Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing
Corn is one of the major crops in Sudan. Disease outbreaks can significantly reduce maize production, causing huge damage. Conventionally, disease diagnosis is made through visual inspection of the damage in fields or through laboratory tests conducted by experts on the affected plant parts of the c...
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| Format: | Article |
| Language: | English |
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Ankara University
2024-07-01
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| Series: | Journal of Agricultural Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3105923 |
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| author | Geofrey Prudence Baitu Y. Benal Öztekin Omsalma Alsadig Adam Gadalla Khaled Adil Dawood Idress |
| author_facet | Geofrey Prudence Baitu Y. Benal Öztekin Omsalma Alsadig Adam Gadalla Khaled Adil Dawood Idress |
| author_sort | Geofrey Prudence Baitu |
| collection | DOAJ |
| description | Corn is one of the major crops in Sudan. Disease outbreaks can significantly reduce maize production, causing huge damage. Conventionally, disease diagnosis is made through visual inspection of the damage in fields or through laboratory tests conducted by experts on the affected plant parts of the crop. This process typically requires highly skilled personnel, and it can be time-consuming to complete the necessary tasks. Machine learning methods can be implemented to rapidly and accurately detect disease and reduce the risk of crop failure due to disease outbreaks. This study aimed to use traditional machine learning techniques to detect maize diseases using image processing techniques. A total of 600 images were obtained from the open-source Plant Village dataset for experimentation. In this study, image segmentation was done using K-means clustering, and a total of 4 GLCM texture features and two statistical features were extracted from the images. In this study, four traditional machine learning algorithms were applied to detect diseased maize leaves (common rust and gray leaf spot) and healthy maize leaves. The results showed that all the algorithms performed well in identifying the diseased and healthy leaves, with accuracy rates ranging from 90% to 92.7%. The highest accuracy scores were obtained with support vector machine and artificial neural networks, respectively. |
| format | Article |
| id | doaj-art-626482f0c4644e2caec273004dc7ebd3 |
| institution | DOAJ |
| issn | 1300-7580 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Ankara University |
| record_format | Article |
| series | Journal of Agricultural Sciences |
| spelling | doaj-art-626482f0c4644e2caec273004dc7ebd32025-08-20T03:19:29ZengAnkara UniversityJournal of Agricultural Sciences1300-75802024-07-0130346447610.15832/ankutbd.128829845Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image ProcessingGeofrey Prudence Baitu0https://orcid.org/0000-0002-3243-3252Y. Benal Öztekin1https://orcid.org/0000-0003-2387-2322Omsalma Alsadig Adam Gadalla2https://orcid.org/0000-0001-6132-4672Khaled Adil Dawood Idress3https://orcid.org/0000-0002-1631-6232ONDOKUZ MAYIS ÜNİVERSİTESİONDOKUZ MAYIS UNIVERSITYONDOKUZ MAYIS UNIVERSITYONDOKUZ MAYIS UNIVERSITYCorn is one of the major crops in Sudan. Disease outbreaks can significantly reduce maize production, causing huge damage. Conventionally, disease diagnosis is made through visual inspection of the damage in fields or through laboratory tests conducted by experts on the affected plant parts of the crop. This process typically requires highly skilled personnel, and it can be time-consuming to complete the necessary tasks. Machine learning methods can be implemented to rapidly and accurately detect disease and reduce the risk of crop failure due to disease outbreaks. This study aimed to use traditional machine learning techniques to detect maize diseases using image processing techniques. A total of 600 images were obtained from the open-source Plant Village dataset for experimentation. In this study, image segmentation was done using K-means clustering, and a total of 4 GLCM texture features and two statistical features were extracted from the images. In this study, four traditional machine learning algorithms were applied to detect diseased maize leaves (common rust and gray leaf spot) and healthy maize leaves. The results showed that all the algorithms performed well in identifying the diseased and healthy leaves, with accuracy rates ranging from 90% to 92.7%. The highest accuracy scores were obtained with support vector machine and artificial neural networks, respectively.https://dergipark.org.tr/en/download/article-file/3105923maize diseasetraditional machine learningimage processingfeature extraction |
| spellingShingle | Geofrey Prudence Baitu Y. Benal Öztekin Omsalma Alsadig Adam Gadalla Khaled Adil Dawood Idress Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing Journal of Agricultural Sciences maize disease traditional machine learning image processing feature extraction |
| title | Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing |
| title_full | Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing |
| title_fullStr | Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing |
| title_full_unstemmed | Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing |
| title_short | Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing |
| title_sort | machine learning based for automatic detection of corn plant diseases using image processing |
| topic | maize disease traditional machine learning image processing feature extraction |
| url | https://dergipark.org.tr/en/download/article-file/3105923 |
| work_keys_str_mv | AT geofreyprudencebaitu machinelearningbasedforautomaticdetectionofcornplantdiseasesusingimageprocessing AT ybenaloztekin machinelearningbasedforautomaticdetectionofcornplantdiseasesusingimageprocessing AT omsalmaalsadigadamgadalla machinelearningbasedforautomaticdetectionofcornplantdiseasesusingimageprocessing AT khaledadildawoodidress machinelearningbasedforautomaticdetectionofcornplantdiseasesusingimageprocessing |