AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery
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 ne...
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3830 |
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| author | Ahmad Almadhor Hafiz Tayyab Rauf Muhammad Ikram Ullah Lali Robertas Damaševičius Bader Alouffi Abdullah Alharbi |
| author_facet | Ahmad Almadhor Hafiz Tayyab Rauf Muhammad Ikram Ullah Lali Robertas Damaševičius Bader Alouffi Abdullah Alharbi |
| author_sort | Ahmad Almadhor |
| collection | DOAJ |
| description | 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 <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow></mrow><mspace width="-0.166667em"></mspace><mo>Δ</mo></mrow></semantics></math></inline-formula>E 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. |
| format | Article |
| id | doaj-art-6fb9fbb7d3c246e7b9b282b069948128 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-6fb9fbb7d3c246e7b9b282b0699481282025-01-17T02:44:03ZengMDPI AGSensors1424-82202021-06-012111383010.3390/s21113830AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution ImageryAhmad Almadhor0Hafiz Tayyab Rauf1Muhammad Ikram Ullah Lali2Robertas Damaševičius3Bader Alouffi4Abdullah Alharbi5Department of Computer Engineering, Networks Jouf University, Sakaka 72388, Saudi ArabiaIndependent Researcher, Bradford BD8 0HS, UKDepartment of Information Sciences, University of Education Lahore, Lahore 41000, PakistanFaculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O.Box 11099, Taif 21944, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaPlant 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 <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow></mrow><mspace width="-0.166667em"></mspace><mo>Δ</mo></mrow></semantics></math></inline-formula>E 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.https://www.mdpi.com/1424-8220/21/11/3830guava fruit diseasesfeature extractionmachine learningagricultural informatics |
| spellingShingle | Ahmad Almadhor Hafiz Tayyab Rauf Muhammad Ikram Ullah Lali Robertas Damaševičius Bader Alouffi Abdullah Alharbi AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery Sensors guava fruit diseases feature extraction machine learning agricultural informatics |
| title | AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery |
| title_full | AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery |
| title_fullStr | AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery |
| title_full_unstemmed | AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery |
| title_short | AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery |
| title_sort | ai driven framework for recognition of guava plant diseases through machine learning from dslr camera sensor based high resolution imagery |
| topic | guava fruit diseases feature extraction machine learning agricultural informatics |
| url | https://www.mdpi.com/1424-8220/21/11/3830 |
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