An efficient smart phone application for wheat crop diseases detection using advanced machine learning.
Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges from various diseases. Timely and accurate identifica...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0312768 |
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| author | Awais Amir Niaz Rehan Ashraf Toqeer Mahmood C M Nadeem Faisal Muhammad Mobeen Abid |
| author_facet | Awais Amir Niaz Rehan Ashraf Toqeer Mahmood C M Nadeem Faisal Muhammad Mobeen Abid |
| author_sort | Awais Amir Niaz |
| collection | DOAJ |
| description | Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges from various diseases. Timely and accurate identification of these wheat plant diseases is crucial to mitigate damage and enhance overall yield. Pakistan stands among the leading crop producers due to favorable weather and rich soil for production. However, traditional agricultural practices persist, and there is insufficient emphasis on leveraging technology. A significant challenge faced by the agriculture sector, particularly in countries like Pakistan, is the untimely and inefficient diagnosis of crop diseases. Existing methods for disease identification often result in inaccuracies and inefficiencies, leading to reduced productivity. This study proposes an efficient application for wheat crop disease diagnosis, adaptable for both mobile devices and computer systems as the primary decision-making engine. The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF). These advanced methods collectively achieve up to 99% accuracy in diagnosing 14 key wheat diseases, representing a significant improvement over traditional approaches. The application provides a practical decision-making tool for farmers and agricultural experts in Pakistan, offering precise disease diagnostics and management recommendations. By integrating these cutting-edge techniques, the system advances agricultural technology, enhancing disease detection and supporting increased wheat production, thus contributing valuable innovations to both the field of machine learning and agricultural practices. |
| format | Article |
| id | doaj-art-619182e0c5d449c8a009a0a0432aab30 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-619182e0c5d449c8a009a0a0432aab302025-01-17T05:31:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031276810.1371/journal.pone.0312768An efficient smart phone application for wheat crop diseases detection using advanced machine learning.Awais Amir NiazRehan AshrafToqeer MahmoodC M Nadeem FaisalMuhammad Mobeen AbidGlobally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges from various diseases. Timely and accurate identification of these wheat plant diseases is crucial to mitigate damage and enhance overall yield. Pakistan stands among the leading crop producers due to favorable weather and rich soil for production. However, traditional agricultural practices persist, and there is insufficient emphasis on leveraging technology. A significant challenge faced by the agriculture sector, particularly in countries like Pakistan, is the untimely and inefficient diagnosis of crop diseases. Existing methods for disease identification often result in inaccuracies and inefficiencies, leading to reduced productivity. This study proposes an efficient application for wheat crop disease diagnosis, adaptable for both mobile devices and computer systems as the primary decision-making engine. The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF). These advanced methods collectively achieve up to 99% accuracy in diagnosing 14 key wheat diseases, representing a significant improvement over traditional approaches. The application provides a practical decision-making tool for farmers and agricultural experts in Pakistan, offering precise disease diagnostics and management recommendations. By integrating these cutting-edge techniques, the system advances agricultural technology, enhancing disease detection and supporting increased wheat production, thus contributing valuable innovations to both the field of machine learning and agricultural practices.https://doi.org/10.1371/journal.pone.0312768 |
| spellingShingle | Awais Amir Niaz Rehan Ashraf Toqeer Mahmood C M Nadeem Faisal Muhammad Mobeen Abid An efficient smart phone application for wheat crop diseases detection using advanced machine learning. PLoS ONE |
| title | An efficient smart phone application for wheat crop diseases detection using advanced machine learning. |
| title_full | An efficient smart phone application for wheat crop diseases detection using advanced machine learning. |
| title_fullStr | An efficient smart phone application for wheat crop diseases detection using advanced machine learning. |
| title_full_unstemmed | An efficient smart phone application for wheat crop diseases detection using advanced machine learning. |
| title_short | An efficient smart phone application for wheat crop diseases detection using advanced machine learning. |
| title_sort | efficient smart phone application for wheat crop diseases detection using advanced machine learning |
| url | https://doi.org/10.1371/journal.pone.0312768 |
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