agricultural informatics guava fruit diseases; feature extraction; machine learning; agricultural informatics
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MDPI and ACS Style

Almadhor, A.; Rauf, H.T.; Lali, M.I.U.; Damaševičius, R.; Alouffi, B.; Alharbi, A. AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery. Sensors 2021, 21, 3830. https://doi.org/10.3390/s21113830

AMA Style

Almadhor A, Rauf HT, Lali MIU, Damaševičius R, Alouffi B, Alharbi A. AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery. Sensors. 2021; 21(11):3830. https://doi.org/10.3390/s21113830

Chicago/Turabian Style

Almadhor, Ahmad, Hafiz T. Rauf, Muhammad I.U. Lali, Robertas Damaševičius, Bader Alouffi, and Abdullah Alharbi. 2021. "AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery" Sensors 21, no. 11: 3830. https://doi.org/10.3390/s21113830

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